Ensemble Targeted Stacked Denoising Autoencoders With Mutual Information Constraint for Rotating Machinery Fault Diagnosis - Zhao, Yinghao; Yang, Xu; Huang, Jian; Gao, Jingjing; Zhou, Xian; Zhang, Tao (IEEE transactions on industrial informatics, 2025)
Since the poor quality of signals and redundant features may reduce the accuracy of fault diagnosis of rotating machinery components, an ensemble targeted stacked denoising autoencoders (ETSDAE) method is proposed for machinery running under harsh environment. At first, a targeted denoising strategy is designed for ensemble models to remove noise from time-domain and frequency-domain data in the encode stage. The multidomian data makes up for the limitations of single-domain data while the denoising strategy improves the anti-noise ability of ETSDAE. On the other hand, two indexes based on mutual information are designed into cost function as a soft constraint to learn min-redundant deep features that have max-relevance to the class targets, thus it can get rid of the dependence on additional feature selection procedure. On this basis, integrated deep features are directly input to single-hidden layer feedforward neural network to realize fault diagnosis. Finally, the effectiveness of the proposed method are verified by rolling bearing test rig and industrial reciprocating pump. The results show that ETSDAE has excellent performance in fault diagnosis, especially in terms of anti-noise and feature learning.