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The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.
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Hindawi Publishing Corporation
International Journal of Rotating Machinery
Volume 2008, Article ID 583982, 10 pages
Review Article
Machine Fault Signature Analysis
Pratesh Jayaswal,1A. K. Wadhwani,2and K. B. Mulchandani3
1Department of Mechanical Engineering, Madhav Institute of Technology and Science, Gwalior 474005, India
2Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior 474005, India
3Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Correspondence should be addressed to Pratesh Jayaswal, pratesh
Received 29 October 2007; Accepted 21 January 2008
Recommended by Yasutomo Kaneko
The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard
to vibration analysis. The dierent types of faults that can be identified from the vibration signature analysis are, for example, gear
fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention
of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the
rolling element bearing fault signature analysis.
Copyright © 2008 Pratesh Jayaswal et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Machine fault problems are broad sources of high mainte-
nance cost and unwanted downtime across the industries.
The prime objective of maintenance department is to keep
machinery and plant equipments in good operating condi-
tion that prevents failure and production loss. If the depart-
ment organizes a predictive maintenance program, this goal
as well as cost benefits can be achieved, while accurate in-
formation at the right time is a crucial aspect of a mainte-
nance regimen [1]. The condition-based maintenance strat-
egy is being employed for uninterrupted production process
in industries. Condition-based maintenance (CBM) consists
of continuously evaluating the condition of a monitored ma-
chine and thereby successfully identifying faults before catas-
trophic breakdown occurs. Numerous condition monitoring
(cm) and diagnostics methodologies are utilizing to iden-
tify the machine faults to take corrective action. Machine
fault identification can be done with dierent methodologies
as vibration signature analysis, lubricant signature analysis,
noise signature analysis, and temperature monitoring, with
the use of appropriate sensors, dierent signal conditioning,
and analyzing instruments.
Vibration signature analysis techniques for machine fault
identification are the most popular among other techniques.
Vibration monitoring is based on the principle that all the
system produces vibration. When a machine is operating
properly, the vibration is small and constant, however, when
faults develop and some of the dynamic process in the ma-
chine changes, there will be changes in vibration spectrum
observed. After the review of previous published work, it is
concluded that gear fault, bearing fault, and coupling fault
are studied for research purpose to fault signature analysis.
The majority of industrial machines use ball or rolling ele-
ments bearings (REB). The vibration signals obtained from
the vicinity of a bearing assembly contain rich information
about the bearing condition. Most of the researchers have
used vibration signature analysis techniques for rolling ele-
ment bearing fault identification in case of single defect on
bearing components. Time-domain and frequency-domain
vibration analysis techniques were tested but eective iden-
tification of bearing condition is, however, not so straight-
forward. Several researchers have used artificial intelligence
techniques as well as time-frequency domain analysis and de-
veloped expert diagnostics system for bearing fault identifi-
cation with the use of artificial neural network, fuzzy logic,
wavelet transform, and hybrid techniques. In this paper, a
review of need and dierent techniques of machine fault sig-
nature analysis are discussed. A special emphasis is given to
rolling element bearing vibration signature analysis, while
other techniques are also covered. This paper is divided into
dierent sections, each dealing with various aspects of the
2 International Journal of Rotating Machinery
subject. It begins with a summary of need of machine fault
diagnosis followed by a general overview of the numerous
means of signature analysis.
Machine fault can be defined as any change in a machin-
ery part or component which makes it unable to perform its
function satisfactorily or it can be defined as the termination
of availability of an item to perform its intended function.
The familiar stages before the final fault are incipient fault,
distress, deterioration, and damage, all of them eventually
make the part or component unreliable or unsafe for con-
tinued use [2]. Classification of failure causes are as follows:
(i) inherent weakness in material, design, and manufac-
(ii) misuse or applying stress in undesired direction;
(iii) gradual deterioration due to wear, tear, stress fatigue,
corrosion, and so forth.
Antifriction bearings failure is a major factor in failure of
rotating machinery. Antifriction bearing defects may be cat-
egorized as localized and distributed. The localized defects
include cracks, pits, and spalls caused by fatigue on rolling
surfaces. The distributed defect includes surface roughness,
waviness, misaligned races, and o-size rolling elements.
These defects may result from manufacturing and abrasive
wear [3].
Modern manufacturing plants are highly complex. Fail-
ure of process equipments and instrumentation increased
the operating costs and resulted in loss of production. Un-
detected or uncorrected malfunctions can induce failures
in related equipments and, in extreme cases, can lead to
catastrophic accidents. Early fault detection in machines
can save millions of dollars on emergency maintenance and
production-loss cost. Gearbox and bearings are essential
parts of many machineries [4]. The early detection of the de-
fects, therefore, is crucial for the prevention of damage and
secondary damage to other parts of a machine or even a total
failure of the associated large system can be triggered [5].
There are certain objectives of machine fault identifica-
(i) prevention of future failure events;
(ii) assurance of safety, reliability, and maintainability of
Machineries failures reveal a reaction chain of cause and
defect. The end of the chain is usually a performance de-
ficiency commonly referred to as the symptom, trouble, or
simply the problem. The machine fault signature analysis
works backwards to define the elements of the reaction chain
and then proceeds to link the most probable failure cause
based on failure analysis with a root cause of an existing or
potential problem. Accurate and complete knowledge of the
causes responsible for the breakdown of a machine is neces-
sary to the engineer, similarly, as knowledge of a breakdown
in health is to the physician. The physician cannot assure a
lasting cure unless he knows what lies at the root of the trou-
ble, and the future usefulness of a machine often depends
Tab le 1: Timings of action for maintenance.
Timings of action Maintenance
Operating to failure Shutdown or breakdown
Fixed time based Preventive
Condition based Predictive or diagnostic
on correct understanding of the causes of failure. The proper
maintenance can be done only after the knowledge of root
cause of failure.
Edwards et al. [6] present a review on fault diagnosis of
rotating machinery to provide a broad review of the state of
the art in fault diagnosis techniques. The early fault detection
and diagnosis allow preventive maintenance and condition-
based maintenance to be arranged for the machine during
scheduled period of downtime caused by extensive system
failures that improves the overall availability, performance
and reduces maintenance cost. For the fault diagnosis prob-
lem, it is not only to detect fault in system, but also to isolate
the fault and find out its causes.
Maintenance is a combination of science, art, and philoso-
phy. The rationalization of maintenance requires a deep in-
sight into what maintenance really is. Ecient maintenance
is a matter of having the right resources in the right place at
the right time. Maintenance can be defined as the total ac-
tivities carried out in order to restore or renew an item to
working condition, if fault is there. Maintenance is also de-
fined as combination of action carried out to return an item
to or restore it to an acceptable condition. The classification
of maintenance according to timings of action for mainte-
nance is shown in Tabl e 1 .
