|Peter Wai-tat Tse (MEPTSE@cityu.edu.hk)
|Posted: Fri Sep 28, 2001 12:39 pm
Subject: Preprint: Wavelet Analysis and Envelope Detection...
|#5 Preprint: Wavelet Analysis and Envelope Detection...
Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault
Diagnosis - Their Effectiveness and Flexibilities.
The components which often fail in a rolling element bearing are the
outer-race, the inner-race, the rollers, and the cage. Such failures
generate a series of impact vibrations in short time intervals, which occur
at Bearing Characteristic Frequencies (BCF). Since BCF contain very little
energy, and are usually overwhelmed by noise and higher levels of
macro-structural vibrations, they are difficult to find in their frequency
spectra when using the common technique of Fast Fourier Transforms (FFT).
Therefore, Envelope Detection (ED) is always used with FFT to identify
faults occurring at the BCF. However, the computation of ED is complicated,
and requires expensive equipment and experienced operators to process.
This, coupled with the incapacity of FFT to detect non-stationary signals,
makes wavelet analysis a popular alternative for machine fault diagnosis.
Wavelet analysis provides multi-resolution in time-frequency distribution
for easier detection of abnormal vibration signals. From the results of
extensive experiments performed in a series of motor-pump driven systems,
the methods of wavelet analysis and FFT with ED are proven to be efficient
in detecting some types of bearing faults. Since wavelet analysis can
detect both periodic and non-periodic signals, it allows the machine
operator to more easily detect the remaining types of bearing faults which
are impossible by the method of FFT with ED. Hence, wavelet analysis is a
better fault diagnostic tool for the practice in maintenance.
Tse P., Peng Y.H. and Yam R., "Wavelet Analysis and Envelop Detection For
Rolling Element Bearing Fault Diagnosis - Their Effectiveness and
Flexibility", Transactions of the ASME: Journal of Vibration and Acoustics,
Vol. 123(3), July, 2001, pp.303-310.
An Effective and Portable Electronic Stethoscope for Fault Diagnosis by
Analyzing the Machine Running Sound Directly
Machine sound is a typical kind of non-stationary signal which carries
information regarding the operating conditions of the machine. In the past,
human ears had been used for detecting any abnormality occurring in a
machine as the method of hearing was simple and fast. However, the audible
range of human hearing is broadband and has a low signal-to-noise ratio,
making the method inefficient. The invention of Fast Fourier Transforms
(FFT) for vibration based machine fault diagnosis helps to increase the
efficiency in diagnosis. However, FFT fails to detect the transitory
characteristics of signals that are fault related. Moreover, the cost of a
FFT based analyzer is expensive and the accessibility of a wired transducer
is limited. Therefore, we are proposing the use of hearing method again -
not from a pair of human ears, but from an electronic stethoscope. We use
Continuous Wavelet Transforms (CWT) to remove noise from raw machine
running sound signals and detect non-stationary impulses generated from the
impacts of defective components. The method of Trajectory Parallel Measure
(TPM) is then used for fault detection and classification. From the results
of testing a number of similar type of gas engines, the concept of
electronic stethoscope, which uses CWT and TPM to diagnose fault by
analyzing the machine running sound directly, is found to be feasible and
Tse P., Xu G., Qu L., and Kumara S., "An Effective and Portable Electronic
Ear for Fault Diagnosis Using Machine Operating Sound Directly",
International Journal of Acoustics and Vibration, an affiliated Journal of
the Institute of Acoustics and Vibration (IIAV), Vol. 6(1), March 2001, pp.
Thank you for your earliest attention.
Ir. Dr. Peter W. Tse
Director, Smart Asset Management Research Laboratory
Department of Manufacturing Engineering & Engineering Management
City University of Hong Kong
Tat Chee Ave., Kowloon, Hong Kong
Tel:(852)27888431 Fax:(852)27888423 E-mail:email@example.com
Web Site: http://www.cityu.edu.hk/meem/samrl/ and