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The present work shows a condition monitoring system applied to detect Fault Condition in EMA Systems. Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. However, unexpected faults and lack of safety hinder the massive use of EMAs in flight control actuators and force to develop new systems and methods for supervision in aircrafts actuators. This research covers wide-range irregularities which are very often more difficult to analyse. In addition to traditional techniques like vibration and current signals, high frequency current bearing pulses and acoustic emissions are also analysed. A multivariable fuzzy inference analysis approach is presented to get around the diagnostic difficulty.
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EMA Fault Detection
Using Fuzzy Inference Tools
Jordi Cusidó i Roura, Miguel Delgado Prieto, Jose Luis Romeral Martínez
MCIA Group, Technical University of Catalonia
1. Introduction
The challenge in aircraft power system architecture is to move towards the electric power
aircraft subsystems including flight control actuation, environmental control system, and
utility functions. In this context emerged the concept of “More Electrical Aircraft”, which is
continuously supported by the new advances in power electronics, electrical motors, digital
control and communications.
Traditionally, for primary flight control surfaces only rotary and linear electro-hydraulic
actuators (EHA) have been considered, however the later trend is to replace them by electro
mechanical actuators (EMA). An EMA has no internal hydraulic fluid, instead using electric
motors to directly drive the ram through a mechanical gearbox. Compared to an EHA, the
EMA has certain advantages. There is a new issue related to the aforesaid new trend with
the aim of improve system reliability. This is the fault detection and diagnosis of the failures
that may take place in both, mechanics and electric components. So, it is well assumed that
the safety is the main issue for the EMAs development. The motor failures, the damaged
bearings, and the eccentricities existing in the drive train, affects on one hand the air gap
flux distribution and on the other leads to current and voltage unbalances. However, it is
difficult to examine EMA faults by analysing only specific fault harmonics due to fault
signal complexity.
To ensure Airplane operation every system needs to be tested and have an associated a Test
Program Set (TPS) System. A lot of work has been done in field of TPS and System Design
ensuring the testability of a component [1]-[2]. Design for Testability is a major trend in the
world of Aerospace and Defence. This paper presents approaches for an one-board fault
detection system and TPS fault detection system for EMA by means of Fuzzy Inference Tool.
Presented application
The solution to monitor the condition of electric motor ball bearings with distributed fault
proposed in this work is based in problem analysis from a multivariable point of view, what
is to say the obtaining of a fault level result from the combined analysis of different signals
with the use of fuzzy logic inference techniques. This is proposed to achieve a reinforced
diagnose that will be more effective when it comes to detect bearing failure than using just a
single signal, especially when it comes to damage severity evaluation.
Acoustic Emission
2. Basic theory
A. Vibration analysis
Vibration analysis is one of the most extended condition monitoring techniques [4]. Despite
being a reliable, well studied robust technique, one of its drawbacks is that it requires that
the motor under test has a vibration transducer installed, condition that makes its online
application expensive. In this work its study is set out as a reference for the other techniques
without this kind of restriction.
Vibration frequency components related to specific faults - inner (firf), outer (forf) and ball
(fbsf) faults - can be calculated using the following expressions [2][4]:
 =
 ·cos
 =
 ·cos
 =
 1−
 ·cos
2 (3)
n: number of balls
r: rotor speed
bd: ball diameter
pd: bearing pitch diameter
: contact angle of the ball on the race
B. As some works and standards [5] [6] [7] set out, a RMS vibration value evaluation of the motor
also provides a good indicator for motor health, allowing machine overall fault diagnosis - Stator
Stator currents analysis (MCSA) represents an interesting alternative method with its own
particularities and benefits; the most interesting of them is sparing the access inside the
motor making it easy to perform its online fault analysis.
Previous works have shown the existing correlation between vibration and currents RMS
values [5]. Although it is a complex function that relates both magnitudes, this work tries to
check the RMS currents reliability in order to perform the motor status diagnose.
With this knowledge, it is possible to execute an RMS calculation over the acquired current
signals with the aim of performing a more precise and straightforward operation.