Every machine component behaves as an individual. Fail-
ure can take place earlier or later than recommended in case
of preventive maintenance. It can be improved by condition-
based maintenance. Dileo et al. [7] present a review on the
classical approaches to maintenance and then compare them
with condition-based maintenance (CBM).
The prevention of potential damage to machinery is nec-
essary for safe, reliable operation of process plants. Failure
prevention can be achieved by sound specification, selection,
review, and design audit routines. When failures do occur,
accurate definition of root cause is an absolute prerequisite
to the prevention of future failure events [2].
Condition-based maintenance is defined as “mainte-
nance work initiated as a result of knowledge of the con-
dition of an item from routine or continuous checking.” It
is carried out in response to a significant deterioration in a
unit as indicated by a change in a monitored parameter of
the unit condition or performance. Condition reports arise
from human observations, checks, and tests, or from fixed
instrumentation or alarm systems grouped under the name
condition monitoring. It is here that one can make use of
predictive maintenance by using a technique called signa-
ture analysis. Signature analysis technique is intended to con-
tinually monitor the health of the equipment by recording
Pratesh Jayaswal et al. 3
systematic signals or information derived from the form of
mechanical vibrations, noise signals, acoustic and thermal
emissions, change in chemical compositions, smell, pressure,
relative displacement, and so on [8]. Mann et al. [9]present
an article explores the benefits of condition-based preventive
maintenance compared to the traditional statistical reliability
approach. Nandi and Toliyat [10] present a review on condi-
tion monitoring and fault diagnosis of electrical machines.
Marcus [11] proposed condition-based maintenance to rail
vehicle for more eective maintenance.
Condition-based maintenance diers from both failure
maintenance and fixed-time replacement. It requires mon-
itoring of some condition-indicating parameter of the unit
being maintained. This contrasts with failure maintenance,
which implies that no successful condition monitoring is un-
dertaken and with fixed-time replacement which is based on
statistical failure data for a type of unit. In general, condition-
based maintenance is more ecient and adaptable than ei-
ther of the other maintenance actions. On indication of de-
terioration, that unit can be scheduled for shutdown at a time
chosen in advance of failure, yet, if the production policy dic-
tates, the unit can be run to failure. Alternatively, the amount
of unnecessary preventive replacement can be reduced, while
if the consequences of failure are suciently dire, the con-
dition monitoring can be employed to indicate possible im-
pending failure well before it becomes a significant probabil-
ity. The trend monitoring method for one or group of similar
machines is possible if sucient data of monitored parame-
ters are available. It relates the condition of machine(s) di-
rectly to the monitored parameters. On the other hand, con-
dition checking method is employed for a wide range of di-
agnostics instruments apart from human senses. Some of the
recent developments in the form of CBM are proactive main-
tenance, reliability centered maintenance (RCM) and total
productive maintenance (TPM).
When a fault takes places, some of the machine parameters
are subjected to change. The change in the machine param-
eters depends upon the degree of faults and the interaction
with other parameters. In most cases, more than one param-
eter are subjected to change under abnormal condition. Con-
dition monitoring can be carried out when the equipment is
in operation, which known as on-line, or when it is o-line,
which means when it is down and not in the operation. While
on-line, the critical parameters that are possible to monitor
are speed, temperature, vibration, and sound. These may be
continuously monitored or may be done periodically. O-
line monitoring is carried out when the machine is down
for whatever reason. The monitoring in such would include
crack detection, a thoroughly check of alignment, state of
balancing, the search for tell-tale sign of corrosion, pitting,
and so on.
The International Standards Organization’s Tech-
nical Committee 108 (ISO/TC108) produces standards
in the area of mechanical vibration, shock, and ma-
chine condition monitoring. ISO/TC108’s Subcommittee 5
(ISO/TC108/SC5) has focused on standards for the condition
monitoring and diagnostic of machines. This subcommittee
has published ISO 13374-1:2003 which establishes general
guidelines for software specifications related to data process-
ing, communication, and presentation of machine condition
monitoring and diagnostic information. This standard
defines the data processing and information flow needed
between processing blocks in condition monitoring systems.
Machine condition monitoring (MCM) is a vital component
of preventive and predictive maintenance programs that
seek to reduce cost and avoid unplanned downtime. MCM
also contributes to health and safety by recognizing faults
which may give rise to pollution or health hazards, and also
by indication of incipient faults which could produce danger
conditions. MCM setups include measurement hardware
and software that acquire and interpret signals generated
by the machine being monitored. Condition monitoring is
taken to mean the use of advanced technologies in order to
determine equipment condition, and to predict potential
failure. It includes, but is not limited to, technologies such
as visual inspection, vibration measurement and analysis,
temperature monitoring, acoustic emission analysis, noise
analysis, oil analysis, wear debris analysis, motor current
signature analysis, and nondestructive testing.
4.1. Visual inspection
visual monitoring can sometimes provide a direct indication
of the machine’s condition without the need for further anal-
ysis. The available techniques can range from using a simple
magnifying glass or low-power microscope. Other forms of
visual monitoring include the use of dye penetrants to pro-
vide a clear definition of any cracks occurring on the ma-
chine surface, and the use of heat-sensitive or thermographic
paints. The condition of many transmission components can
readily be checked visually. For example, the wear on the sur-
faces of gear teeth gives much information. Problems of over-
load, fatigue failure, wear and poor lubrication can be dier-
entiated from the appearance of the teeth.
4.2. Vibration analysis
Modern condition monitoring techniques encompass many
dierent themes; one of the most important and informa-
tive is the vibration analysis of rotating machinery. Using
vibration analysis, the state of a machine can be constantly
monitored and detailed analysis may be made concerning the
health of the machine and any faults which may arise or have
already arisen. Machinery distress very often manifests itself
in vibration or a change in vibration pattern. Vibration anal-
ysis is therefore, a powerful diagnostic and troubleshooting
tool of major process machinery.
On-load monitoring can be performed mainly in the fol-
lowing three ways.
(i) Periodic field measurements with portable instru-
ments; this method provides information about long-
term changes in the condition of plant. The portable
instruments are employed with a high load factor and
can often be placed in the care of only one man. Use
4 International Journal of Rotating Machinery
of life curves and the LEO approach assist the decision
(ii) Continuous monitoring with permanently installed
instruments; it is employed when machine failures are
known to occur rapidly and when the results of such
failure are totally unacceptable as in the case of turbine
generator units.
