Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms

• Published in: Information (Basel) Vol. 14; no. 6; p. 329
• Main Authors: Sobhi, Sayedabbas, Reshadi, MohammadHossein, Zarft, Nick, Terheide, Albert, Dick, Scott
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Algorithms; anomaly detection; Automation; Circuits; Condition monitoring; Data mining; Deep learning; Design; Electric motors; Failure; Fault detection; Fault diagnosis; Fourier transforms; Horsepower; Hybrid systems; Induction electric motors; Induction motors; inferential sensing; Machine learning; Machinery; Magnetic fields; Magneto-electric machines; Maintenance and repair; Preventive maintenance; Sensors; Wavelet transforms
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14060329

Electric induction motors are one of the most important and widely used classes of machines in modern industry. Large motors, which are commonly process-critical, will usually have built-in condition-monitoring systems to facilitate preventive maintenance and fault detection. Such capabilities are usually not cost-effective for small (under ten horsepower) motors, as they are inexpensive to replace. However, large industrial sites may use hundreds of these small motors, often to drive cooling fans or lubrication pumps for larger machines. Multiple small motors may further be assigned to a single electrical circuit, meaning a failure in one could damage other motors on that circuit. There is thus a need for condition monitoring of aggregations of small motors. We report on an ongoing project to develop a machine-learning-based solution for fault detection in multiple small electric motors. Shallow and deep learning approaches to this problem are investigated and compared, with a hybrid deep/shallow system ultimately being the most effective.