化工学报2017，Vol.68Issue(4)：1499-1508,10.DOI:10.11949/j.issn.0438-1157.20161239
基于MSPCA-KECA的冷水机组故障监测及诊断
Fault detection and diagnosis for chillers using MSPCA-KECA
摘要
Abstract
There are differences among different levels of the same type of the fault, which may cause misdiagnose. A fault diagnosis strategy based on multi-scale principal component analysis and kernel entropy component analysis (MSPCA-KECA) is proposed. Taking the features extracted by MSPCA as the input of KECA classifier can be used for fault online detection as well as automatic identification. MSPCA combines wavelet multi-scale analysis with principal component analysis to select the scales which contain fault-related information, and then use PCA to extract the fault-related features, extracting the similarity among different levels of the same type of fault and the difference among different faults, which can improve the ability of fault diagnosis. The combination of KECA and Cauchy-Schwarz (CS) statistics extract and express the angular structure of different kinds of faults, which is good for fault classification. The control limit here is achieved by support vector data description (SVDD) for the unacquainted distribution of the statistics. Through the simulation of ASHRAR 1043-RP chiller data, the feasibility and effectiveness of the MSPCA-KECA method are verified.关键词
故障诊断/多尺度主元分析/核熵成分分析/冷水机组Key words
fault diagnosis/MSPCA/KECA/chillers分类
信息技术与安全科学引用本文复制引用
齐咏生,张海利,王林,高学金,陆晨曦..基于MSPCA-KECA的冷水机组故障监测及诊断[J].化工学报,2017,68(4):1499-1508,10.基金项目
国家自然科学基金项目(61364009,21466026,61640312) （61364009,21466026,61640312）
内蒙古自治区自然科学基金项目(2015MS0615). supported by the National Natural Science Foundation of China (61364009, 21466026, 61640312) and the Natural Science Foundation of Inner Mongolia (2015MS0615). （2015MS0615）