C. High frequency common-mode pulses
One of the biggest culprits for bearings failure are common-mode circulating currents,
which are generated by switching inverters and expose the motor terminals to high dv/dt.
This phenomenon has been widely exposed in [6] and [7].
In this experiment, to limit the acquired signal to only pulses flowing through bearings (the
responsible of balls degradation), a motor modification was introduced. All the ball bearing
under test were aisled from the motor stator frame but in a point connected to ground
EMA Fault Detection Using Fuzzy Inference Tools 269
through a cable where the pulses were measured. Bearings insulation was achieved by
surrounding the piece with a PTFE flat ring with a hole mechanized in it to let the cable pass
These currents typically show a frequency range of about 5 MHz with a typical period of 20
μs between bursts.
D. Acoustic emissions
The Acoustic Emission Technique (AET) is a very promising tool that has practical
application in several fields and specifically recent important relevance in condition
monitoring of machines. [8] [9] [10]
Acoustic emissions (AE) are high frequency elastic waves in the ultrasound range that
appear when a material suffers localised plastic deformation. The analysis of this stress
waves shows the nature of the original producing source and, therefore, enables the
diagnose conducting to the actual element fault type and severity. In the bearings field, AE
is a good tool to detect impulsive faults like wear, ball impacts and lubrication problems
(like contaminants or degradation).
Acoustic Emission is therefore defined as a radiation of mechanical elastic waves produced
by the dynamic local rearrangement of the material internal structure. This phenomenon is
associated with cracking, leaking and other physical processes and was described for the
first time by Josef Kaiser in 1950. He described the fact that no relevant acoustic emission
was detected until the pressure applied over the material under test surpassed the
previously highest level applied.
Acoustic Emissions Technique is classified as a passive technique because the object under
test generates the sound and the Acoustic Emission sensor captures it. By contrast, Active
methods rely on signal injection into the system and analysis of variations of the injected
signal due to system interaction.
Then an acoustic emission sensor captures the transient elastic waves produced by cracking
or interaction between two surfaces in relative motion and converts their mechanical
displacement into an electrical signal. This waves travel through the material in
longitudinal, transverse (shear) or surface (Rayleigh) waves, but the majority of sensors are
calibrated to receive longitudinal waves.
Wherever the crack is placed, the signal generated travels from the point of fracture to the
surface of the material. The transmission pattern will be affected by the type of material
crossed and then isotropic material will lead to spherical wave front types of propagation
only affected by material surfaces or changes, where the Snell law rules their reflection and
reflexion. On Figures 1 and 2 is shown the evolution of acoustic waves inside a Material. On
Figure 2 it is shown how appear reflections on waves due to the defect.
The biggest advantage of this method is probably that it is capable of detecting the earliest
cracks of the system and their posterior growth, making possible fault detection before any
other current method. The main drawback is that it requires additional transducers and a
well controlled environment.
Acoustic Emission
Fig. 1. Acoustic Emission Wave Propagation
Fig. 2. Acoustic Emission Wave Propagation in fractured Material
Bearing measurements and analysis
The hertzian contact stresses between the rolling elements and the races are one of the basic
mechanisms that initiate a localized defect. Faults in bearings mainly appear in races and
balls. Damages in the bearing races are due to metal fatigue and consequent plastic limit
variation. Singular ball defects include cracks, pits, and spalls on the rolling surfaces, at it is
shown on Figure 3.
Fig. 3. Bearing Damage Evolution
EMA Fault Detection Using Fuzzy Inference Tools
Bearing race damage is characterized firstly by changes of metal characteristics like elastic
limit and later by the appearance of pits and transverse flutes burnt into the bearing race.
Bearing race defects lead to changes in high frequency resonance of the metal. Singular
defects in rolling element give rise to isolated impacts as the defective surface hits another
surface and produces a single detectable AE pulse. Frequency and periodicity of the pulses
are related to material characteristics and rotating speed, and are also depending on the type
of bearing.
