A novel deep learning diagnosis scheme for rotating machinery using adaptive local iterative filtering and ensemble hierarchical extreme learning machine

There are many hyper-parameters to be tuned in both machine learning model and deep learning model, and the structure of the deep learning model is large and complicated, making it extremely difficult for fault-feature extraction and classification. In order to address these two problems, a deep learning diagnosis method is proposed in this study by combining adaptive local iterative filtering and ensemble hierarchical extreme learning machine. Adaptive local iterative filtering and entropy feature matrix are used to extract fault features, and an ensemble hierarchical extreme learning machine with deep learning architecture is proposed for unsupervised feature learning and supervised classification. The proposed deep learning diagnosis scheme is tested on fault benchmark datasets under different severity conditions to verify its effectiveness and accuracy. The test results show that the proposed method performs better than traditional extreme learning machine and other variants.