Sensorless Robust Anomaly Detection of Roller Chain Systems Based on Motor Driver Data and Deep Weighted KNN

Condition monitoring (CM) is crucial for ensuring equipment reliability. Yet, its practical implementation encounters several challenges. On the one hand, these include hurdles in big data collection/storage, sensor selection/calibration/installation. On the other hand, developing effective CM techniques, especially for anomaly detection throughout the degradation process, requires extracting and selecting appropriate health indicators (HIs) and ensuring accurate anomaly detection. While bearings and gears have been intensively studied in the past, only little attention has been given to roller chain (RC) systems. Therefore, this study introduces an innovative approach leveraging a sensorless strategy and deep weighted K-nearest neighborhood (DWKNN) for detecting the abnormal status in RC systems. First, by utilizing readily available motor driver data, the need for expensive sensor selection/installation and data management is eliminated, enhancing cost-effectiveness and applicability across diverse industrial applications. Second, leveraging position information from the motor, the raw data are segmented, transformed into the frequency domain, and fused to provide a comprehensive understanding of the system’s behavior, thus improving CM performance. Subsequently, DWKNN entails intelligent indicator extraction and anomaly detection. Intelligent indicator extraction utilizes the deep sparse autoencoder (DSAE) method to dig hidden information in the acquired data and represent the degradation process. Meanwhile, anomaly detection is achieved through a weighted K-nearest neighborhood (WKNN), ensuring effectiveness and robustness. Through validation on multiple chains, the developed methodology demonstrates its effectiveness in addressing real-world CM challenges in industrial environments, offering a cost-effective and reliable solution compared with other methods.