A number of signal processing methods have been used on the time domain to diagnose
failures by AE measurements in machinery [13]-[14]. Although these methods are quite
simple to apply, it is apparent that they involve a significant expertise in the interpretation
of the output [15]. As a conclusion, and despite lubrication and some bearing faults are
detectable in the time domain analysis, to benefit most from the high sensitivity of AE to
defects filtering and reconstruction from time - frequency transforms are proposed in this
project, as a way to diagnose the bursts and apply time – frequency analysis to perform the
feature extraction and characterise the faults. If mean or overall AE parameters obtained
from characterisation are considered as fault detection parameters, they can be most suited
as a trending parameter where its current value is to be compared to previous ones under
similar operational conditions; however more detailed investigations are still required for
applying AE for prognosis.
Fig. 4. Main defects on Gears
Gear boxes measurements and analysis
Whilst vibration analysis on gear fault diagnosis is well established, gearboxes are inevitably
more complex to monitor using vibration analysis as they contain various shaft support
bearings rotating at different speeds and a number of gear teeth interactions which again are
operating at different speeds.
Fatigue tests were carried out and they showed that AE detected the first sign of failure
when the gear reached 90% of its final life. As the crack progressed, AE amplitude
increased. During the final stage of gear tooth fracture, a significantly high amplitude AE
burst was detected. On the other hand, the vibration level did not change significantly in the
initial stage of crack initiation and propagation until the final stage of failure [16].
Acoustic Emission
As alternative to these vibration monitoring techniques, AE measurements can be made and
then do further analyses by using different signal processing techniques, such as cumulative
energy count [17], monitoring of rms, standard deviation and duration of AE [18], and
kurtosis analysis [19].
Wavelet transforms have been also used for fault diagnosis of gears [20]. By this technique,
the time domain AE signals of a rotating machine with normal and defective gears can be
processed through wavelet transform to decompose in terms of low-frequency and high-
frequency components. The extracted features from the wavelet transform were used as
inputs to an artificial – intelligence based diagnostic approach. From these experiments, it is
concluded that AE method offered an advantage over vibration monitoring techniques,
especially for rubbing faults at a low speed. However, difficulty in understanding AE
signals, complexity in related signal processing and a lack of industrial development have
hampered the manufacture and large scale use of these kind of sensors.
3. Experimental results
3.1 Experimental setup
In order to assess the effectiveness of the methodology proposed in this work, it has been
checked by means of experimental data obtained from a motor bench.
The experimental motor bench is based on two identical featured face to face motors, the
motor under test and the motor that acts as a load. Between the motors it has been added an
screw and a mobile part which is displaced over it. The screw as well as the mobile part has
been provided by SKF. The motors are two SPMSMs with 3 pairs of poles, rated torque of
2.3 Nm, 230 Vac, and rated speed of 6000 rpm provided by ABB Group. The motor under
test was driven also by an ABB power converter model ACSM1. The drive control for the
motor was a vector control, with speed control loop.
The measurement equipment is focused on the acquisition of a stator current, stator
common mode currents, vibration and acoustic emissions. The stator currents have been
measured by means of a Tektronix current probe model A622. It provides 10–100 mV/A
output and can measure ac/dc currents from 50 mA to 100 A-peak over a frequency range
from dc to 100 kHz. The stator currents have been acquired by means a PXIe 1062 system
from National Instruments sampling at 50 kHz, 100 ms for each measurement.
The vibration measurement has been performed by an ENDEVCO Isotron KS943B.100
triaxial accelerometer with IEPE (Integrated Electronics Piezo Electric) standard output and
linear frequency response from 0.5 Hz to 22 kHz with a maximum of 60g. The acceleration
data was collected using a specific acquitsition card connected to the PXIe 1062 system from
National Instruments sampling at 20kS/s, 10 seconds for each measurement.
3.2 Experimental results
A. Vibrations
With regard to the bearings units under test, there was a healthy one (with very similar
vibration levels to other new units tested in previous works) and the other two tested units
had different levels of damage due their operation hours, qualitatively evaluated with a
shock pulse tester from SPM Instruments.
