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



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


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 133 | PDF Downloads 115



Deep learning, Predictive Maintenance, Condition Monitoring

Convolutional Neural Networks for Sentence Classification" by Yoon Kim (2014)

"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline" by Zhiguang Wang and Tim Oates (2017)

"Adversarial Autoencoders" by Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow (2016)

"Variational Autoencoder for Deep Learning of Images, Labels and Captions" by Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens and Lawrence Carin (2016)
Special Session Papers