A novel deep output kernel learning method for bearing fault structural diagnosis

Abstract

Abstract In recent years, machine learning techniques have been proved a promising tool for bearing fault diagnosis. However, in the traditional machine learning-based diagnosis methods, the fault features tend to be relatively simple and couldn’t work well for different fault type once a specific feature extraction method is determined. Meanwhile, although deep learning techniques can adaptively extract more representative features from bearing fault data, they are generally computationally expensive with slow convergence speed. Even if some deep learning algorithms like Multi-Layer Extreme Learning Machine (ML-ELM) can get fast training speed by means of non-tuned training strategy, they are inevitably of randomness to some extents. To solve this problem, a new deep learning method called deep output kernel learning is proposed in this paper to conduct collaborative diagnosis of multiple bearing fault types. The initial motivation is using the structural domain information among multiple bearing fault types to improve the diagnosis model’s generalization ability and robustness. By adopting ML-ELM as baseline algorithm, this paper firstly utilizes autoencoder to adaptively extract deep features, and then uses them to construct an objective function with output kernel regularizer. Finally, after solving this optimization problem, an output kernel matrix is obtained, and with this matrix, the final diagnosis model is built by fusing the multiple outputs of fault classifier. Experimental results on CWRU and IMS bearing data sets show that, compared to one state-of-the-art signal analysis method and eight typical machine learning-based diagnosis methods including four shallow learning algorithms and four deep learning algorithms, the proposed method can effectively improve the accuracy of bearing fault diagnosis in an acceptable time. Moreover, the results from the Kruskal-Wallis Test also indicate the proposed method has good numerical stability.