Detecting Impulses in Mechanical Signals by Wavelets
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Detecting Impulses in Mechanical Signals by Wavelets W.-X. Yang Institute of Vibration Engineering, Northwestern Polytechnical University, Xi’an 710072, China Email: [email protected]
X.-M. Ren Institute of Vibration Engineering, Northwestern Polytechnical University, Xi’an 710072, China Email: [email protected] Received 21 February 2003; Revised 17 October 2003; Recommended for Publication by Marc Moonen The presence of periodical or nonperiodical impulses in vibration signals often indicates the occurrence of machine faults. This knowledge is applied to the fault diagnosis of such machines as engines, gearboxes, rolling element bearings, and so on. The development of an effective impulse detection technique is necessary and significant for evaluating the working condition of these machines, diagnosing their malfunctions, and keeping them running normally over prolong periods. With the aid of wavelet transforms, a wavelet-based envelope analysis method is proposed. In order to suppress any undesired information and highlight the features of interest, an improved soft threshold method has been designed so that the inspected signal is analyzed in a more exact way. Furthermore, an impulse detection technique is developed based on the aforementioned methods. The effectiveness of the proposed technique on the extraction of impulsive features of mechanical signals has been proved by both simulated and practical experiments. Keywords and phrases: wavelet transform, envelope analysis, fault diagnosis, rolling element bearing, soft threshold.
1.
INTRODUCTION
The extraction of impulsive features in vibration signals is vital for diagnosing such machines as engines, rolling element bearings, gearboxes, and so on. Researchers have developed many methods for fulfilling this purpose, for example, cepstrum analysis [1], signal demodulation procedure [2], transmission error measurement [3], higher-order time-frequency analysis [4], moving window procedure [5], and envelope analysis [6]. These techniques either use a time domain averaging procedure or adopt the classical timefrequency analyzing method that only provides constant time/frequency resolution analysis, so they are not powerful enough to deal with nonstationary signals. Recently, interest in the use of wavelet transforms (WTs) for processing nonstationary signals has grown [7]. Different from these convenient methods, the WTs provide a constant frequency-tobandwidth ratio analysis. In consequence, WTs possess fine time resolution in the high frequency ranges and excellent frequency resolution in low frequency region. This feature of WTs uniquely fits the requirement in failure diagnosis [8]. However, the impulse detection results generated by WTs are still not easy to be identified especially when the signal-tonoise ratio (SNR) of the detected signal is low. In view of this, a new wavelet-based impulse detection technique is studied in this paper.
2.
SUPERIORITY OF MORLET WAVELET ON IMPULSE DETECTION
The wavelet transform of a signal x(t) is defined as
WTx (a
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