Abstract
Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.
Highlights
Bearings and gears are widely used in automobiles, machines, turbines, and mining equipments
We have presented a framework of sparse representation–based classification for machinery fault diagnosis
A structured dictionary with label is learned by structure Fisher discrimination dictionary learning (DL), whose sub-dictionaries have discrimination ability: smaller within class dissimilar and larger distance between classes
Summary
Bearings and gears are widely used in automobiles, machines, turbines, and mining equipments. Pre-emptive detection of bearings and gears failure is critical to the reliable operation of mechanical systems.[1] This can be achieved by making use of the information contained in vibration signals. The traditional basis representation algorithms such as fast Fourier transform (FFT), wavelet,[2] and variants of wavelet[3] are effective in dealing with vibration signals for fault diagnosis. The structural and discriminative information of vibration signals are not commonly used in the past.
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