Cooling fan bearing fault identification using vibration measurement
ABSTRACT As a commonly used assembly in computer cooling systems, the normal operation of a cooling fan is critical for guaranteeing system stability and reducing damage to electronic components. Reliability analyses have shown that fan bearing failure is a major failure mode. Therefore, it is necessary to conduct research on fault detection of cooling fan bearings. In this paper we propose vibration-based fan bearing fault detection through the wavelet transform and the Hilbert transform. An experiment on fan bearings was conducted to collect vibration data for the validation of our proposed method. The analysis results show that the proposed method can identify different bearing faults.
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ABSTRACT: Based on field return and test data, the major failure mechanisms and failure modes of cooling fan system are presented in this paper. Then, the failure criteria and the reliability metrics for cooling fan systems are discussed. By critically comparing the accelerated life testing methods from various vendors, a practical accelerated life testing methodology is presented. The acceleration testing models and acceleration factor are also discussed. In the last section, a comprehensive reliability qualification procedure is proposedReliability and Maintainability Symposium, 2006. RAMS '06. Annual; 02/2006
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ABSTRACT: A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings is presented in this paper. Detection of both localized and distributed categories of defect has been considered. An explanation for the vibration and noise generation in bearings is given. Vibration measurement in both time and frequency domains along with signal processing techniques such as the high-frequency resonance technique have been covered. Other acoustic measurement techniques such as sound pressure, sound intensity and acoustic emission have been reviewed. Recent trends in research on the detection of defects in bearings, such as the wavelet transform method and automated data processing, have also been included.Tribology International. 01/1999;
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ABSTRACT: De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.Journal of Sound and Vibration. 01/2006;
Cooling Fan Bearing Fault Identification Using
School of Mechanical, Electronic and Industrial Engineering
University of Electronic Science and Technology of China
Chengdu, Sichuan 611731, China
Michael Azarian, Michael Pecht
Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD 20742, USA
Abstract—As a commonly used assembly in computer cooling
systems, the normal operation of a cooling fan is critical for
guaranteeing system stability and reducing damage to electronic
components. Reliability analyses have shown that fan bearing
failure is a major failure mode. Therefore, it is necessary to
conduct research on fault detection of cooling fan bearings. In
this paper we propose vibration-based fan bearing fault detection
through the wavelet transform and the Hilbert transform. An
experiment on fan bearings was conducted to collect vibration
data for the validation of our proposed method. The analysis
results show that the proposed method can identify different
Keywords—cooling fan bearing; fault identification; discrete
wavelet transform; Hilbert transform
Nowadays, computers are used in many different areas,
such as telecommunication,
marketing, health care, etc. Computer system failure may bring
inconvenience in our daily lives, or even cause severe
economic losses and catastrophic accidents under certain
conditions. The requirement of high operational reliability has
driven the research on diagnosis and failure analysis of
computer systems. However, it has been a challenging task due
to the complicated interactions of system performance
parameters and application environments (e.g., temperature,
moisture, and vibration) and their effects on system
degradation and failure .
As a commonly used assembly in a computer cooling
system, the mechanical parts of a cooling fan include bearings,
shaft, fan blades, and fan housing. A fan is used to move
heated air away from the components in the case. According to
, fan failure is a major problem for many electronic systems.
Bearing failure is the top contributor to fan failure.
The normal operation of a cooling fan impacts a computer
system by preventing instability, malfunction, and damage to
electronic components caused by overheating. Therefore, it is
necessary to conduct research on cooling fan bearing fault
detection so as to guarantee the normal operation of a fan.
The major type of bearing used in cooling fans is ball
bearings, mainly because it has a longer lifespan at higher
temperatures (63,000 hours at 50oC) compared to sleeve
bearings (40,000 hours at 50oC) . Ball bearings are the
fundamental rotating parts in mechanical systems, and much
research has been conducted in bearing fault diagnosis [4-8].
However, the literature on computer cooling fan bearing
reliability is very limited [2, 9, 10].
It is a challenge to identify ball bearing fault signatures
based on vibration signal because the bearing components,
including inner race, outer race, cage, and rollers, complicate
the bearing vibration signals. When a local fault exists in a ball
bearing, the surface is locally affected and the vibration signals
exhibit modulation . Therefore, it is necessary to
implement filtering and demodulation so as to obtain fault-
sensitive features from the raw signals. At present, the Hilbert
transform has been widely used as a demodulation method in
vibration-based fault diagnosis [12, 13]. It has a quick
algorithm and can extract the envelope of the vibration signal.
In addition, the wavelet analysis is able to decompose a signal
into different scales corresponding to different frequency
bandwidths [12, 14, 15], which can be treated as band-pass
The purpose of this study was to investigate fan bearing
fault identification methods using vibration measurements that
can be used for cooling fan degradation assessment and
prognostics. A test rig with a cooling fan was established, with
no lubricant in the ball bearing so as to accelerate the
experiment. To identify the type of bearing failure from the
vibration measurement at the end of experiment, a new method
was proposed based on the wavelet and the Hilbert transforms.
This paper is organized as follows. In Section 2, a brief
introduction to the wavelet and Hilbert transforms is given.
Section 3 presents our proposed method for fan bearing fault
identification using the vibration signal. A case study on fan
bearings is presented in Section 4, including a description of
the experiment and validation of the proposed method. Our
conclusions are summarized in Section 5.
A. Wavelet Transform
The wavelet transform is the time-frequency decomposition
of a signal into a set of wavelet basis functions. It possesses
flexibility in both the time and frequency domains, and it has
* Corresponding author. Email: firstname.lastname@example.org. Phone: +86-28-
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been widely used in machinery fault diagnosis. The continuous
wavelet transform (CWT) of a finite energy time domain signal
)(tx with the wavelet )(t
is defined as :
where ∗ denotes the complex conjugation,
and a and b are the scale and translation parameters,
As seen in (1), the wavelet coefficient
plane. A small scale parameter a corresponds to
a high-frequency component, and the translation parameter b
represents the location of the wavelet basis in the time domain.
is a measure of similarity between the signal
the wavelet )(t
at different frequencies determined by the
scale parameter a and different time locations determined by
the translation parameter b .
Although the CWT offers the possibility of a detailed
analysis of transients with an arbitrary fine frequency scale, it
is not computationally efficient and it results in high
redundancy. The discrete wavelet transform (DWT) is derived
from the CWT through discretization of the wavelet, and the
most common discretization of the wavelet is based on a power
of 2 (i.e., dyadic scales and positions ). That is,
Therefore, the discrete wavelet function and scaling function
can be defined as follows:
) 2 (
Mallat  proposed a fast wavelet decomposition and
reconstruction method, which is a classical signal processing
scheme, known as a two-channel sub-band coding. In the
decomposition process, the signal is convolved with a low-pass
filter and a high-pass filter, respectively. It produces two pieces
of decomposed signals, namely, the approximation signal and
the detail signal. For example, let
decomposition process can be repeated as follows:
detail signal at the jth decomposition level, respectively. In
reconstruction, a pair of low-pass and high-pass reconstruction
filters are convolved with )(tAj
are the approximation signal and
The decomposition of a signal using dyadic orthogonal
wavelets is a quadratic sub-band filtering. Suppose a signal is
collected at a sampling frequency
obtained by DWT on each scale corresponds to a frequency
2. The frequency band of the approximation
A is expressed as
s F . The information
]2, 0 [
And the frequency band of the detail
This means that the approximation and detail are the narrow-
banded sub-signals of the original signal.