EMA Fault Detection Using Fuzzy Inference Tools 273
Fig. 5. RMS Vibration for healthy unit, all speeds in rpm and loads.
Fig. 6. RMS Vibration for lightly damaged unit, all speeds in rpm and loads.
Fig. 5, 6 and 7 show the evolution of the RMS value of each motor vibration for all speeds
and load values tested. The healthy unit shows lower values especially detectable under
nominal conditions.
Fig. 7. RMS Vibration for heavily damaged unit, all speeds in rpm and loads.
Acoustic Emission
Clearly, the healthy motor in Fig. 5 shows lower RMS values of vibration in comparison to
the other two units. Fig. 7 unit data, which was in the worst operational condition according
to the SPM measurements performed, gave also the highest levels of RMS vibration values.
B. Stator currents
To avoid the influence of the main harmonic power value in the RMS measurement, signals
have been previously filtered using a band-rejection 5th order Butterworth filter centred in
the power supply main harmonic with a bandwidth of 20 Hz between higher and lower cut-
off frequencies. Tables 1 and 2 compare the RMS filtered values of the heavily and lightly
damaged units with the healthy one.
Heavily Damaged-Healthy (A RMS)
Load % \ speed 300 750 1050 1500
00,004 -0,006 -0,008 -0,007
50 0,036 0,03 0,073 0,044
100 0,018 0,026 0,024 0,024
Table 1. Difference in RMS filtered current value between heavily damaged unit and healthy
one used as reference.
Lightly Damaged-Healthy (A RMS)
Load % \ speed 300 750 1050 1500
00,008 0,002 -0,003 -0,003
50 0,002 -0,011 -0,002 -0,005
100 0,02 0,012 0,003 0,014
Table 2. Difference in RMS filtered current value between lightly damaged unit and healthy
one used as reference.
A significant difference can be clearly appreciated when the motor is heavily damaged
under load condition. Light damage is noticeable under nominal load conditions but its
detection does not seem to be easily reliable.
B. High Frequency bearings pulses
Bearings pulses threshold analysis has been executed to validate theories of correlation
between bearings state (wear, lubrication, distributed defects, etc.) and pulses discharge
over a threshold value.
The results summarized in Figure 8 show that over a defined threshold level healthy
bearings undergo a bigger number in comparison to the damaged units. It is noticeable also
that this method is able to detect failure at its initial stage if the threshold is correctly placed.
C. Acoustic emission testing
Acoustic Emission acquired data has been statistically classified by means of value binning
tools and histogram presentation.
Fifteen sets of data were acquired for each motor and averaged. Fig. 5 shows the results
comparing the RMS voltage values acquired for the different units under test.
EMA Fault Detection Using Fuzzy Inference Tools
Fig. 8. Number of bearing pulses over threshold value of 3.5 A for all motors under test.
Healthy, lightly damaged and heavily damaged.
Fig. 9. Acoustic Emission voltage values classification
It is advisable that pulses over 8 V only appeared during the damaged motor testing while
under 7 V that unit does not show more activity than the healthy and lightly damaged units.
Then, the fuzzy inference system designed uses as reference the number of pulses that
surpass the 7 V value, which is the zone where the distinction of the fault severity of the unit
seemed to be more noticeable.
3.3 Fuzzy inference tool
The analysis of the actual bearing status was performed using a fuzzy logic inference
implementation [11] [12] which maps given inputs to a single output, the different signals
acquired are linked to a damage value scaled from 1 to 3.
The membership functions, like Fig. 10, have been obtained through training and validation
process, for each signal under analysis using real motor data. MATLAB “Adaptive neuro-fuzzy
inference system” tool has been used for this purpose. Fig. 11 shows the obtained relationship
between Vibration and Stator Current RMS values against the Failure Level output for a motor
speed of 1500 rpm and a load of 0%.
