Real-time Online Fault Diagnosis of Rolling Bearings Based on KNN Algorithm

Abstract

In order to realize the predictive maintenance of rolling bearings in industry, this paper proposes a real-time online fault diagnosis method for rolling bearings based on KNN algorithm. The method mainly includes two steps: fault diagnosis model training and real-time online fault diagnosis. Firstly, the vibration signal is preprocessed: data classification, data cleaning, data segmentation and feature parameter extraction, and then training and optimizing the fault diagnosis model. In the real-time online fault diagnosis part of the rolling bearing, the real-time online extraction of the characteristic parameters of the vibration signal is used to realize real-time online fault diagnosis through the fault diagnosis model. The results show that the fault diagnosis model based on KNN algorithm is better than the fault diagnosis model based on C4.5 algorithm and CART algorithm, which is more suitable for fault diagnosis of rolling bearings. Using this method to diagnose rolling bearings can help predictive maintenance before rolling bearing failures and reduce the economic losses caused by unplanned downtime of critical equipment.