Application of PCA method and FCM clustering to the fault diagnosis of excavator’s hydraulic system

摘要：

In order to improve reliability of the excavator’s hydraulic system, a fault diagnosis approach based upon principal component analysis (PCA) method and fuzzy cmeans (FCM) clustering was proposed. PCA is a powerful method for re-expressing multivariate data, which could effectively extract the correlation among process variables. With this approach, samples of target faults were used to develop PCA models in the first step and the largest eigenvalues extracted from the models were used as fault feature vector. Secondly, FCM clusering performed as fault classifier to determine the test fault. Simulated faults were introduced to validate the approach. Simulation results show that the proposed fault diagnosis approach could effectively applied to the excavator’s hydraulic system.

关键词： Hydraulic system excavator fault diagnosis principal component analysis (PCA) fuzzy c-means (FCM)clustering

作者: Xiangyu He Qinghua He

作者单位: College of Mechanical and Electrical Engineering Central South University Changsha, Hunan Province, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）