A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11
• Main Authors: Han, Te, Li, Yan-Fu, Qian, Min
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Deep learning; domain generalization; Domains; Employee welfare; Fault diagnosis; Feature extraction; Gearboxes; Hybrid systems; intelligent fault diagnosis; Machinery; Regularization; Rotating machinery; Task analysis; Training; vibration signal; Working conditions

The data-driven methods in machinery fault diagnosis have become increasingly popular in the past two decades. However, the wide applications of this scheme are generally compromised in real-world conditions because of the discrepancy between the training data and testing data. Although the recently...