The Method Based on Clustering for Unknown Failure Diagnosis of Rolling Bearings

Effective fault diagnosis is an important guarantee for the safe and stable operation of mechanical systems. Nowadays, most fault diagnosis methods are for known classes. However, the unknown fault usually occurs in the process of mechanical operation and may be wrongly divided into defined classes, which increases the risk of mechanical shutdown and the difficulty of maintenance. Therefore, to achieve high-accuracy diagnosis with unknown fault, a method based on transfer learning and deep transfer clustering (DTC) is proposed, named transfer clustering calculation center points (TCCP). TCCP completes the task of fault diagnosis with an unknown class by calculating the feature space distribution between the sample to be tested and the known samples and further improves the accuracy by matching the target distribution. In two open-source bearing vibration datasets and an acoustic emission (AE) dataset of the self-made bearing, five bearing diagnostic models are used to compare the unknown failure diagnosis. The average accuracy with unknown failure by TCCP is 97.08%, 89.78%, and 95.24%, respectively. The accuracy is 24.45%, 23.16%, and 10.08% higher than the average accuracy without TCCP. TCCP can get better diagnostic results under both vibration signals and audio signals. TCCP has predominant universality and generalization capabilities and provides an effective approach for the identification of unknown failure.