重庆大学学报：自然科学版2012，Vol.35Issue(4)：100-106,111,8.
数据驱动的在线MW-MSPCA故障诊断
Online MW-MSPCA data-driven fault diagnosis
摘要
Abstract
To track the non-stationary dynamics of the process which contains time-varying and multi-scale data, an online moving window multi-scale principal component analysis（MW-MSPCA） data-driven-based fault diagnosis method is proposed. In this data-driven diagnosis technique, wavelet threshold denoising is used to solve the conflict between the statistical model deviation and data correlation decreasing. The statistical models are updated by using moving window principal component analysis in various scales. The contribution of individual process variable to the process behavior change is illustrated in a 3-dimensional contribution chart. A quantitative evaluation mechanism is also given to evaluate the diagonising accuracy. The numerical experimental results for 6135D diesel demonstrate that the proposed method can diagnose sensor fault better in terms of false rejection, false alarm and diagnosing accuracy for fault diagnosis upon comparing with conventional multi-scale principal component analysis （MSPCA） and adaptive multi-way principal component analysis（AMPCA） modeling.关键词
数据驱动/多尺度故障诊断/滑动窗口主元分析Key words
data-driven/multi-scale fault diagnosis/moving window principal component分类
信息技术与安全科学引用本文复制引用
胡友强,余嘉,李鹏华..数据驱动的在线MW-MSPCA故障诊断[J].重庆大学学报：自然科学版,2012,35(4):100-106,111,8.基金项目
国家自然科学基金资助项目 （）
中央高校基本科研业务资助 （）
重庆市攻关项目资助 （）
