A key-factor denoising strategy for quasi periodic non-stationary incipient faults diagnosis. (30th June 2022)

• Record Type: Journal Article
• Authors: Yang, Jing; Xie, Guo; Yang, Yanxi
• Is Part Of: Measurement. Volume 197(2022)
• Publication Date: 2022-06-30
• Subjects: EEMD -- Key-factor denoising -- Sparse autoencoder -- Incipient fault -- Intelligent fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111304
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Key-factor denoising strategy is proposed to alleviate noise interference. Weight constraints are designed to reduce the feature learning blindness of SAE. Adaptive loss function is designed to improve the mining data ability of SAE. KF-ISAE based intelligent diagnosis method for incipient faults is proposed. Application results confirm the effectiveness and practicability of the method. Abstract: Currently, the analysis method based on monitoring data has become an effective means of machinery fault diagnosis, and the fault diagnosis with obvious data features has achieved fruitful results. However, the incipient fault signals of equipment not only show the characteristics of weak intensity, quasi periodic and non-stationary, but also are submerged in strong background noise, which often makes it difficult to extract effective information directly from the original signals. Therefore, in order to effectively solve the problem of incipient fault diagnosis, and considering the capability of sparse autoencoder (SAE) to extract features automatically, this paper proposes a key-factor denoising strategy and an improved SAE network, and then an improved SAE network with key-factor denoising strategy (KF-ISAE) based intelligent diagnosis method for quasi periodic non-stationary incipient faults is proposed. The main contributions of the proposed method are as follows. On the one hand, signal denoising that cannot be ignored in fault diagnosis is achieved by the developed incipient faults sensitivity based key-factor denoising strategy, and on the other hand, for SAE, the blindness of feature learning is handled by the formed weights constraints. In addition, the health condition identification and the fault severity level determination of machinery are completed by the improved SAE network designed in this paper. Finally, verification and comparative experiments show the effectiveness and practicability of the proposed method.