Acoustic Emission
Fig. 10. Membership function plot for Current RMS. (motor speed: 1500 rpm, motor load: 0%)
Fig. 11. Plotted surface showing the relationship between the system inputs Vibrations RMS
value (g) and Stator Currents RMS value (A) versus the Failure Level output. (Motor speed:
1500 rpm, motor load: 0%)
This process explanation will be properly expanded on the final version of this paper.
To perform the evaluation of the monitoring system designed, fifteen sets of data were
collected from the same units and processed. Table 3 summarizes the obtained results.
Unit Matches Success %
Healthy 15 100 %
Lightly Damaged 14 93,33%
Heavy Damage 13 86,66%
Table 3. System testing results
All healthy data sets were correctly identified, whilst one of the lightly damaged was
recognised as a heavily damaged set and two of the heavily damaged sets were identified as
lightly damaged ones. The percentage of success was reasonably high and its improvement
is still possible if more data sets are used during the system training stage.
EMA Fault Detection Using Fuzzy Inference Tools 277
4. Conclusions
This chapter gives an overview of a condition monitoring system that uses a multisensory
fuzzy inference approach used to detect faults in EMA Systems. The results show that a
multivariable design contributes positively to damage monitoring of EMA, being a more
solid solution than just using any of the single signals involved..
The results show that a multivariable design contributes positively to damage monitoring of
bearings, being a more solid solution than just using any of the single signals involved..
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Acoustic Emission
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... The statistical data processed from the AE signal and the cuting power were also used as input data for ANFIS [9]. Cusido et al. provided approaches for a one-board fault detecting system and test program set (TPS) fault detecting system for electromechanical actuators (EMA) ball bearings by analysing the diferent vibra- tion and AE signals and by using FL inference techniques [49]. ...
... The approaches presented by Jordi [67] for Test Program Set (TPS) fault detection system for electro mechanical actuators (EMA) and a one-board fault detection system by analysis of various vibration and acoustic emissions signals applying fuzzy logic inference techniques. Subsequently, the approach of support vector machine (SVM) is carried out as classification technique with emphasis on statistical learning theory (SLT) relying on the concept of hyper plane classifier or linearly separability. ...
... The approaches presented by Jordi [67] for Test Program Set (TPS) fault detection system for electro mechanical actuators (EMA) and a one-board fault detection system by analysis of various vibration and acoustic emissions signals applying fuzzy logic inference techniques. Subsequently, the approach of support vector machine (SVM) is carried out as classification technique with emphasis on statistical learning theory (SLT) relying on the concept of hyper plane classifier or linearly separability. ...
Full-text available
Machinery condition monitoring has become one of the essential components in the industry due to the ability of providing insight to the machine condition during operation as well as enhancing productivity and increasing machine reliability. This paper provides a review on using acoustic emission (AE) technique combined with artificial intelligence (AI) methods in the field of machinery condition monitoring and fault diagnosis. Even though many papers have been published in the area of machinery condition monitoring, this paper puts emphasis on gears and bearing only. Furthermore, the paper attempts to summarize and evaluate the recent condition monitoring research that utilizing AI includes fuzzy logic, artificial neural network (ANN), support vector machine (SVM), and genetic algorithms (GA) in fault diagnosis, fault classification, fault localization and fault size estimation in gear and bearing based on features extraction from AE signal. Machine condition monitoring philosophy and techniques have evolved based on intellectual systems. However, the acquired AE signal was found to be complicated in the application of gear and bearing monitoring, therefore it is required more attention. In addition, the use of AI methods in gear and bearing fault diagnosis still in the growing stage that requires lots of encouragement as it has a promising future in the field of machinery condition monitoring.
... The statistical data processed from the AE signal and the cutting power, were also used as input data for ANFIS [18]. Cusido et al. provides approaches for a one board fault detecting system and Test Program Set (TPS) fault detecting system for electro mechanical actuators (EMA) ball bearings by analyzing the different vibration and acoustic emission signals and by using fuzzy logic inference techniques [45]. Omkar et al. presented the results of fuzzy modeling to discover the problem in grinding, through digital processing of the acoustic emission signals produced during the process. ...
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Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.
