Qiang Ma1,2,3,4, Zepeng Li1,2, Kai Yang1,2,*, Shaofeng Zhang1,2, Zhuopei Wei1,2
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876
- 17 November 2025
Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >