基于状态空间主成分分析网络的故障检测方法

doi:10.11949/j.issn.0438-1157.20180025

化工学报 ›› 2018, Vol. 69 ›› Issue (8): 3528-3536.DOI: 10.11949/j.issn.0438-1157.20180025

基于状态空间主成分分析网络的故障检测方法

董顺^1,2, 李益国^1,2, 孙栓柱³, 刘西陲^1,2, 沈炯^1,2

1 东南大学能源与环境学院, 江苏南京 210096;
2 东南大学能源热转换及其过程测控教育部重点实验室, 江苏南京 210096;
3 江苏方天电力技术有限公司, 江苏南京 211102

收稿日期:2018-01-09 修回日期:2018-04-09 出版日期:2018-08-05 发布日期:2018-08-05
通讯作者: 李益国
基金资助:
国家自然科学基金项目（51476027）；江苏省自然科学基金项目（BK20141119）。

Fault detection method based on state space-PCANet

DONG Shun^1,2, LI Yiguo^1,2, SUN Shuanzhu³, LIU Xichui^1,2, SHEN Jiong^1,2

1 School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu, China;
2 Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, Jiangsu, China;
3 Jiangsu Frontier Electric Technology Company Limited, Nanjing 211102, Jiangsu, China

Received:2018-01-09 Revised:2018-04-09 Online:2018-08-05 Published:2018-08-05
Supported by:
supported by the National Natural Science Foundation of China (51476027) and the Natural Science Foundation of Jiangsu Province (BK20141119).

摘要/Abstract

摘要：

作为一种经典的方法，主成分分析（PCA）在多元统计过程监控领域得到了广泛的应用。然而，主成分分析及其各种改进方法仅从原始数据中提取了一层特征，缺乏对深层次特征的提取。计算机领域深度学习技术的发展表明了深层次的网络结构有利于数据特征的提取，因此，将主成分分析网络（PCANet）这种深度学习网络结构引入到故障诊断领域，与多元统计过程监控方法进行结合，以增强故障检测效果。在PCANet框架下，针对工业过程数据的动态特征，在网络结构中增加了状态空间模型作为动态层以解决动态性问题。此外，还以故障检测为目标重新设计了输出层。最后，通过在TE过程上的仿真测试验证了该方法用于故障检测的可行性和有效性。

关键词: 过程系统, 主元分析, 算法, 故障检测, 状态空间, 深度学习

Abstract:

As a classical algorithm for feature extraction, principal component analysis (PCA) has been widely used in multivariate statistical process monitoring. However, PCA and its various improved methods extracted from original data only one layer of features but no deep layer features. The development of deep learning technology in computer field indicates that deep network structure is beneficial to extraction of data features. Therefore, principal component analysis network (PCANet), a deep learning network structure, was introduced into fault detection and combined with multivariate statistical process monitoring method to enhance fault detection efficiency. Under framework of PCANet, state space model was added to network structure as dynamic layer to solve dynamic issue of industrial process data. In addition, the output layer was redesigned to use fault detection as target function. Finally, method feasibility and validity for fault detection were verified by simulated testing on the Tennessee Eastman (TE) process.

Key words: process systems, principal component analysis, algorithm, fault detection, state space, deep learning

中图分类号:

TP277

董顺, 李益国, 孙栓柱, 刘西陲, 沈炯. 基于状态空间主成分分析网络的故障检测方法[J]. 化工学报, 2018, 69(8): 3528-3536.

DONG Shun, LI Yiguo, SUN Shuanzhu, LIU Xichui, SHEN Jiong. Fault detection method based on state space-PCANet[J]. CIESC Journal, 2018, 69(8): 3528-3536.

参考文献 30

[1]	GE Z, SONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10):3543-3562.
[2]	SANG W C, LEE C, LEE J M, et al. Fault detection and identification of nonlinear processes based on kernel PCA[J]. Chemometrics & Intelligent Laboratory Systems, 2005, 75(1):55-67.
[3]	RUSSELL E L, CHIANG L H, BRAATZ R D. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis[J]. Chemometrics & Intelligent Laboratory Systems, 2000, 51(1):81-93.
[4]	AJAMI A, DANESHVAR M. Data driven approach for fault detection and diagnosis of turbine in thermal power plant using independent component analysis (ICA)[J]. International Journal of Electrical Power & Energy Systems, 2012, 43(1):728-735.
[5]	任浩, 屈剑锋, 柴毅, 等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8):1345-1358. REN H, QU J F, CHAI Y, et al. Deep learning for fault diagnosis:the state of the art and challenge[J]. Control & Decision, 2017, 32(8):1345-1358.
[6]	TAMILSELVAN P, WANG P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115(7):124-135.
[7]	CHEN Z Q, LI C, SANCHEZ R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015, 2015(2):1-10.
[8]	JANSSENS O, SLAVKOVIKJ V, VERVISCH B, et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound & Vibration, 2016, 377(1):331-345.
[9]	CHAN T H, JIA K, GAO S, et al. PCANet:a simple deep learning baseline for image classification?[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2015, 24(12):5017-5031.
[10]	LIONG V E, LU J, WANG G. Face recognition using deep PCA[C]//Information, Communications and Signal Processing (ICICS) 20139th International Conference on IEEE. 2013:1-5.
[11]	WEN Q, GE Z, SONG Z. Data-based linear Gaussian state-space model for dynamic process monitoring[J]. AIChE Journal, 2012, 58(12):3763-3776.
[12]	ZHOU L, LI G, SONG Z, et al. Autoregressive dynamic latent variable models for process monitoring[J]. IEEE Transactions on Control Systems Technology, 2017, 25(1):366-373.
[13]	LI X, DUAN F, SATTAR T, et al. Canonical variable analysis for fault detection, system identification and performance estimation[C]//International Conference Design and Modeling of Mechanical Systems. Springer, Cham, 2017:247-257.
[14]	SAMUEL R T, CAO Y. Kernel canonical variate analysis for nonlinear dynamic process monitoring[J]. IFAC-PapersOnLine, 2015, 48(8):605-610.
[15]	SHANG J, CHEN M, JI H, et al. Recursive transformed component statistical analysis for incipient fault detection[J]. Automatica, 2017, 80(1):313-327.
[16]	YIN S, DING S X, HAGHANI A, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of Process Control, 2012, 22(9):1567-1581.
[17]	YIN S, DING S X, XIE X, et al. A review on basic data-driven approaches for industrial process monitoring[J]. IEEE Transactions on Industrial Electronics, 2014, 61(11):6418-6428.
[18]	GE Z. Multivariate Statistical Process Control:Process Monitoring Methods and Applications[M]. SONG Z. New York:Springer Science & Business Media, 2012:18-20
[19]	BAKDI A, KOUADRI A. A new adaptive PCA based thresholding scheme for fault detection in complex systems[J]. Chemometrics & Intelligent Laboratory Systems, 2017, 162(1):83-93.
[20]	童楚东, 蓝艇, 史旭华. 基于互信息的分散式动态PCA故障检测方法[J]. 化工学报, 2016, 67(10):4317-4323. TONG C D, LAN T, SHI X H. Fault detection by decentralized dynamic PCA algorithm on mutual information[J]. CIESC Journal, 2016, 67(10):4317-4323.
[21]	江伟, 王振雷, 王昕. 基于混合分块DMICA-PCA的全流程过程监控方法[J]. 化工学报, 2017, 68(2):759-766. JIANG W, WANG Z L, WANG X. Plant-wide process monitoring based on mixed multiblock DMICA-PCA[J]. CIESC Journal, 2017, 68(2):759-766.
[22]	张妮, 田学民, 蔡连芳. 基于RISOMAP的非线性过程故障检测方法[J]. 化工学报, 2013, 64(6):2125-2130. ZHANG N, TIAN X M, CAI L F. Non-linear process fault detection method based on RISOMAP[J]. CIESC Journal, 2013, 64(6):2125-2130.
[23]	AYECH N, CHAKOUR C, HARKAT M F. New adaptive moving window PCA for process monitoring[J]. IFAC Proceedings Volumes, 2012, 45(20):606-611.
[24]	CHIANG L H. Fault Detection and Diagnosis in Industrial Systems[M]. RUSSELL E L, BRAATZ R D. London:Springer, 2001:103-109.
[25]	RUSSELL E L. Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes[M]. CHIANG L H, BRAATZ R D. New York:Springer Science & Business Media, 2012:84-90
[26]	DING S X. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems[M]. London:Springer, 2014:23-30.
[27]	CHEN Z. Data-driven Fault Detection for Industrial Processes[M]. Duisburg:Springer Fachmedien Wiesbaden, 2017:31-33.
[28]	薄翠梅, 韩晓春, 易辉, 等. 基于聚类选择k近邻的LLE算法及故障检测[J]. 化工学报, 2016, 67(3):925-930. BO C M, HAN X C, YI H, et al. Neighborhood selection of LLE based on cluster for fault detection[J]. CIESC Journal, 2016, 67(3):925-930.
[29]	ZHANG Y. Fault detection and diagnosis of processes using nonlinear improved kernel independent component analysis (KICA) and support vector machine (SVM)[J]. Industrial & Engineering Chemistry Research, 2008, 47(18):6961-6971.
[30]	BERNAL-DE-LÁZARO J M, LLANES-SANTIAGO O, PRIETO-MORENO A, et al. Enhanced dynamic approach to improve the detection of small-magnitude faults[J]. Chemical Engineering Science, 2016, 146(1):166-179.