(iii) Signature analysis: scientific collection of information,
signals or signatures, diagnosis and detection of the
faults by a thorough analysis of these signatures based
on the knowledge hitherto acquired in the field, and
judging the severity of faults for decision making, all
put together, is called signature analysis. The technique
involves the use of electronic instrumentation espe-
cially designed for the purpose of varied capacities,
modes of application and design features.
Vibration signals are the most versatile parameters in ma-
chine condition monitoring techniques. Periodic vibration
checks reveal whether troubles are present or impending. Vi-
bration signature analysis reveals which part of the machine
is defective and why. Although a number of vibration anal-
ysis techniques have been developed for this purpose, still a
lot of scope is there to reach a stage of expertise.
4.3. Temperature monitoring
Temperature monitoring consists of measuring of the opera-
tional temperature and the temperature of component sur-
faces. Monitoring operational temperature can be consid-
ered as a subset of the operational variables for performance
monitoring. The monitoring of component temperature has
been found to relate to wear occurring in machine elements,
particularly in journal bearings, where lubrication is either
inadequate or absent. The techniques for monitoring tem-
perature of machine components can include the use of op-
tical pyrometers, thermocouples, thermography, and resis-
tance thermometers.
4.4. Acoustic emission analysis
Acoustic emission refers to the generation of transient waves
during the rapid release of energy from localized sources
within a material. The source of these emissions is closely as-
sociated with the dislocation accompanying plastic deforma-
tion and the initiation or extension of fatigue cracks in mate-
rial under stress. The other sources of acoustic emission are
melting, phase transformations, thermal stress, cool-down
cracking, and the failure of bonds and fibers in composite
materials. Acoustic emissions are measured by piezoelectric
transducers mounted on the surface of the structure under
test and loading the structure. Sensors are coupled to the
structure by means of a fluid couplant or by adhesive bonds.
The output of each piezoelectric sensor is amplified through
a low-noise preamplifier, filtered to remove any extraneous
noise and furthered processed by suitable electronic equip-
Traditionally, acoustic emissions as a technique has been
restricted to the monitoring of high cost structures due to the
expenses of the monitoring equipment. However, as equip-
ment costs steadily fall, the range of viable applications ex-
pands rapidly. Olsson et al. present a frame work for fault
diagnosis of industrial robots using acoustic signals and case-
based reasoning [12]. This frame work utilizes the case-based
reasoning for fault identification based on sound recording
in robot fault diagnosis. Wue et al. have developed experi-
mental setup for online fault detection and analysis of mod-
ern water hydraulic system [13], and suggested that the in-
corporation of wavelet transformation into the analysis of
acoustic emission opens up the door for future research,
which can prove to be very relevant toward condition mon-
itoring. Choe et al. [14]workedonneuralpatternidenti-
cation of railroad wheel-bearing faults from audible acoustic
signals by comparison of FFT, continuous wavelets transform
(CWT) and discrete wavelets transform (DWT) features.
4.5. Noise analysis
Noise signals are utilized for condition monitoring because
noise signals measured at regions in proximity to the exter-
nal surface of machines can contain vital information about
the internal processes, and can provide valuable information
about a machine’s running condition. When machines are in
a good condition, their noise frequency spectra have charac-
teristic shapes. As faults begin to develop, the frequency spec-
tra change. Each component in the frequency spectrum can
be related to a specific source within the machine. This is the
fundamental basis for using noise measurement and analysis
in condition monitoring. Sometimes the signal which is to be
monitored is submerged within some other signal and it can-
not be detected by a straightforward time history or spectral
analysis. In this case, specialized signal processing techniques
have to be utilized.
4.6. Wear debris analysis
It is not possible to examine the working parts of a com-
plex machine on load, nor is it convenient to strip down the
machine. However, the oil which circulates through the ma-
chine carries with it evidence of the condition of parts en-
countered. Examination of the oil, any particle it has car-
ried with it, allows monitoring of the machine on load or
at shutdown. A number of techniques are applied, some
very simple, other involving painstaking tests and expen-
sive equipments. Presently, available lubricant sampling or
monitoring techniques like rotary particles depositor (RPD),
spectrophotometer oil analysis programme (SOAP), Ferro-
graphic oil analysis and recent software used techniques are
available to distinguish between damage debris and normal
wear debris. Every machine ever designed undergoes a pro-
cess of wear and tear in operation, yet a battery of modern
condition monitoring techniques is available to monitor this
process and trigger preventive maintenance routines which
depend on identifying any problem before it has the chance
to develop to the point of final breakdown. Now recently, en-
gineers have been able to extend their knowledge of condi-
tions within operating machinery by studying the particles
of metallic debris which can be found in lubricating oil from
Pratesh Jayaswal et al. 5
engines, gearboxes, final drive units and transmissions, or in
hydraulic fluid, and recording the number, size, and type of
these fragments of debris.
4.7. Motor current signature analysis
Motor current signature analysis (MCSA) is a novel diagnos-
tic process for condition monitoring of electric motor-driven
mechanical equipment (pumps, motor-operated valves,
compressors, and processing machinery). The MCSA pro-
cess identifies, characterizes, and trends overtime the instan-
taneous load variations of mechanical equipment in order to
diagnose changes in the condition of the equipment. It mon-
itors the instantaneous variations (noise content) in the elec-
tric current flowing through the power leads to the electric
motor that drives the equipment. The motor itself thereby
acts as a transducer, sensing large and small, long-term and
rapid, mechanical load variations, and converting them to
variations in the induced current generated in the motor
windings. This motor current noise signature is detected,
amplified, and further processed as needed to examine its
time-domain and frequency-domain (spectral) characteris-
tics. Korde [15] demonstrates that the spectrum analysis of
the motors current and voltage signals can hence detect var-
ious faults without disturbing its operation using FFT trans-
4.8. Nondestructive testing
The principle of nondestructive testing (NDT) is to be able to
use the components or structure after examination. The in-
spection should not aect the item involved, and must there-
fore, be nondestructive. NDT includes many dierent tech-
nologies, each suitable for one or more specific inspection
tasks, with many dierent disciplines overlapping or compli-
menting others. Thus the best technique(s), for any one ap-
plication, should be decided by an expert eddy current test-
ing, electrical resistance testing, flux leakage testing, mag-
netic testing, penetrant testing, radiographic testing, reso-
nant testing, thermographic testing, ultrasonic testing, and
visual testing are some of the dierent NDT techniques.
The word signature has been coined to designate signal pat-
terns which characterize the state or condition of a system
from which they are acquired. Signatures are extensively used
as a diagnostic tool for mechanical system. In many cases,
some kind of signal processing is undertaken on those sig-
nals in order to enhance or extract specific features of such
vibration signatures. It is very important to consider the type
and range of transducers used as pickup for capturing vibra-
tion signal. Signature-based diagnostic makes extensive use
of signal processing techniques involving one or more meth-
ods to deal with the problem of improvement in the signal to
noise ratio.
