Bearing fault detection with application to PHM Data Challenge

Mechanical faults in the items of equipment can result in partial or total breakdown, destruction and even catastrophes. By implementation of an adequate fault detection system the risk of unexpected failures can be reduced. Traditionally, fault detection process is done by comparing the feature sets acquired in the faulty state with the ones acquired in the fault– free state. However, such historical data are rarely available. In such cases, the fault detection process is performed by examining whether a particular pre–modeled fault signature can be matched within the signals acquired from the monitored machine. In this paper we propose a solution to a problem of fault detection without any prior data, presented at PHM’09 Data Challenge. The solution is based on a two step algorithm. The first step, based on the spectral kurtosis method, is used to determine whether a particular experimental run is likely to contain a faulty element. In case of a positive decision, fault isolation procedure is applied as the second step. The fault isolation procedure was based on envelope analysis of band–pass filtered vibration signals. The band–pass filtering of the vibration signals was performed in the frequency band that maximizes the spectral kurtosis. The effectiveness of the proposed approach was evaluated for bearing fault detection, on the vibration data obtained from the PHM’09 Data Challenge.

[1]  Robert B. Randall,et al.  Differential diagnosis of spall vs. cracks in the gear tooth fillet region: Experimental validation , 2009 .

[2]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[3]  W. Staszewski.  WAVELET BASED COMPRESSION AND FEATURE SELECTION FOR VIBRATION ANALYSIS , 1998 .

[4]  F. Combet,et al.  Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .

[5]  Roger F. Dwyer,et al.  Detection of non-Gaussian signals by frequency domain Kurtosis estimation , 1983, ICASSP.

[6]  Wenyi Wang,et al.  EARLY DETECTION OF GEAR TOOTH CRACKING USING THE RESONANCE DEMODULATION TECHNIQUE , 2001 .

[7]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[8]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[9]  Walter Bartelmus,et al.  MATHEMATICAL MODELLING AND COMPUTER SIMULATIONS AS AN AID TO GEARBOX DIAGNOSTICS , 2001 .

[10]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults. Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults , 2008 .

[11]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[12]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[13]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[14]  Janko Petrovčič,et al.  A System for Automated Final Quality Assessment in the Manufacturing of Vacuum Cleaner Motors , 2008 .

[15]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[16]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .