Fault diagnosis method based on improved discriminative dictionary learning

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (4) : 110-114.

WeiGang Wang1,2, ZhanSheng Liu1,

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Abstract

In recent years, sparse representation based classification method has been successfully employed in pattern recognition. The learning of dictionary and the training of classifier in this method are usually independent in existing approaches, so it reduces the identification accuracy. In this paper, we propose a novel fault diagnosis method based on improved dictionary learning model, which integrates discriminative sparse coding error and classification performance criterion with the reconstruction error. And this model is solved by K-singular value decomposition (K-SVD) algorithm that realizes the synchronization learning of dictionary and classifier. For our method, original signal is decomposed firstly by empirical mode decomposition, and the features of time domain and frequency domain are extracted from the decomposed intrinsic mode functions; then training samples are input into the improved model that is optimized by K-SVD; finally testing samples are identified by using learned dictionary and classification weights. Experimental results show that the algorithm not only can be applied in the small sample faults diagnosis, and the robustness and classification performance are significantly higher than other algorithms.

Key words

spare coding / dictionary learning / empirical mode decomposition / fault diagnosis

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WeiGang Wang1,2, ZhanSheng Liu1,. Fault diagnosis method based on improved discriminative dictionary learning[J]. Journal of Vibration and Shock, 2016, 35(4): 110-114

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