Vibration-based monitoring techniques have been widely
used for detection and diagnosis of bearing defects for several
decades. These methods have traditionally been applied, sep-
arately in time and frequency domains. A time-domain anal-
ysis focuses principally on statistical characteristics of vibra-
tion signal such as peak level, standard deviation, skewness,
kurtosis, and crest factor. A frequency domain approach uses
Fourier methods to transform the time-domain signal to the
frequency domain, where further analysis is carried out, con-
ventionally using vibration amplitude and power spectra. It
should be noted that use of either domain implicitly excludes
the direct use of information present in the other. These tech-
niques have been broadly classified in three areas, namely, the
5.1. Time-domain analysis
The time domain refers to a display or analysis of the vi-
bration data as a function of time. The principal advantage
of this format is that little or no data are lost prior to in-
spection. This allows for a great deal of detailed analysis.
However, the disadvantage is that there is often too much
data for easy and clear fault diagnosis. Time-domain anal-
ysis of vibration signals can be subdivided into the following
categories: time-waveform analysis, time-waveform indices,
time-synchronous averaging, negative averaging, orbits, and
probability density moments.
5.2. Frequency domain
The frequency domain refers to a display or analysis of the
vibration data as a function of frequency. The time-domain
vibration signal is typically processed into the frequency do-
main by applying a Fourier transform, usually in the form
of a fast Fourier transform (FFT) algorithm. The principal
advantage of this format is that the repetitive nature of the
vibration signal is clearly displayed as peaks in the frequency
spectrum at the frequencies where the repetition takes place.
This allows for faults, which usually generate specific charac-
teristic frequency responses, to be detected early, diagnosed
accurately, and trended overtime as the condition deterio-
rates. However, the disadvantage of frequency-domain anal-
ysis is that a significant amount of information (transients,
nonrepetitive signal components) may be lost during the
transformation process. This information is nonretrievable
unless a permanent record of the raw vibration signal has
been made. The various methods of frequency-domain vi-
bration signature analysis are bandpass analysis, shock pulse
(spike energy), enveloped spectrum, signature spectrum, and
cascades (waterfall plots).
5.3. The quefrency domain
The quefrency is the abscissa for the cepstrum which is de-
fined as the spectrum of the logarithm of the power spec-
trum. It is used to highlight periodicities that occur in the
spectrum in the same manner as the spectrum is used to
highlight periodic components occurring in the time domain
[16]. One of the ways the expert system detects bearing tones
is by looking at the spectrum of a spectrum. This process
is called cepstrum analysis, “cepstrum” being a play on the
word “spectrum.”
6 International Journal of Rotating Machinery
Renwick and Babson [1] demonstrate that the predictive
maintenance using vibration analysis has achieved meaning-
ful results in successfully diagnosis machinery problems. The
benefits of such programs include not only evident-cost ben-
efits such as reducing machinery downtime and production
losses, but also the more subtle long-term cost benefits which
can result from accurate maintenance scheduling.
source identification and fault detection from vibration
signals associated with items which involve rotational mo-
tion such as gears, rotors and shafts, rolling element bearings,
journal bearings, flexible couplings, and electrical machines
depend upon several factors: (i) the rotational speed of the
items, (ii) the background noise and/or vibration level, (iii)
the location of the monitoring transducer, (iv) the load shar-
ing characteristics of the item, and (v) the dynamic interac-
tion between the item and other items in contact with it.
The main causes of mechanical vibration are unbalance,
misalignment, looseness and distortion, defective bearings,
gearing and coupling in accuracies, critical speeds, various
form of resonance, bad drive belts, reciprocating forces, aero-
dynamic or hydrodynamic forces, oil whirl, friction whirl,
rotor/stator misalignments, bent rotor shafts, defective rotor
bars, and so on. Some of the most common faults that can be
detected using vibration analysis are summarized in Tab l e 2
Weqerich et al. developed a nonparametric modeling
technique by smart signal and demonstrate the use of this
approach for detecting faults in rotating machinery via ex-
tracted features from vibration signals [18]. Lei et al. [19]
present damage diagnosis approach using time series analysis
of vibration signals for structural health monitoring bench-
mark problem.
Sohn and Farrar [20] have presented a procedure for
damage detection and localization within a mechanical sys-
tem solely based on the time series analysis of vibration data.
Sahinkaya et al. [21]haveworkedonfaultdetectionandtol-
erance in synchronous vibration control of rotor magnetic
bearing system. A simple and eective algorithm has been
developed to build fault detection and tolerances capabili-
ties into the open-loop adaptive control of the synchronous
vibration of flexible rotors supported or equipped with mag-
netic bearings.
Lebold et al. [22] have presented review of vibration-
analysis methods for gearbox diagnostics and prognostics.
This review listed some of the most traditional features used
for machinery diagnostics and presented some of the signal
processing parameters that impact their sensitivity.
Ver m a an d B al an [ 23] present a fundamental study on
the vibration behavior of electrical machine stators using
an experimental model analysis and suggested that vibra-
tion level even at resonance can be reduced by designing the
electromagnetic forces to have circumferential mode associ-
ated with corresponding resonance. Ocak and Loparo [24]
present algorithms for estimating the running speed and the
bearing-key frequencies of an induction motor using vibra-
tion data that can be used for failure detection and diagnosis.
Tab le 2: Some typical faults and defects that can be detected with
vibration analysis.
Item Fault
Tooth messing faults,
cracked and/or worm teeth,
eccentric gear
Rotors and shaft
Bent shaft
Eccentric journals
Loose components
Critical speed
Cracked shaft
Blade loss
Blade resonance
Rolling element bearings
Pitting of race and ball/roller
Other rolling elements defect
Journal bearing
Oil whirl
Oval or barreled journal
Journal/bearing rub
Flexible coupling Misalignment
Electrical machines
Unbalanced magnetic pulls
Broken/damaged rotor bars
Air gap geometry variations
Structural and foundation faults
Structural resonance
Piping resonance
Vortex shedding
Plenge et al. [25] developed optical inspection techniques for
vibration analysis and defect indication in railway.
Vibration signals from gearboxes and roller bearings
share many common characteristics. First, the signals are
usually noisy. This is because the accelerometers for sig-
nals collection are mounted on the outer surface of gearbox.
The signals obtained from these accelerometers include vi-
brations from meshing gears, bearings, and the equipment’s
many other parts. Second, symptoms from faulty bearings
are very similar to those from faulty gears. For example, pe-
riodic impulses may indicate either cracked teeth of gears or
damaged races or rollers of roller bearings. Such periodic im-
pulses, however, cannot be detected easily with the frequency
spectrum because of the heavy noise distributed in the low-
frequency area [4].
