An IoT-Based Monitoring System for Induction Motor Faults Utilizing Deep Learning Models

Irgat E., ÇİNAR E., Ünsal A., YAZICI A.

Journal of Vibration Engineering and Technologies, 2022 (SCI-Expanded) identifier identifier


© 2022, Krishtel eMaging Solutions Private Limited.Purpose: Electrical motors are among the most widely used equipment components across many industries. Therefore, monitoring electrical motors for the early detection of faults is essential for uninterrupted production and can save time and money for manufacturers. Bearing faults are one of the most frequently encountered fault types in induction motors. Although standard offline datasets and algorithm designs have been studied extensively in recent years, they lack a full-scale IoT-based monitoring system design for data collection and deployment in the field. In this paper, outer race, inner race, and ball faults of the bearings of a three-phase (1.5 kW, 6 poles) induction motor are studied using vibration signals and an IoT-based monitoring system. Methods: The vibration signals are collected on a new testbed where the Edge processing units can process the signals and also transfer the sensor data over an IoT-based system. Raw data signals, such as vibration, current, and torque, are preprocessed at the Edge. Features are transferred to a database with Message Queuing Telemetry Transport (MQTT) for long-term storage. Only the vibration signals are analyzed for the detection of bearing faults. The monitoring of vibration signals of the motor can be implemented online or offline locally at the Edge with the implemented IoT system. A Convolution Neural Network (CNN)-based deep learning algorithm is utilized to establish a data-driven condition monitoring AI model. The vibration signals are converted into spectrograms using Short Time Fourier Transform (STFT), and then the CNN model is trained. Results: The proposed IoT-based monitoring system solution, combined with the AI method, can successfully process the sensor signals from the motor. The model can distinguish a healthy bearing from three types of faulty bearings with an average accuracy rate of 95%. Conclusion: In this study, unlike the majority of the studies in the literature focusing on algorithm development with standard datasets for motor faults, a new induction motor condition monitoring system based on IoT technologies is designed and implemented. The system is scalable and can be deployed to monitor a fleet of motor equipment in manufacturing. Based on the needs, the AI model can run both at the Edge and at the server side with data analytics tools to monitor the condition of induction motor faults.