Multi-layer Contribution Propagation Analysis for Fault Diagnosis

doi:10.1007/s11633-018-1142-y

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Multi-layer Contribution Propagation Analysis for Fault Diagnosis
	Ruo-Mu Tan 1; Yi Cao 2
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2019
卷号	16 期号:1 页码:40-51
摘要	The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multi-layer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multi-layer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multi-layer linear algorithms.
关键词	Process monitoring fault detection and diagnosis contribution plots feature extraction multivariate statistics.
DOI	10.1007/s11633-018-1142-y
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42320
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK 2.College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
推荐引用方式 GB/T 7714	Ruo-Mu Tan,Yi Cao. Multi-layer Contribution Propagation Analysis for Fault Diagnosis[J]. International Journal of Automation and Computing,2019,16(1):40-51.
APA	Ruo-Mu Tan,&Yi Cao.(2019).Multi-layer Contribution Propagation Analysis for Fault Diagnosis.International Journal of Automation and Computing,16(1),40-51.
MLA	Ruo-Mu Tan,et al."Multi-layer Contribution Propagation Analysis for Fault Diagnosis".International Journal of Automation and Computing 16.1(2019):40-51.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
IJAC-2018-03-062.pdf（1077KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载