Deep Learning/Machine Learning Techniques for Vibration Condition Monitoring of Major Facilities in Automobile Assembly/Painting Plants

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Published Sep 4, 2023
Gun Sik Kim Deog Hyeon Kim Jin Woo Park Ju Heon Hwang

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.

Abstract 132 | PDF Downloads 112

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Keywords

Deep learning, Predictive Maintenance, Condition Monitoring

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Section
Special Session Papers