High Accurate Smart Device for Real-Time Monitoring Electric Motor Conditions Based on IoT Technology and Artificial Intelligence
Main Article Content
Abstract
In the study, the authors developed a portable, non-invasive smart device for real-time monitoring of electric motors' working conditions based on IoT technology and artificial intelligence. The device collects vibration data of an electric motor, predicting anomalies using deep learning algorithms. Additionally, an application was built to track the real-time working conditions of the electric motors. Whenever an anomaly is detected, an alert message is immediately sent to the user via their smartphone. For anomaly prediction, two types of vibration data were utilized in the deep learning algorithms: one in the time domain and the other in the frequency domain, obtained through a discrete Fourier transform. Various feature extraction models in deep learning algorithms were employed to assess the accuracy of each model in predicting electric motor anomalies. Experiments were conducted on a grinding machine operating under various grinding conditions to evaluate the accuracy of the developed device in predicting anomalies. The results indicate that predicting the working condition of an electric motor using time-domain vibration data is more accurate than using frequency-domain data. It was found that the Serenest26d_32x4d and Reset 34 feature extraction models achieved better training results with time-domain vibration data compared to other models. The Reset 34 feature extraction model achieves the highest accuracy, with an F1-score of 1, when predicting the working condition of the grinding machine. The running time for all prediction models is under 0.02 seconds, demonstrating the capability for real-time monitoring of the electric motor's working condition using the developed device.
Keywords
Motor condition monitoring, non-invasive monitoring, smart device, deep learning algorithms, real-time monitoring
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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