An Electrical Signal Based Anomaly Detection Method for Motor Bearing Faults Under Varying Operating Conditions
Part Of
2025 IEEE Energy Conversion Conference Congress and Exposition, ECCE 2025
ISBN (of the container)
9798331541309
ISBN
9798331541309
Date Issued
2025
Author(s)
Abstract
This paper presents a novel motor bearing fault diagnosis method based on the artificial intelligent based convolutional conditional autoencoder (CCAE). Conventional fault diagnosis methods typically require the fault reflected harmonic signatures among different failures. The proposed CCAE only requires normal phase current signals. It significantly reduces the complexity of bearing fault diagnosis process. In this paper, a one-dimensional (1-D) CCAE based on normal current signals is proposed and trained to create a single-class model. By using this unsupervised classification, the proposed CCAE can detect the anomaly bearing fault. In addition, a Durenberger-style state observer is proposed to estimate the motor speed and load torque conditions and to combine the state observer with CCAE, the bearing fault can be identified under different load and speed conditions. Based on experimental results, it is shown that the proposed CCAE with state observer can effectively detect bearing anomaly for different faults. These faults might not be detected based on conventional spectrum analysis of fault harmonic signatures.
Event(s)
17th Annual IEEE Energy Conversion Conference Congress and Exposition, ECCE 2025, Philadelphia, 19 October 2025 - 23 October 2025
Subjects
anomaly detection
autoencoder (AE)
Motor current signature analysis (MCSA)
unsupervised learning
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
conference paper