Vibration-Related Fault Diagnosis in Cold Rolling Mill by Using EEMD and SVM

Abstract

Abstract:

Abstract： By analyzing the vibration process of cold rolling and using the structure model of the rolling system, a dynamic rolling force of the rolling vertical system was built, with the consideration of the influence of rolling vibration. A data-driven fault diagnosis was proposed based on industrial field data by using ensemble empirical mode decomposition (EEMD) and support vector machine (SVM), with the focus on the generalized fault, which were mostly caused by variations of process parameters under complex working conditions. According to the decoupling effect on measured rolling force data with the EEMD algorithm, the intrinsic mode function (IMF) component was defined as fault eigenvector and chosen as the input into the classifier of vector machine. Then, the vibration-related fault of cold rolling mills could be diagnosed by the distinction between the normal state and the fault state by SVM.

Key words: rolling mill, vertical vibration, fault diagnosis, support vector machine(SVM), signal , processing

CLC Number:

TP 273

YANG Xu1,PENG Kaixiang1,LUO Hao2,KRUEGER Minjia2,ZONG Dazi1,DING Steven X2. Vibration-Related Fault Diagnosis in Cold Rolling Mill by Using EEMD and SVM[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 751-756.

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