Induction Motor Eccentricity Fault Detection and Quantification Using Topological Data Analysis

• Published in: IEEE access 2024, Vol.12, p.37891-37902
• Main Authors: Wang, Bingnan, Lin, Chungwei, Inoue, Hiroshi, Kanemaru, Makoto
• Format: Article
• Language: English
• Subjects: Data analysis; Data mining; Eccentricity; Electric machines; Fault detection; Feature extraction; Induction motor drives; Induction motors; Machine learning; Motor stators; Motors; Rotors; Stator windings; Time domain analysis; topological data analysis; Topology; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3376249

In this paper, we propose a topological data analysis (TDA) method for the processing of induction motor stator current data, and apply it to the detection and quantification of eccentricity faults. Traditionally, physics-based models and involved signal processing techniques are required to identify and extract the subtle frequency components in current data related to a particular fault. We show that TDA offers an alternative way to extract fault related features, and effectively distinguish data from different fault conditions. We will introduce TDA method and the procedure of extracting topological features from time-domain data, and apply it to induction motor current data measured under different eccentricity fault conditions. We show that while the raw time-domain data are very challenging to distinguish, the extracted topological features from these data are distinct and highly associated with eccentricity fault level. With TDA processed data, we can effectively train machine learning models to predict fault levels with good accuracy, even for new data from eccentricity levels that are not seen in the training data. The proposed method is model-free, and only requires a small segment of time-domain data to make prediction. These advantages make it attractive for a wide range of data-driven fault detection applications.