... This fact makes necessary the diagnosis support of additional signal analysis (Frosini et al., 2008). The current trends in condition monitoring are related with the fusion of different features, which provide the possibility to merge fault indicators from different physical magnitudes (Cusido et al. 2010). This data fusion improves the diagnostic reliability, because fault indicators that are not descriptive enough themselves can contribute to detect faults in relation with others, especially if the features are extracted from different physical magnitudes, which enhance the monitoring capabilities. ...
Regarding the acquisition system, it is based on four different sensors connected to a main acquisition device. A triaxial shear design MMF branded piezoelectric accelerometer model KS943B.100 with IEPE (Integrated Electronics Piezo Electric) standard output and linear frequency response from 0.5 Hz to 22 kHz, was attached using stud mounting to the drive-end motor end-shield and its data was collected at 20kS/s during 1 second for each measurement. Phase stator currents were acquired using Hall effectTektronix A622 probes with a frequency range from DC to 100 kHz and collected at 20 kHz during 1 second for each measurement. High frequency CMC signal was measured at the cable attached to the bearings housing with a Tektronix TCPA300 amplifier and TCP303 current probe, which provides up to 15 MHz of frequency range, and acquired at 50 MHz during 100 ms for each measurement. Acoustic emissions were acquired with the use of a Vallen-Systeme GmbH VS-150M sensor unit with a range from 100 kHz to 450 kHz and resonant at 150 kHz. A Vallen-Systeme GmbH AEP4 40dB preamplifier was used before data acquisition at a sampling frequency of 25MS/s during 20ms each measurement. All the described sensors are connected to a PXI acquisition system from National Instruments formed by different specific boards.
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One of the earliest documented applications of acoustic emission technology (AET) to rotating machinery monitoring was in the late 1960s. Since then, there has been an explosion in research- and application-based studies covering bearings, pumps, gearboxes, engines, and rotating structures. In this paper we present a comprehensive and critical review to date on the application of AET to condition monitoring and diagnostics of rotating machinery.
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Acoustic emission (AE) was originally developed for non-destructive testing of static structures, however, over the years its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison to vibration analysis. However, limitations in the successful application of AE technique for monitoring bearings have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The investigation reported in this paper was centered on the application of standard acoustic emissions (AE) characteristic parameters on a radially loaded bearing. An experimental test-rig was modified such that defects could be seeded onto the inner and outer races of a test bearing. As the test-rig was adapted for this purpose it offered high background acoustic emission noise providing a realistic test for fault diagnosis. In addition to a review of current diagnostic methods for applying AE to bearing diagnosis, the results of this investigation validated the use of r.m.s, amplitude, energy and AE counts for diagnosis. Furthermore, this study determined the most appropriate threshold level for AE count diagnosis, the first known attempt.
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There are not international standards or universally accepted limit values available, that classify rotating machines as slow or high speed machines. The old international standard ISO 2372, which has been replaced with the new ISO 10816 standard, gave the vibration velocity severity ranges for different classes of machines. The old standard covered machines with rotational speeds from 600 rpm to 12000 rpm. The new standard does not contain any rotational speed limits. Sometimes the limit for low-speed rotating machines is set to 20 rpm or 30 rpm. In industry, it is easy to find machinery where the rotational speed in continuous running is lower than 2 rpm. In the condition monitoring of rotating machines, it is common practice to measure the vibration velocity or acceleration. At very low frequency, the vibration velocity amplitude becomes weak and therefore displacement measurement can sometimes be a suitable vibration measurement parameter. When the rolling element in the rolling bearing passes the early-stage fault in the case of an extremely low rotational speed, the energy that the collision generates is very low. In that case, the defect is difficult to detect in the frequency domain but can possibly be seen in the time domain. The frequency bandwidth of acoustic emission (AE) measurement method is typically in the range 100 kHz to 1 MHz. In that range, vibrations occur in a material by fracture of crystallites, crack nucleation and growth, several mechanisms involving dislocations, phase transformations in materials, boiling and electrical discharges. Each of these mechanisms is characterised by a rapid collective motion of a group of atoms. The present paper describes the use of the acoustic emission method in the monitoring of faults in an extremely slowly rotating rolling bearing. The introduction describes the principle of the measurement method of acoustic emission and the analysis methods used for the acoustic emission signal. The paper contains the results of AE measurements where the rotational speed of the shaft was from 0.5 rpm to 5 rpm. The measurements were carried out using a laboratory test rig with grease lubricated spherical roller bearings of an inner diameter of 130 mm and a load of 70 kN. Prior to testing the test bearing had been naturally damaged on its outer race during normal use in industry. The results of the acoustic emission measurement have been compared with the results of low-frequency vibration measurements, which have been carried out in the same test arrangement. The paper gives an example where acoustic emission measurements have been used in industry, in the monitoring of slowly rotating machinery.