Lin et al. [4] have obtained excellent results for mechani-
cal fault detection based on the wavelet denoising technique.
The method has performed excellently when used to denoise
mechanical vibration signals with a low signal-to-noise ratio.
Pratesh Jayaswal et al. 7
Each of the rolling element bearings used in industries con-
sists of two rings, one inner and the other outer. A set of
balls or rolling elements placed in raceways rotate inside
these rings. Even when properly applied and maintained, the
bearing will still be subjected to one cause of failure, fatigue
of bearing material. Fatigue is the result of shear stresses
cyclically applied immediately below the load-carrying sur-
faces and is observed as spalling away of surface metal. How-
ever, material fatigue is not the only cause of spalling. There
are causes of premature spalling. So, although the observer
can identify spalling, he must be able to discern between
spalling produced at the normal end of bearing’s useful life
and that triggered by causes found in the three major clas-
sifications of premature spalling as lubrication, mechanical
damage, and material defects. Most bearing failures can be
attributed to one or more of the following causes as de-
fective bearing seats on shafts and in housings, misalign-
ment, faulty mounting practice, incorrect shaft and hous-
ing fit, inadequate lubrication, ineective sealing, and vi-
bration, while the bearing is not rotating, passage of elec-
tric current through the bearing [2]. As one of the most
essential parts in rotating machinery rolling element bear-
ings are often subjected to high stress and operate under se-
vere conditions, Their integrity becomes an issue particu-
larly in key machinery. A machine could be seriously jeopar-
dized, if defects occur to those rolling element bearing during
service [5]. A new approach for the categorization of bear-
ing faults was introduced by Stack et al. [26]. A common
way in which bearing faults are often classified according to
the location of the fault (an inner race/outer race/ball/cage
fault). In this research, bearing faults were grouped into
one of two categories, as single point defects and general-
ized roughness. The single point defects are defined as vis-
ible defects that appear on the raceways, rolling elements, or
A single point defect produces one of the four charac-
teristics fault frequencies depending on which surface of the
bearing contains the fault. In spite of the name, a bearing
can possess multiple single point defects. The other group of
bearing faults, generalized roughness, refers to an unhealthy
bearing whose damage is not apparent to the unaided eye.
Example of this failure mode includes deformation or warp-
ing of the rolling elements or raceways and overall surface
roughness due to heating, contaminated lubricant, or elec-
tric discharge machining. The eects produced by this failure
mode are dicult to predict, and there are no characteristics
fault frequencies with this type of fault.
In rolling element bearing failure analysis, the low-
frequency phenomenon is the impact caused by a defect of
a bearing. The high-frequency carrier is a combination of
the natural frequencies of the associated rolling element or
even of the machine [27]. There are a number of factors that
contribute to the complexity of the bearing signature. First,
variation of bearing geometry and assembly make it impos-
sible to precisely determine bearing characteristics frequen-
cies. Secondly, locations of bearing defects cause dierent be-
havior in the transient response of the signal, which is easily
buried in wide band response and noisy signals. Thirdly, sig-
nature appears to be very dierent with the same type of de-
fect at dierent stages of damage, severity. Finally, operating
speed and loads of the shaft greatly aect the way and the
amount a machine vibrates.
Several researchers worked on the subject of rolling el-
ement bearing defect detection and diagnosis through vi-
bration analysis. Time domain, frequency domain, time-
frequency domain based on short time Fourier transform
(STFT) and wavelet transform and advanced signal process-
ing techniques have been implemented and tested. Time-
domain analysis focuses on dealing directly with the time-
domain waveform of vibration signals. The indices RMS,
peak value, and crest factor are often used to quantify the
time signal. The statistical parameters such as kurtosis and
skewness values are robust to varying bearing operating con-
dition and are good indicators of incipient defects. The dis-
advantage, however, is that as the defect spreads across the
bearing surfaces the values of these parameters drop back to
normal [28].
The frequency domain, spectrum of the vibration signal
reveals frequency characteristics of vibration. If the frequen-
cies of the impulse occurrence are close to one of the bear-
ing characteristic frequencies, such as ball pass inner race
frequency, ball pass outer race frequency, ball spin frequen-
cies, it may indicate a defect related fault in the bearing. Fast
Fourier transform is used in conventional frequency-domain
signature analysis techniques for conversion of time-domain
signal in frequency-domain signal. Other frequency-domain
techniques are generally used are the calculation of power
spectral density, bandpass analysis, envelope analysis. The
eectiveness of bandpass-analysis method relies on a suit-
able choice of narrow-band frequencies around the selected
resonance. In envelope analysis, signals are filtered through
bandpass filter and filtered signal is demodulated with the
help of full-wave rectification or via Hilbert transform and
then spectrum analyzed. The passband and envelope anal-
ysis techniques are useful to detect rolling element bearing
faults when signals are noisy due to severity of fault or due to
associated noise from other sources as shaft misalignment,
unbalance, and looseness.
The fast Fourier transform has drawback, when signal is
nonstationary or noisy, even in FFT, time information is lost.
Many researchers have used short-time Fourier transform
(STFT) to overcome the time information problem but low-
resolution problem exists in STFT. The wavelets transform is
currently used to overcome both the time information and
low resolution problems. A major advantage of the wavelets
transform is that this method can exhibit the local features
of the signals and give account of how energy is distributed
over frequencies changes from one instant to the next.
The confidence of bearing fault diagnosis can be im-
proved by using a range of failure indicators including per-
formance indices, oil analysis, thermography, and motor cur-
rent readings in conjunction with vibration analysis. These
indicators are generally assimilated and analyzed by human
expert but a computational expert system, based on neural
network, fuzzy logic, and rule based logic, as well as hy-
brid techniques containing elements of all three methods,
8 International Journal of Rotating Machinery
is being used and continually improved in order to automate
the process.
The neural network technology provides an attractive
complement to traditional vibration analysis because of the
potential of neural networks to operate in real-time mode
and to handle data that may be distorted or noisy [29]. Neu-
ral networks have proven the ability in the area of nonlinear
pattern classification and can correctly identify the dierent
causes of bearing vibration [30]. The fuzzy logic has proven
ability in mimicking human decisions, and the bearing fault
diagnosis problem has typically been solved by an experi-
enced engineer. The fuzzy logic is promising for automation
in the area of bearing vibration diagnosis, if the input data
is well processed [31]. The advantages of the fuzzy logic ap-
proach include the possibility to change the linguistic rules
into decisions by copying the procedure and thinking of a
human analyzer. The rules that include uncertainty and in-
accuracy are changed into numbers describing the severity or
the probability of fault. The rules and membership functions
can be tuned to find the good sensitivity of the diagnostic
With the development of soft computing techniques such as
artificial neural network (ANN) and fuzzy logic, there is a
growing interest in applying these approaches to the dier-
ent areas of engineering. These systems gained popularity
over other methods, as they are model free estimators capable
of synthesizing nonlinear and noisy systems. The fuzzy logic
was developed as a mean for representing, manipulating, and
utilizing uncertain information (information that is usually
expressed in linguistic terms). The recent surge of interest is
in merging or combining NN and fuzzy logic system into a
functional system to overcome their individual weaknesses.
