Abstract
In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.
Highlights
Among the majority of rotating machines, fairly important and frequently encountered components are rolling bearings, and the operating efficiency of an entire machine or an entire system is directly affected by their operating state.erefore, the state detection and classification of rolling bearings have always been and will still be a research hotspot because of their low-cost maintenance and the reduction of unpredictable influences in some cases [1]
Inspired by the dictionary pair learning (DPL) and the traditional collaborative representation (CR) classification, we proposed an improved CR classification method to diagnose bearings in this paper. e main novelty of our work consists of two aspects: (1) the features are directly extracted from the input raw signals using the wavelet transform, and the feature matrix is trained by the DPL; and (2) in the fault diagnosis phase, the sparse and projection coefficients are combined to enhance the robustness of the CR classification and accuracy
Since there are some correlations among the atoms in C, more than one linear combination can be used to represent the corresponding x with the same residual. us, to find the appropriate linear combination that coincides with the actual situation, we introduce the sparsity of the coding coefficient vector into the CR representation to augment the dense representation to improve the performance of CRbased classification [31]
Summary
Among the majority of rotating machines, fairly important and frequently encountered components are rolling bearings, and the operating efficiency of an entire machine or an entire system is directly affected by their operating state. In the early stage of fault diagnosis, some statistics (e.g., the root mean square and kurtosis) were extensively used to extract the fault characteristics of the original signals in time domain These statistical parameters can be computed using fairly simple methods [5, 6], they can not completely represent the fault information features due to the existence of unknown periods or frequencies when analyzing vibration signals with complex faults, which may lead to low classification precision. E main novelty of our work consists of two aspects: (1) the features are directly extracted from the input raw signals using the wavelet transform, and the feature matrix is trained by the DPL; and (2) in the fault diagnosis phase, the sparse and projection coefficients are combined to enhance the robustness of the CR classification and accuracy.
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