Abstract
The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.
Highlights
Sparse representation has been widely employed in image, video, and speech signal processing [1,2,3]
K-means singular value decomposition (K-SVD) dictionary learning method, the learned dictionary will demonstrate that multiple different basis functions belong to the same fault feature mode, which just correspond to the different impact positions, that is, the K-SVD algorithm does not consider the shift invariant characteristics in the periodic impact signals, while the shift invariant K-SVD algorithm (SI-KSVD) [16] can effectively solve this problem, in which each fault mode, namely basis function, can appear at any moment and a translation of the same basis function is conducted to represent the periodically recurring signal characteristics
A new fault diagnosis method for rolling bearing based on shift invariant sparse feature and optimized support vector machine (SVM) is proposed
Summary
Sparse representation has been widely employed in image, video, and speech signal processing [1,2,3]. Liu et al used shift-invariant sparse coding for feature extraction and achieved fault diagnosis of rolling bearings [18]. Ding put forward a fault diagnosis method based on convolution sparse coding and applied it to wheelset bearing in high-speed train [22]. Ding et al put forward a sparse feature extraction method using periodic convolution sparse representation and applied it to machinery fault detection [24]. Shift invariant K-SVD algorithm is conducted to generate sparse feature of the vibration signals of rolling bearing. A novel method using shift invariant sparse feature and optimized SVM is put forward to realize bearing fault diagnosis. Shift invariant K-SVD is adopted to learn an over-complete dictionary, whose training samples come from the vibration signals of rolling bearings at different running states.
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