基于动态特性描述的变量加权型分散式故障检测方法

钟凯; 韩敏; 韩冰

doi:10.16383/j.aas.c180276

基于动态特性描述的变量加权型分散式故障检测方法

doi: 10.16383/j.aas.c180276

钟凯^1,,
韩敏^1,,
韩冰^2,

1.
大连理工大学电子信息与电气工程学部大连 116023
2.
上海船舶运输科学研究所航运技术与安全国家重点实验室上海 200135

基金项目: 国家自然科学基金(61773087)资助

详细信息

作者简介:
钟凯：大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为工业过程监控, 故障诊断. E-mail: kaizhong0402@ahu.edu.cn

韩敏：大连理工大学电子信息与电气工程学部教授. 主要研究方向为模式识别, 复杂系统建模与分析及时间序列预测. 本文通信作者. E-mail: minhan@dlut.edu.cn

韩冰：航运技术与安全国家重点实验室研究员. 主要研究方向为深海动力定位控制, 船舶动力装置的故障诊断和预测. E-mail: hanbing@sssri.com

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出版历程
- 收稿日期: 2018-05-03
- 录用日期: 2018-12-12
- 刊出日期: 2021-10-13

Dynamic Feature Characterization Based Variable-weighted Decentralized Method for Fault Detection

ZHONG Kai^1
,,
HAN Min^1
,,
HAN Bing^2
,

1.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023
2.
State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai 200135

Funds: Supported by National Natural Science Foundation of China(61773087)

More Information

Author Bio:
ZHONG Kai　Ph.D. candidate at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers industrial process monitoring and fault diagnosis

HAN Min　Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling and analysis of complex system, and time series prediction. Corresponding author of this paper

HAN Bing　Professor at the State Key Laboratory of Navigation and Safety Technology. His research interest covers deep sea dynamic positioning control, and fault diagnosis and prognostic of ship power plant

摘要

摘要: 现代工业生产过程往往具有复杂的动态特性: 不同测量变量间会存在不同的时序相关性, 且变量间的相互影响会反映在不同的采样时刻上. 现有的动态过程监测模型往往不能充分挖掘变量间的动态特性, 其故障检测效果也有待进一步提高. 在此背景下, 本文提出一种基于动态特性描述的变量加权型分散式故障检测方法. 利用最大相关最小冗余(Minimal redundancy maximal relevance, mRMR) 算法更准确地描述动态过程变量间的相关性关系, 并利用该相关性的值对原始增广矩阵进行加权处理, 且不同延迟变量对当前测量值的影响大小就通过权值来体现, 因此能更加全面地刻画该测量值的动态特性. 最后建立一种融合mRMR算法, 贝叶斯推理以及动态主成分分析(Dynamic principal componemt amalysis, DPCA)模型的新的分布式建模策略, 提高了模型的容错能力和泛化能力, 取得了更好的故障检测结果.
- 主成分分析 /
- 故障检测 /
- 最大相关最小冗余 /
- 加权型分散式方法
Abstract: Modern industrial process is often accompanied by complicated dynamic behaviours: different measured variables have different serial correlations, and the interactions among these variables are reflected in different sampling instants. The existing dynamic process monitoring models can not fully excavate the dynamic characteristics among variables, then the corresponding fault detection results need to be further improved. Against this background, this paper proposes the dynamic feature characterization based variable-weighted decentralized method for fault detection, which uses the minimal redundancy maximal relevance (mRMR) algorithm to get more accurate description of the relationship among different measured variables in dynamic process, then the augmented matrix can be weighted according to the mRMR values, and the influences of different delay variables on current measured one are reflected by the weights. Hence, the dynamic characteristics of the measured variable can be described more comprehensively. Finally, a new distributed modeling strategy combining the mRMR algorithm, Bayesian inference and dynamic principal component analysis (DPCA), which improves fault tolerance and generalization ability of the model, thus obtaining better fault detection results.
- Principal component analysis /
- fault detection /
- minimal redundancy maximal relevance (mRMR) /
- weighted decentralized method

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图 1 基于mRMR的动态特性描述

Fig. 1 mRMR-based dynamic feature characterization

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图 2 基于mRMR-WDPCA故障监测的流程图

Fig. 2 Flowchart of mRMR-WDPCA based fault detection

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图 3 TE过程的结构图

Fig. 3 Structure diagram of the TE process

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图 4 4种方法的故障平均漏报率

Fig. 4 Average missing alarm rates of the four methods

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图 5 故障10的过程监控结果

Fig. 5 The monitoring charts of Fault 10

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图 6 故障16的过程监控结果

Fig. 6 The monitoring charts of Fault 16

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表 1 TE过程的误报率(%)

Table 1 False alarm rates of TE process (%)

模型	$T^2_s$	${ BIC}_{T^2}/T^2/T^2_d$	${ BIC}_{Q}/Q/Q_r$
DPCA	—	0.63	3.24
DLV	1.00	3.02	3.24
MI-DPCA	—	0.21	1.98
mRMR-WDPCA	—	1.63	2.13

下载: 导出CSV

表 2 TE过程故障漏报率(%)和检测延迟数(个)

Table 2 Missing alarm rates (%) and detection delay (delayed samples) of TE process

故障编号	故障类型	DPCA		DLV		MI-DPCA		mRMR-WDPCA
故障编号	故障类型	$T^2/Q$	检测延迟数	$T^2_{s}/ T^2_{d}/Q_r$	检测延迟数	${BIC}_{T^2} /{BIC}_{Q}$	检测延迟数	${BIC}_{T^2} /{BIC}_{Q}$	检测延迟数
1	阶跃	0.13	0	0.00	0	0.13	0	0.25	0
2	阶跃	1.50	2	1.00	0	1.38	10	1.50	10
4	阶跃	0.00	0	0.00	0	0.00	0	0.00	0
5	阶跃	55.00	0	0.13	0	73.13	0	0.00	0
6	阶跃	0.00	0	0.00	0	0.00	0	0.00	0
7	阶跃	0.00	0	0.00	0	0.00	0	0.00	0
8	随机	2.63	1	6.38	10	2.50	13	1.75	12
10	随机	48.88	18	37.50	7	25.50	24	18.88	2
11	随机	6.00	3	19.00	3	4.63	3	13.50	3
12	随机	0.88	0	9.00	0	0.63	0	0.13	0
13	慢偏移	4.63	35	4.88	26	4.63	39	5.38	41
14	粘滞	0.00	0	0.00	0	0.00	0	0.00	0
16	未知	48.00	10	36.6	39	23.50	11	14.75	7
17	未知	2.25	16	5.13	16	2.13	0	3.38	0
18	未知	9.38	15	9.63	17	9.38	16	9.00	1
19	未知	33.38	0	37.00	10	37.63	1	65.002	2
20	未知	36.38	12	35.13	2	33.38	55	32.50	45
21	恒定故障	49.50	26	49.25	7	42.63	40	47.13	9

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