基于状态空间主成分分析网络的故障检测方法

Fault detection method based on state space-PCANet

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 30

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902.
[2]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[3]	温凯杰, 郭力, 夏诏杰, 陈建华. 一种耦合CFD与深度学习的气固快速模拟方法[J]. 化工学报, 2023, 74(9): 3775-3785.
[4]	张逸豪, 王振雷. 基于最大信息系数的分组支持向量数据描述故障检测[J]. 化工学报, 2023, 74(9): 3865-3878.
[5]	尹刚, 李伊惠, 何飞, 曹文琦, 王民, 颜非亚, 向禹, 卢剑, 罗斌, 卢润廷. 基于KPCA和SVM的铝电解槽漏槽事故预警方法[J]. 化工学报, 2023, 74(8): 3419-3428.
[6]	郑玉圆, 葛志伟, 韩翔宇, 王亮, 陈海生. 中高温钙基材料热化学储热的研究进展与展望[J]. 化工学报, 2023, 74(8): 3171-3192.
[7]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[8]	李贵贤, 曹阿波, 孟文亮, 王东亮, 杨勇, 周怀荣. 耦合固体氧化物电解槽的CO₂制甲醇过程设计与评价研究[J]. 化工学报, 2023, 74(7): 2999-3009.
[9]	王光, 单发顺, 钱禹丞, 焦建芳. 基于集成学习传递熵的化工过程微小故障检测方法[J]. 化工学报, 2023, 74(7): 2967-2978.
[10]	周继鹏, 何文军, 李涛. 异形催化剂上乙烯催化氧化失活动力学反应工程计算[J]. 化工学报, 2023, 74(6): 2416-2426.
[11]	邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537.
[12]	高学金, 姚玉卓, 韩华云, 齐咏生. 基于注意力动态卷积自编码器的发酵过程故障监测[J]. 化工学报, 2023, 74(6): 2503-2521.
[13]	郑书闽, 郭鹏程, 颜建国, 王帅, 李文博, 周淇. 微小通道内过冷流动沸腾阻力特性实验及预测研究[J]. 化工学报, 2023, 74(4): 1549-1560.
[14]	贠程, 王倩琳, 陈锋, 张鑫, 窦站, 颜廷俊. 基于社团结构的化工过程风险演化路径深度挖掘[J]. 化工学报, 2023, 74(4): 1639-1650.
[15]	宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638.