Deep transfer network for rotating machine fault analysis. (December 2019)

• Record Type: Journal Article
• Authors: Qian, Weiwei; Li, Shunming; Jiang, Xingxing
• Is Part Of: Pattern recognition. Volume 96(2019:Dec.)
• Publication Date: 2019-12
• Subjects: Intelligent fault diagnosis -- Rotating machine -- Deep transfer network -- Auto-balanced high-order KL divergence -- Smooth conditional distribution alignment -- Weighted joint domain adaptation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.106993
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Both the first and higher-order moments contribute in distribution alignments. Soft labels can also align conditional distributions effectively. Joint distribution alignments work better than marginal distribution alignments. Abstract: Machine learning-based intelligent fault diagnosis methods have gained extensive popularity and been widely investigated. However, in previous works, a major assumption accepted by default is that the training and testing datasets share the same distribution. Unfortunately, this assumption is mostly invalid in real-world applications for working condition variation of rotating machine can cause the distribution discrepancy between datasets easily, which results in performance degeneration of traditional diagnosis methods. Aiming at it, although some deep learning and transfer learning-based methods are proposed and validated effective recently, the dataset distribution alignments of them mainly focus on marginal distribution alignments, which are not powerful enough in some scenarios. Hence, a novel distribution discrepancy evaluating method called auto-balanced high-order Kullback–Leibler (AHKL) divergence is proposed, which can evaluate both the first and higher-order moment discrepancies and adapt the weights between them dimensionally and automatically. Meanwhile, smooth conditional distribution alignment (SCDA) is also developed, which performs excellently in aligning the conditional distributions through introducing soft labels instead of adopting widely-used pseudo labels. Furthermore, based on AHKL divergence and SCDA, weighted joint distribution alignment (WJDA) is developed for comprehensive joint distribution alignments. Finally, built on WJDA, we construct a novel deep transfer network (DTN) for rotating machine fault diagnosis with working condition variation. Extensive experimental evaluations through 18 transfer learning cases demonstrate its validity, and further comparisons with the state of the arts also validate its superiority.