Fault detection and diagnosis for chillers using MSPCA-KECA

QI Yongsheng; ZHANG Haili; WANG Lin; GAO Xuejin; LU Chenxi

doi:10.11949/j.issn.0438-1157.20161239

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Fault detection and diagnosis for chillers using MSPCA-KECA

更新时间：2025-12-10

- Fault detection and diagnosis for chillers using MSPCA-KECA
- CIESC Journal Issue 4, Pages: 1499-1508(2017)
- 作者机构：
  
  1. 内蒙古工业大学电力学院,内蒙古,呼和浩特,010080
  2. 北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161239
  CLC： P277
- Online First：05 April 2017，
  
  Published：05 April 2017
- 稿件说明：
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QI Yongsheng, ZHANG Haili, WANG Lin, et al. Fault detection and diagnosis for chillers using MSPCA-KECA[J]. CIESC Journal, 2017, (4): 1499-1508.
DOI：

QI Yongsheng, ZHANG Haili, WANG Lin, et al. Fault detection and diagnosis for chillers using MSPCA-KECA[J]. CIESC Journal, 2017, (4): 1499-1508. DOI： 10.11949/j.issn.0438-1157.20161239.

摘要

针对冷水机组同类型不同等级故障的变量变化存在差异会造成误诊断的问题，提出一种基于多尺度主元分析-核熵成分分析（MSPCA-KECA）的故障诊断策略。MSPCA提取故障特征，其输出作为KECA分类器的输入，实现故障的实时监测与自动诊断。首先，改进的MSPCA算法通过将小波多尺度分析与主元分析相结合，筛选故障信息可能存在的尺度直接重构并采用PCA提取故障特征，获取不同类型故障之间差异的同时也保留了同类型但不同等级故障之间的相似性，提高故障诊断的可靠性。之后建立KECA非线性分类器并引入一种新的监测统计量——散度测度统计量，使降维后不同特征信息之间呈现显著的角度差异，易于分类。最后，采用支持向量数据描述（SVDD）算法确定新统计量的控制限，以克服无法获知统计量分布的问题。通过对冷水机组数据的仿真研究，验证了MSPCA-KECA方法的可行性及有效性。

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.

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