B. Hilbert Transform
From a signal processing perspective, the Hilbert transform
can be interpreted as a filtering operation in which the
amplitude of the frequency component is unchanged, while the
phase is shifted by 90o. It is a time-domain convolution that
maps one real-valued time-history into another. Given a time
domain signal )(tx, its Hilbert transform )]([ txH is defined as:
where t and τ are the time and translation parameters,
In machinery fault detection, modulation caused by local
faults is inevitable in collected signals. In order to identify
fault-related signatures, demodulation is a necessary step, and
it can be accomplished by forming a complex-valued time-
domain analytic signal )]([ txA
and )]([ txH
. That is,
i ; )(ta is the envelope of )]([ txA, which represents an
estimate of the modulation in the signal.
THE PROPOSED METHOD FOR FAN BEARING FAULT
A. Fan Bearing Failure Behavior
The cooling fan bearings studied in this paper are typical of
those used in many computers. Therefore, it is necessary to
understand its failure behavior in order to conduct research on
fan bearing fault detection. Typical failures of ball bearings
include local faults on the inner race, outer race, cage, and
rollers. If there is a local fault on a certain part of a ball bearing,
the corresponding fault-related characteristic frequency and its
harmonics can be identified through spectral analysis in the
frequency domain . The formulae for the various
characteristic frequencies are as follows:
Ball spin frequency (BSF):
Ball pass frequency, inner race (BPFI):
Ball pass frequency, outer race (BPFO):
Fundamental train frequency (FTF):
the number of rolling elements, d is the mean diameter of the
rolling elements (mm), D is the pitch diameter of the bearing
(mm), and β is the contact angle (o).
rf is the rotating speed of the bearing shaft (Hz), n is
B. The Proposed Method
The function of fan bearings is to reduce friction and allow
a fan to operate at high speeds with lower noise. In cases where
noise can be heard, serious faults may have developed on
different parts of the bearing, complicating the vibration
measurement. In addition, fan bearings used in computer
cooling fans are small (5–8 mm in pitch diameter), which
makes it almost impossible to place several sensors on different
positions around the bearings for data collection. The DWT is a
quadratic sub-band filtering technique that can decompose the
original signal into different bands, and the Hilbert transform
provides a means of signal demodulation. Therefore, these two
techniques are used in fan bearing vibration analysis for the
identification of faults. A flow chart of the fan bearing fault
identification method based on the wavelet and the Hilbert
transforms is shown in Fig. 1.
Figure 1. Flow chart of the proposed method.
Given a piece of signal
necessary to reduce the trend and the DC component in the
signal. That is,
)(tx , a pre-processing step is
)(ty is the pre-processed signal, x is the mean value of
, and σ is the standard deviation of ))(tx .
In order to obtain sub-signals corresponding to different
frequency bands, the DWT with the Daubechies wavelet is
utilized, and the decomposition level is J . Therefore, a series
of detail signals )(tDj
the frequency range of )(tDj
is described in (7).
J,...,2 , 1
can be obtained, and
When a local fault exists in the bearing, there is modulation
in the signal. To reduce the impact of modulation, the Hilbert
transform was performed on all of the detail signals using (8)
and (9), and the corresponding analytical signals and their
Jj,...,2 , 1
), can be obtained. Here,
is the envelope signal of the j th detail signal
Hilbert transform. To identify the existence of characteristic
frequency components (such as BSF, BPFI, BPFO, FTF) of the
bearing, perform the spectrum analysis of )(taj
Here, )( fESj
denotes the absolute value of the Fourier
transform amplitude of the j th envelope )(taj
IV. CASE STUDY
A. Description of Experiment
The normal lifespan of a cooling fan can be several years,
and it is unrealistic to conduct an experiment for such a long
time. For a bearing to have its nominal lifespan at its nominal
maximum load, lubrication has a critical impact on the lifespan
of the bearing. Therefore, it is reasonable to accelerate the fan
bearing experiment through the reduction of the lubrication
level. Assuming that the nominal amount of lubricant is at the
100% level, then a certain lubrication level p% describes the
percentage of lubricant being added in the bearing.
