A novel supervised sparse feature extraction method and its application on rotating machine fault diagnosis

Abstract

Abstract Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to its efficiency in extracting discriminative features automatically. Sparse filtering (SF) is a simple and effective unsupervised feature extraction method aiming at optimizing the feature sparsity. However, the sparsity realized by SF is irregular and the features are unnecessarily discriminative for further classification. Hence, a simple and fast supervised feature extraction algorithm called supervised regularized sparse filtering (SRSF) is proposed, which explores a new way to optimize for sparsity. The supervised feature extraction is realized through fusing a novel parameterized sparse label matrix (PSLM) into the feature matrix to regular the sparsity. Meanwhile, a new objective function is developed together with it, and they work together to quicken the network convergence. In addition, SRSF can find out the specific frequencies from the learned weight matrix for each health condition innovatively, which connects the proposed method with traditional signal processing techniques. Furthermore, based on SRSF, a three-stage fault diagnosis network is developed. Experiments on a bearing case and a gearbox case are conducted separately to verify its effectiveness, and comparisons with the state of the art confirm its superiority.