Weighted domain adaptation networks for machinery fault diagnosis. (September 2021)

• Record Type: Journal Article
• Authors: Wei, Dongdong; Han, Te; Chu, Fulei; Zuo, Ming Jian
• Is Part Of: Mechanical systems and signal processing. Volume 158(2021)
• Publication Date: 2021-09
• Subjects: Machine learning -- Deep learning -- Transfer learning -- Domain adaptation -- Fault diagnosis -- Gearbox -- Vibration signals; 62P30 -- 68T10; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107744
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Multiple source domain adaptation for machinery fault diagnosis. Higher weights on sources closer towards an unlabeled target working condition. MMD values match with the physical gaps of domains. Negative transfer happens when MMD values are low. Abstract: Intelligent fault diagnosis of machines has received much attention in this big data era. Most reported models are constructed under the assumption that the training and testing data are from the same distribution. However, data distribution will shift due to working condition changes, posing challenges on the performance of intelligent models. This study considers the case that out of many known working conditions with labeled historical data, the model is to be used under another unlabelled target working condition. A multiple source domain adaptation method is proposed to learn fault-discriminative but working-condition-invariant features from raw vibration signals. Different known working conditions will be assigned with different weights, on the basis of their distributional similarities to the target working condition. Two case studies are carried out to validate the effectiveness of the proposed method, respectively on rotating speed changes and load level changes.