In this study, an experiment was conducted to validate the
proposed method for fan bearing fault identification. To
accelerate the experiment, the lubrication level was 0%, which
means that no lubricant was in the bearing. Figure 2 shows the
cooling fan with bearings tested in this experiment.
Figure 2. The cooling fan with bearings being tested in this experiment.
The specifications of the fan bearing in our experiment are
given in Table 1. The cooling fan used in this experiment was
in its brand new state with ungreased ball bearings before the
experiment. At the beginning, the vibration signal of the fan
was collected at a sampling frequency of 102.4 kHz with 10-
second periods under an ambient environment. The fan speed
was 4,000 rpm. After that, the cooling fan was stressed in a
chamber at a high temperature (70oC). After 72 hours of
running at its maximum speed of 4800 rpm, the vibration
signal of the fan was collected at a sampling frequency of
102.4 kHz with 10-second periods under an ambient
environment. The fan speed was 4,000 rpm. Therefore, the
rotation speed of the bearing during data collection was
. Using (10)–(13),
characteristic frequencies were determined, as listed in Table 2.
rfthe various fault
FAN BEARING SPECIFICATIONS
Number of rolling elements n
Diameter of rolling element d
Pitch diameter of bearing D
Contact angle β
CHARACTERISTIC FREQUENCIES OF FAN BEARING
Ball spin frequency ( rR
) 211.96 Hz
Ball pass frequency, inner race (rI
) 256.87 Hz
Ball pass frequency, outer race (rO
Fundamental train frequency (rC
B. Experimental Results
To validate the proposed method, the vibration signals
collected before and after the 72-hour stressing experiment
were used to conduct the following analysis. The Daubechies
wavelet dB10 was used for signal decomposition; the
decomposition level was J =6. The Hilbert transform was
applied to demodulate the details )(tDj
6 ,...,2 , 1
Figure 3. Analysis results of the fan bearing before the stressing experiment.
The analysis results of the fan bearing before the stressing
experiment are shown in Fig. 3. Since the ball bearing was in a
brand new state at the beginning, no fault-related frequency
components were found from the spectra of the detail signals.
Fig. 4 shows the analysis results of the fan bearing after the
72-hour stressing experiment. From the spectra of detail signals
tDtD, many fault-related characteristic frequencies
and their harmonics were identified, which indicated that there
were several local faults at different parts of the fan bearing. In
fact, noise coming from the fan was heard at the end of the
Figure 4. Analysis results of the fan bearing after 72-hour stressing
Cooling fan bearing failure is a major failure mode in
computer cooling systems. Cooling fan bearing failure can
result in system instability, malfunction, and damage to the
computer system. In this paper, a fan bearing fault
identification method based on the discrete wavelet transform
and the Hilbert transform is proposed. In order to validate the
proposed method, an experiment with ungreased fan bearings
was conducted to obtain the vibration signal before and after
failure. The analysis results showed that the proposed method
0 200400600800 10001200
(2)Spectral of D2 signal
(1)Spectral of D1 signal
0 200 4006008001000 1200
(3)Spectral of D3 signal
(4)Spectral of D4 signal
(5)Spectral of D5 signal
(6)Spectral of D6 signal
(2)Spectral of D2 signal
(1)Spectral of D1 signal
(3)Spectral of D3 signal
0200 400 600800 1000 1200
(5)Spectral of D5 signal
(4)Spectral of D4 signal
(6)Spectral of D6 signal
0 200400600800 1000 1200
can identify the fault-related characteristic frequencies of the
The work described in this paper provides a promising way
to establish potential metrics for the description of bearing
health degradation. Therefore, it is desirable to develop a
condition monitoring system based on the proposed method
and realize on-line health evaluation of cooling fans. With such
a function, the critical failure of cooling systems can be
avoided, and the reliability and availability of electronics
systems can be guaranteed.
