의사-레이블기법을 이용한 기계학습기반 설비상태 모니터링과 진단

Abstract

This paper deals with a monitoring problem of the hot strip roughing mill that consists of lots of mechanical and electrical units. In fault diagnosis, various and complex units could be failed with unknown reasons and the main issue is to how to detect efficiently the abnormal or failed condition of the equipments. In this paper, pseudonormal and pseudo-abnormal data sets were defined and labeled to apply a classification analysis algorithm to unlabeled equipment vibration data. The Lagrangian perspective is used to compare the initial baseline pseudonormal dataset with the pseudo-abnormal dataset determined over time to monitor the classification evaluation scale for the status of the equipment. We use the support vector machine (SVM), K-NN, and discriminant analysis together to diagnose equipment fault and give alarm to maintain the facility properly applied classification algorithms and a procedure are proposed to generate alarms through test of correlation coefficient, cluster performance, and mean difference between clusters. A case study is done based on the experimental field gear box vibration data.