Deep Learning/Machine Learning Techniques for Vibration Condition Monitoring of Major Facilities in Automobile Assembly/Painting Plants
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Abstract
We have been expanding the vibration monitoring system to prevent malfunctions of rotating equipment in Hyundai/Kia Motors' global factories. In this paper, a secondary analysis model was explored using an existing legacy program containing vibration trend and spectrum data. In the existing case, it goes through the steps of setting the alarm level - raising the vibration - reaching the alarm - alarm - recognizing - analyzing the vibration - drawing the result. An automation program was applied to reduce the steps to vibration increase - derivation of abnormal equipment - result analysis. In addition, we will also cover essential system components for the operation of additional development programs.
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Deep learning, Predictive Maintenance, Condition Monitoring
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