Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning

• Published in: IEEE sensors journal Vol. 21; no. 2; pp. 1727 - 1734
• Main Authors: Choudhary, Anurag, Goyal, Deepam, Letha, Shimi Sudha
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive recognition; Bearings; Bearings (machine parts); Classification; Classification results; Decision analysis; Decision trees; Defects; Discrete Wavelet Transform; Discrete wavelet transforms; Discriminant analysis; Electric Power Engineering; Elkraftteknik; Fault classification; Fault detection; Fault diagnosis; Fault tree analysis; Feature extraction; induction motor; Induction motors; Infrared imagery; Infrared imaging; Infrared thermal image; infrared thermography; Linear discriminant analysis; Machine learning; Mahalanobis distances; Nondestructive testing; Optimal feature sets; Performance evaluation; Principal component analysis; Principal components analysis; Rotary machines; Sensors; Shutdowns; Support vector machines; Thermography; Thermography (imaging); Two-dimensional discrete wavelet transform (2-D DWT); Wavelet transforms
• ISSN: 1530-437X; 1558-1748; 1558-1748
• DOI: 10.1109/JSEN.2020.3015868

Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure.