Anomaly detection of unbalanced rotating shaft based on deep learning and thresholds
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초록
Motors are important machines used in various industries. They provide power to various pumps, air compressors, refrigeration plants, purifiers, and air-conditioning plants. However, the motor may not be optimally coupled with the driven machinery during the repair process, and the bearings may become damaged over time as the machine operates. These problems can cause an imbalance in the motor shaft, thus resulting in vibrations. Therefore, vibrations and abnormal indicators must be detected timely to ensure machine safety. A deep-learning model for anomaly detection based on publicly available bearing data was developed in this study. Bearing data from various experiments were plotted and their characteristics were analyzed. Additionally, the vibration amplitude graphs of certain sections were saved as images. The saved images were categorized into normal and abnormal, and then classified using a convolutional neural network (CNN) model. Evaluation of the model performance on the test set for the trained CNN model shows an accuracy of 0.95, which indicates that the model performs well in distinguishing between normal and abnormal vibration amplitudes. Furthermore, anomaly detection based on vibration-amplitude threshold values was performed.
Abstract
Motors are important machines used in various industries. They provide power to various pumps, air compressors, refrigeration plants, purifiers, and air-conditioning plants. However, the motor may not be optimally coupled with the driven machinery during the repair process, and the bearings may become damaged over time as the machine operates. These problems can cause an imbalance in the motor shaft, thus resulting in vibrations. Therefore, vibrations and abnormal indicators must be detected timely to ensure machine safety. A deep-learning model for anomaly detection based on publicly available bearing data was developed in this study. Bearing data from various experiments were plotted and their characteristics were analyzed. Additionally, the vibration amplitude graphs of certain sections were saved as images. The saved images were categorized into normal and abnormal, and then classified using a convolutional neural network (CNN) model. Evaluation of the model performance on the test set for the trained CNN model shows an accuracy of 0.95, which indicates that the model performs well in distinguishing between normal and abnormal vibration amplitudes. Furthermore, anomaly detection based on vibration-amplitude threshold values was performed.
참고문헌 (12)
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