A procedure is presented for fault detection of gears through wavelet transforms and artificial neural network (ANN). The time domain vibration signals of a rotating machine with normal and defective gears are processed through discrete wavelet transform to decompose in terms of low-frequency and high-frequency components. The features extracted from the decomposed signals are used as inputs to an ANN based fault detection approach. The ANN is trained using back-propagation algorithm with a subset of the experimental data for known machine conditions and tested using the remaining set of data. The procedure is illustrated through the experimental vibration data of a pump with and without gear fault. The roles of different vibration signals and their characteristic features in the fault detection process are investigated. The selection of features relevant to machine conditions leads to faster training requiring far less iterations.
In industry it is often very slowly rotating machinery which is the most critical to the production process as well as being the largest and the highest value. These factors combine to increase the economic requirement for Condition Based Maintenance and hence the importance of suitable means of Condition Monitoring. However slow rotational speeds result in reduced energy loss rates from damage related processes and because of this Condition Monitoring techniques which detect energy loss tend to be more difficult if not impossible to apply. Perhaps surprisingly this is not the case for the Acoustic Emission (AE) technique which is well suited to detecting very small energy release rates. As a result AE is able to detect subtle defect related activity from machinery, even when it is rotating very slowly. In this paper a new AE based signal processing approach is introduced which can provide simple but sensitive means of detecting the presence and evolution of faults in very slowly rotating machinery. These developments have further led to the creation of what is believed to be the first easily retro-fitted and affordable on-line monitoring module for very slowly rotating machinery.
In 1983 the initial results of an IEEE survey on large motors was published and presented at the 1983 IandCPS Conference. This was the first presentation of the results of a survey completed in 1982 of motors larger than 200 hp and no older than 15 years. The results presented here of the 1982 survey are to investigate the data further to address questions generated by the results of the earlier paper, to find additional correlations of the reliability criteria of some of the more interesting categories, and to bring out more results and categories available from the survey data. For information on the overall survey response and the general results of the surveyed categories, refer to the previous paper.
Vibration and debris monitoring methods are being increasingly used to detect gear tooth breakage. In this paper an alternate method of detecting gear tooth cracking is investigated. It is based on the phenomenon of acoustic emission (AE). The detectability of growing cracks using AE is established. Before this method can be used to detect crack growth in real systems, the transmissibility of these waves has to be studied. These waves have to propagate across a number of mechanical interfaces as they travel from the source to the sensor. The loss in strength of these waves at various interfaces commonly encountered in mechanical systems is studied in this paper.
Acoustic emission testing (AE) was employed in this research to monitor sound indications emanating from a specially designed and built gear box. Various types of failures were purposely induced, simulating the possible wear and tear conditions that a gearbox may undergo during its useful life. Results obtained at different speeds were plotted against causes of failure. Subsequent analysis revealed the importance of correlating AE results with known failures. The technique developed for early failure diagnosis has the potential of utilizing AE as a tool for predictive maintenance.
A survey on published information concerning the phenomena of bearing currents in variable speed drive systems due to fast switching IGBT-inverters is presented. This is taken as a starting point for further systematic investigations on the influence of different system parameters on bearing currents. The first results of these investigations are presented. Copyright © 2005 John Wiley & Sons, Ltd.