Wavelet analysis is an emerging field of mathematics that
has provided new tool and algorithms for the type of prob-
lems encountered in process monitoring. Wavelet transform
(WT) is a mathematical approach that decomposes a time-
domain signal into dierent frequency groups. Wavelet algo-
rithms process data at dierent scales and resolutions
The monitoring and diagnosis of machinery is a well-
established discipline, but much progress remains to be made
in automating diagnosis as well as developing low-cost re-
liable technologies which can be applied cost-eectively in
the majority of production environment. Developments in
microtechnology and artificial intelligence have driven the
trends toward more extensive onboard diagnostics. Recent
systems have relied on artificial intelligence techniques to
strengthen the robustness of diagnostics systems. Four arti-
ficial techniques have been widely applied as expert system,
neural networks, fuzzy logic, and model-based systems [9].
Dierent kinds of artificial intelligence method have become
common in fault diagnosis and condition monitoring. For
example, fuzzy logic and neural networks have been used in
modeling and decision making in diagnostics schemes. Neu-
ral networks-based classifications are used in diagnosis of
rolling element bearings.
Shikari and Sadiwala worked on automation in
condition-based maintenance using vibration analysis
[32]. In this work, importance of intelligent system in CBM
is focused. Dyke [33] describes an example of the applica-
tion of the DLI engineering ExpertAlert expert automated
diagnostics system to successful diagnosis of machine tool
spindle bearing problems. Sima [34] proposes a strictly
neural expert system architecture that enables the creation of
the knowledge base automatically by learning from example
inferences. Bandyopadhya et al. [35] have developed an
expert system for real-time condition monitoring using
vibration analysis for turbine bearing. Poyhonen et al. [36]
have applied support vector classification to fault diagnostics
of an electrical machine.
Zhenya et al. proposed a multilayer feed forward
network-based machine state identification method. They
represent certain fuzzy relationship between the fault symp-
toms and causes, with highly nonlinearity between the input
and the output of the network [37]. The rolling element bear-
ing signals are investigated accordingly to the principle that
the wavelet can extract the signal envelope by Jun and Liao. A
wavelet-based self-information extracting envelope method
was applied, application of the method demonstrates that the
method is eective to extract the rolling bearing signal enve-
lope and is useful to analysis the bearing faults [27].
Four approaches based on bispectral and wavelet analy-
sis of vibration signals are investigated as signal processing
techniques for application of a number of induction motor
rolling element bearing faults by Yang et al. [38]. A general
methodology for machinery fault diagnosis through a pat-
tern recognition technique is developed by Sun et al. [28],
this involves data acquisition, feature extraction, mapping for
feature fusion, and piecewise-linear classification and diag-
nosis. They conclude, to increase the sensitivity and reliabil-
ity of pattern recognition, one is encouraged to include as
many feature parameters as possible without concern the re-
dundancy or numerical singularities.
Satish and Sharma [39] demonstrate a novel and cost-
eective approach for diagnosis and prognosis of bearing
faults in small and medium-size induction motor. In this
work, a fuzzy back-propagation network was developed by
combining neural network with fuzzy logic to identify the
present condition of the bearing and to estimate the remain-
ing life of the motor.
Fan and Zuo [40]proposedaneective method to extract
modulating signal and to detect the early gear fault. In this
new fault detection method, combination of Hilbert trans-
form and wavelet-packet transform were used. Both simu-
lated signals and real vibration signals collected from a gear-
box dynamics simulator were used to verify the proposed
Duraisamy et al. [41] have described a comparative study
of membership functions for design of fuzzy logic fault diag-
nosis system for single-phase induction motor.
Intelligent systems cover a wide range of techniques re-
lated to hard science such as modeling and control theory,
and soft science such as the artificial intelligence. Intelligent
systems, including neural networks, fuzzy logic, and wavelet
techniques, utilize the concepts of biological systems and
Pratesh Jayaswal et al. 9
human cognitive capabilities. These three systems have been
recognized as a robust and alternative to some of the classical
modeling and control methods [24].
In this paper, authors have been presented a brief review of
art of machinery fault detection, dierent conventional and
recent techniques were discussed for machine fault signa-
ture analysis with particular regard to rolling contact bearing
fault diagnosis through vibration analysis. After the review of
literature on machine fault signature analysis, the following
points are concluded.
(i) Prevention of potential failure is required for reliable
and safe operations of machineries and the preven-
tion of catastrophic failure can be done by appro-
priate maintenance. Condition-based maintenance is
the best suitable technique to avoid unwanted futuris-
tic failures through condition monitoring or signature
analysis for rotating machineries. Vibration signature
analysis is the best suitable technique available for fault
(ii) Among all machine components rolling contact bear-
ing is needed more attention towards signature anal-
ysis. The lot of scope is available in bearing fault sig-
nature analysis through vibration data for multiple
points or generalized faults.
(iii) Vibration analog signal can be converted in dis-
crete data for further investigation and various time-
domain and frequency-domain features can be used
for further investigations. The Hilbert and wavelets
transform have tremendous scope in machine fault
signature analysis.
(iv) Expert system based on ANN and fuzzy logic can be
developed for robust fault categorization with the use
of extracted features from vibration signal.
These conclusions motivate further research to incorpo-
rate other parameters and symptoms with vibration features
to develop more robust expert systems for machine faults sig-
nature analysis.
The authors are pleased to acknowledge the support of Mad-
hav Institute of Technology and Science (MITS), Gwalior,
India, for providing the facility of literature review. A spe-
cial thanks to Dr. S. Wadhwani, Lecturer, Electrical Engineer-
ing Department, MITS Gwalior, India, for the motivation
throughout the literature survey.
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... The rolling element bearing (REB) is employed in numerous industrial applications, and its failure can lead to machinery failure [1,2]. As a result, defect analysis is required to avoid bearing failure [2,3]. REB vibration data gives a great deal of information about problems and their fault location [4]. ...