This research was partially supported by National Natural
Science Foundation of China (Grant No. 50905028),
Fundamental Research Funds for the Central Universities
(Grant No. ZYGX2009X015). We would like to thank Mr.
Hyunseok Oh at CALCE PHM Group, University of Maryland,
for his work on the experiment and discussion on the
improvement of this research.
 M. Pecht, Prognostics and Health Management of Electronics. London:
 X. Tian, “Cooling fan reliability: failure criteria, accelerated life testing,
modeling and qualification,” Proceedings of 2006 Reliability and
Maintainability Symposium, 2006, pp. 380-384.
 M. Williams, “Ball vs. sleeve: a comparison in bearing performance,”
Technical Paper from NMB Technologies Corp.
 N. Tandon, and A. Choudhury, “A review of vibration and acoustic
measurement methods for the detection of defects in rolling element
bearings,” Tribology International, Vol.32, No.8, 1999, pp. 469-480.
 W. He, Z.N. Jiang, and K. Feng, “Bearing fault detection based on
optimal wavelet filter and sparse code shrinkage,” Measurement, Vol.42,
No.7, 2009,pp. 1092-1102.
 V.K. Rai, and A.R. Mohanty, “Bearing fault diagnosis using FFT of
intrinsic mode functions in Hilbert-Huang transform,” Mechanical
Systems and Signal Processing, Vol.21, No.6, 2007, pp. 2607-2615.
 H. Qiu, J. Lee, J. Lin, and G. Yu, “Wavelet filter-based weak signature
detection method and its application on rolling element bearing
prognostics,” Journal of Sound and Vibration, Vol.289, No.4-5, 2006, pp.
 R.B. Randall, and J. Antoni, “Rolling element bearing diagnostics – A
tutorial,” Mechanical Systems and Signal Processing, Vol.25, No.2,
2011, pp. 485-520.
 H. Oh, M. H. Azarian, M. Pecht, C. H. White, R. C. Sohaney, and E.
Rhem, “Physics-of-failure approach for fan PHM in electronics
applications,” Proceedings of the IEEE Prognostics and System Health
Management Conference 2010, 2010, pp. 1-6.
 H. Oh, T. Shibutani, and M. Pecht, “Precursor monitoring approach for
reliability assessment of cooling fans,” Journal of Intelligent
Manufacturing, 2009, DOI 10.1007/s10845-009-0342-2.
 J. Antoni, and R.B. Randall, “Differential diagnosis of gear and bearing
faults,” Journal of Vibration and Acoustics, Vol.124, No.2, 2002, pp.
 D. Wang, Q. Miao, X. Fan, and H.Z. Huang, “Rolling element bearing
fault detection using an improved combination of Hilbert and Wavelet
transforms.” Journal of Mechanical Science and Technology, Vol.23,
No.12, 2009, pp. 3292-3301.
 Y. Qin, S. Qin, and Y. Mao, “Research on iterated Hilbert transform and
its application in mechanical fault diagnosis,” Mechanical Systems and
Signal Processing, Vol.22, No.8, 2008, pp. 1967-1980.
 D. Wang, Q. Miao, and R. Kang, “Robust health evaluation of gearbox
subject to tooth failure with wavelet decomposition,” Journal of Sound
and Vibration, Vol.324, No.3-5, 2009, pp. 1141-1157.
 G. Niu, A. Widodo, J.D. Son, B.S. Yang, D.H. Hwang, and D.S. Kang,
“Decision-level fusion based on wavelet decomposition for induction
motor fault diagnosis using transient current signal,” Expert Systems
with Applications, Vol.35, No.3, 2008, pp. 918-928.
 S.G. Mallat, “A theory for multiresolution signal decomposition: the
wavelet representation,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, Vol.11, No.7, 1989, pp. 674-693.
 Q. Miao, D. Wang, and H.Z. Huang, “Identification of characteristic
components in frequency domain from signal singularities,” Review of
Scientific Instruments, Vol.81, No.3, 2010, 035113.