A feature reconstruction and SAE model based diagnosis method for multiple mixed faults

Abstract

Owing to the merits of automatic feature extraction and depth structure, intelligent fault diagnosis based on deep neural networks has become a great concern. However, the non-fault state monitoring data volume of actual industrial machinery is rich, whereas the fault state data volume is insufficient and weak. Furthermore, achieving multiple mixed-fault diagnoses using skewed data distributions is extremely difficult. A feature reconstruction and sparse auto-encoder model-based diagnosis method for multiple mixed faults is proposed in this study to bridge these gaps. Such a feature reconstruction algorithm is designed and employed to address the following issues: 1) expensive computing resulting from the long sequential features of vibration monitoring data and 2) the extraction problem caused by the submersion of scarce data features. Furthermore, an adaptive loss function was formulated, and a deep auto-encoder network was constructed to identify the health status and determine the fault level. Diagnoses of artificial and real faults verify the availability and superiority of the proposed scheme, demonstrating the adaptability and robustness of these hyperparameters.