... Vibration analysis is often used to detect localized faults in REB [5,6]. The periodic force effects caused by impulsivity at a particular frequency in the presence of a defect are computed using the shaft speed, sampling frequency, and bearing geometry [2][3][4]. As a result, periodic impulses are a vital status indication of REBs, and defect diagnosis classifies the bearing characteristic frequencies (BCFs) in most circumstances by assuming the outer race is stationary [4,5,7]. ...
The rolling element bearing is used in various machinery and produces vibration due to imperfections, surface irregularities during manufacture, damaged bearings, and inaccuracies in the allied element. Also, the rolling element bearing vibration generally shows non-linear dynamic characteristics and is masked with heavy background noise. This noble investigation advances a hybrid technique for removing background noise from the vibration signal and detecting bearing defects. Translation invariant wavelet denoising is the initial stage in this hybrid method for noise removal from the signal. The second phase uses Hierarchical Entropy (HE) for defect feature frequency extraction. Hierarchical entropy at scale four and SampEns of eight hierarchical decomposition nodes was utilized to determine the defect feature vector. In particular, low-frequency components are investigated through multi-scale entropy (MSE), but hierarchical entropy (HE) incorporates low-frequency and high-frequency components and can extract more defective information. Implemented a multi-class support vector machine (SVM) for extracting Hierarchical entropy as feature vectors. These feature vectors are trained by utilizing particle swarm optimization (PSO). To accomplish a prediction model, examine the optimal SVM parameters and then various bearing conditions with the variation of type, size, speed, and load severity identified by SVM. The investigation results show that hierarchical entropy can adequately and more precisely express the features of bearing vibration signals. It is beyond MSE, and the proposed Nobel hybrid Translation invariant wavelet denoising and Hierarchical entropy-based method will effectively remove the noisy background signal. Also, it distinguishes different bearings successfully, indicates the bearing conditions correctly, and is more prominent than those found on MSE.
... Several industries are planning to implement and evaluate industry IoT, which is expected to lead to growth. In addition to connecting smart machines, ensuring data management, sensors, and application layers are the [3,4] -✓ ✓ ✓ ✓ -Axial flux [5,6] -✓ ✓ ✓ --Lubricating oil debris [7] ----✓ -Cooling gas [8] ✓ ✓ ✓ ---Partial discharge [9] ✓ -----components of the industrial IoT concept [31]. For monitoring the status of IoT systems online, big data management and sensor data analysis are challenging tasks [32]. ...
... More than 19 million measurements were collected. Any type of change in the part or component of the machinery that prevents it from satisfactorily performing its intended function is termed a machine fault (Jayaswal et al., 2008). The fault in the current study is defined as the conditions in the ACS that cause it to deviate from its normal operations. ...
Existing studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS-ML). HUS-ML first identifies normal operations and known faulty conditions through supervised learning. Then, an anomaly detection algorithm is applied to spot any unseen faulty conditions. The framework is tested using acceleration measurements from a low-rise automated construction system prototype. HUS-ML outperformed the conventional machine learning approach in activity recognition and fault detection with an average F1 score of 86.6%. The conventional approach failed to detect unseen faulty operations. HUS-ML identified known faulty operations and unseen faulty operations with F1 scores of 98.11% and 76.19%, respectively. The generalizability of the framework is demonstrated by validating it on an independent benchmark dataset with good results.
... Thus, identifying machine failures before they break is extremely important for the maintenance sector of any industry. [1][2][3]. ...
PurposeFault diagnosis is vital to any maintenance sector since early fault detection can avoid catastrophic failures and also a waste of both time and money. Common defect diagnostic methods take just a few features from the vibration signal, which can lead to a wrong analysis. Deep Learning (DL) is well-known for its ability to extract features from a signal, and a Convolutional Neural Network (CNN) is one of the most successful deep learning approaches.Methods This paper uses a CNN with Short-time Fourier Transform (STFT), a time-frequency feature map, to extract as much information as possible from vibration signals. To validate the method, an experimental bench was used where it was possible to simulate up to six different faults. A vibration signal in the time domain was recorded to obtain the STFT response. Then, a CNN is trained to diagnose and predict the faults, considering the STFT as the only input.ResultsThe findings suggest that the proposed method can properly identify the various faults.Conclusion Since the approach is based on frequency domain analysis, it can be easily replicated for different motors.
To ensure higher reliability, it is necessary to identify the most prominent cause of failure. It was observed in multiple works that most of the researchers used failure mode and effect analysis (FMEA) and failure mode and effect criticality analysis (FMECA) but these approaches contain lots of hindrances. Alternatively, the MATRIX FMEA approach applied to identify the most prominent cause of failure and critical sub-component prone to that particular failure. The matrix FMEA developed for design engineers so that they can identify the potential cause of failures and design-out them in new product design. The central idea of this research is to apply this approach to maintenance, since the potential failure mode and the affected components were known. In most research, a single component such as a cylinder or turbocharger is used for analysis, but this research targets an entire machine. Multiple researchers also highlighted that the combination of non-destructive testing (NDT) provides better results instead of single NDT. Multiple researchers also highlighted the suitability of the analytical hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) for decision making (DM). Therefore, this work covered firstly, a novel AMFMEA (AHP-Matrix FMEA) to identify the most critical component of particular machinery located in a heavy industrial setup and secondly, DM approach for combining NDTs. It was observed that the incorporation of AHP in Matrix-FMEA improved its analytical ability and reduced overall computation time. This work also provided the guideline with a detailed procedure for combining NDTs.
Full-text available
The rotating machine comprises of numerous components such as shaft and bearing. The overall performance of machine is dependent on the health of these components. Vibration analysis is an effective tool to identify these faults. A methodology for ball bearings fault diagnosis using Artificial Neural Network and Decision Tree classifier is presented in this paper. The Finite Element analysis is carried out using ANSYS for a healthy bearing and a bearing having fault at inner race. The experimental vibration data for healthy and faulty bearing is acquired using FFT analyzer. Finally, after bearing faults classification using statistical feature extraction, the data input is fed to machine learning algorithm. Two machine learning techniques are used for faults classifications, i.e., Artificial Neural Network (ANN) and Decision Tree Classifier (DT). The simulation data is used for training purpose whereas the experimental data is used for testing purpose. It is observed that Ball Pass Frequency at inner Race is the indication of fault. The simulation and experimental results are in close agreement with the literature available. The proposed model of machine learning is able to identify rolling element bearing faults. The accuracies of ANN model and DT classifier model are 87% and 89% respectively.
The article presents the idea of the response compression technique called signature analysis used to validate PLC software in terms of functional control safety. The method of implementation of the developed validation idea, having practical application, was presented on the simulation examples. The research results based on a selected example are presented.
In machining, the tool condition has to be monitored by condition monitoring techniques to prevent damage by the use of tools and the workpiece. Cutting forces acting on the tool between zero and maximum values cause the cutting edge to crack and break. Predetection of this situation in the cutting tool is very important to prevent any negative situation that may occur. This study introduces a vibration-based intelligent tool condition monitoring technique to detect involute form cutter faults such as tool breakage at different levels during gear production on a milling machine. Machine learning algorithms such as artificial neural network, random forest, support vector machine, and K-nearest neighbor were used to detect the broken teeth and its level of breakage. According to the results obtained, it was observed that all the algorithms are successful in detecting faults in different teeth; also they have identification advantages according to different fault levels. In addition, the time and frequency domain analysis and continuous wavelet transform were used to determine the local faults. The developed machine learning-based detection performances compared the classical time and frequency domain analyses and continuous wavelet transform to prove the effectiveness and precision of the proposed methods. The results showed that all of the machine learning techniques have satisfactory performance to be used as fast and precise detection tools without complex calculations for detecting tool breakage.
Rolling element bearing generates a complex vibration due to geometrical flaws, surface irregularities during production, faulty bearing used, and error in the associated component. These vibration signals are usually covered with background noise. This research paper has shown the extraction of fault feature frequency when the raw vibration signal is immersed with heavy noise and this can be achieved by applying kurtosis, spectral kurtosis, and bandpass filter before the envelope spectrum. Besides, this article has revealed several methods that can extract several frequency spectrum characteristics. In this paper, the vibration signal case study is discussed, and this vibration signal was immersed with heavy background noise. The envelope spectrum analysis fails to isolate the fault function from this signal and needs some advanced strategy to solve this kind of situation. Many researchers used the de-noising process as a pre-filter signal before enveloping. Also, de-noising has some drawbacks, like the loss of original features. The noise degrades the performance of signal-processing algorithms. Applying kurtosis, spectral kurtosis, and bandpass filter before the envelope spectrum is done to overcome these drawbacks. These frequency spectrum characteristics more efficiently serve a bearing health. For bearing diagnostics, envelope analysis is a useful vibration analysis tool. The frequency-domain characteristics allow for a quick assessment of a machine's health without the need for extensive diagnostics. The intermittent impacts of a defected bearing were collected using envelope analysis from the modulated signal. When the vibration signal is comparatively low in energy and “embedded” within additional vibration from the related part, it is still rational.
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Antifriction bearing failure is a major factor in failure of rotating machinery. As a fatal defect is detected, it is common to shut down the machinery as soon as possible to avoid catastrophic damages. Performing such an action, which usually occurs at inconvenient times, typically results in substantial time and economical losses. It is, therefore, important to monitor the condition of antifriction bearings and to know the details of severity of defects before they cause serious catastrophic consequences. The vibration monitoring technique is suitable to analyze various defects in bearing. This technique can provide early information about progressing malfunctions. This paper describes the suitability of vibration monitoring and analysis techniques to detect defects in antifriction bearings. Time domain analysis, frequency domain analysis and spike energy analysis have been employed to identify different defects in bearings. The results have demonstrated that each one of these techniques is useful to detect problems in antifriction bearings.
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Monitoring of the vibration characteristics of mechanical systems provides valuable insight into the health of the equipment. Evaluation of vibration features using standards derived from performance of new systems provides benefits in numerous applications. Analysis of the data stream is carried out either on-line or off-line, often utilizing Fourier analysis techniques at a constant rate of operation. In many applications, acquiring vibration data only while the monitored machinery is operating at a specific condition is a difficult limitation that often results in a scarcity of useful data. To ideally assess the health of the machinery in a reliable and timely fashion, it is advantageous to monitor vibration at variable operating states thus avoiding many of the afore-mentioned data collection difficulties. In addition, it is possible to capture valuable machinery health information that might otherwise be neglected, since developing faults might be expected to appear preferentially at some of the different operating conditions. A nonparametric modeling technique developed by SmartSignal Corporation provides very early warnings of the onset of faults in plant applications over full operating ranges. A multivariate model is constructed based on traditional monitoring sensors (pressures, temperatures, flows, etc.); this model is then used to generate high fidelity real-time estimates of sensor values that represent normal plant behavior. These estimates are compared to the actual sensor readings to produce residuals that are analyzed to detect faulty system components. A variety of trending and statistical tests are used to reliably detect the evolving changes in the equipment. In doing so, the advantageous behavior of a residual constructed using a properly functioning model consistently facilitates early enunciation of the developing fault. Here we demonstrate the use of this nonparametric approach for detecting faults in rotating machinery via extracted features from vibration signals captured at constant and variable speeds. The features become the "sensors", analogous to the traditional sensors used in plant monitoring applications. We demonstrate an ability to reliably detect subtle changes in the modeled features within vibration data obtained from a laboratory mechanical test system with induced faults. These subtle changes can then be correlated with the severity of the induced faults, which in practice can provide a mechanism for estimating remaining useful life of the monitored machinery.
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This paper aims to provide a broad review of the state of the art in fault diagnosis techniques, with particular regard to rotating machinery. Fault diagnosis is a subject too wide ranging to allow a comprehensive coverage of all of the areas associated with this field to be undertaken, and it is not the authors' intention to do so. However, a general overview of the broader issues of fault diagnosis is provided, and several of the various methodologies are discussed. A detailed review of the subject of fault diagnosis in rotating machinery is then presented. Special treatment is given to the areas of mass unbalance, bowed shafts, and cracked shafts, these being among the most common rotor-dynamic faults. Vibration response measurements yield a great deal of information concerning any faults within a rotating machine, and many of the methods using this technique are reviewed.
Conference Paper
The rolling bearing signal is investigated according to the principal that the Wavelet can extract the signal envelope. A Wavelet-based self-information extracting envelope method (WSEM) is applied. Application of the method demonstrates that the method is effective to extract the rolling bearing signal envelope and is useful to analysis the rolling bearing faults.
We present a generic methodology for machinery fault diagnosis through pattern recog- nition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different bearing housing structures. More specifically, the proposed technique contains effective feature extraction, good learning ability, reli- able feature fusion, and a simple classification algorithm. Examples with experimental testing data were used to illustrate the idea and effectiveness of the proposed method. @DOI: 10.1115/1.1687391#
In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the wavelet transform and envelope spectrum is proposed for detecting and localizing defects in rolling element bearings. This approach is capable of completely extracting the characteristic frequencies related to the defect from the resonant frequency band. Based on the Shannon entropy of wavelet-based envelope spectra, a criterion to select optimal scale to monitor the condition of bearings is also presented. Experiment results show that the proposed approach is sensitive and reliable in detecting defects on the outer race, inner race, and rollers of bearings.