基于稀疏残差距离的多工况过程故障检测方法研究
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摘要
针对多工况过程,本文提出一种新的基于稀疏残差距离(Sparse residual distance, SRD)统计指标的故障检测方法.首先对正常的多工况标准化后数据直接进行稀疏分解,提取多个工况数据间相关关系,得到字典和对应的稀疏编码,以便构建全局检测模型,避免分工况且突出数据特征.然后计算正常多工况数据的近似值,构建稀疏残差空间,提出计算稀疏残差k近邻距离构建故障检测统计量,利用k近邻捕捉过程具有的非线性、多工况特征.最后通过数值案例和TE (Tennessee Eastman)生产过程进行仿真实验,验证了所提方法的有效性.
For multi-mode processes, a new fault detection method employing sparse residual distance(SRD) is proposed in this paper. Firstly, standardized normal multi-mode process data is directly used for sparse decomposition to extract correlation between multi-mode data, and a global detection model is established using the obtained dictionary and corresponding sparse coding, so as to avoid distinguishing modes and highlight data characteristics. Then calculating the approximate value of the normal multi-mode process data to construct the sparse residual space, in which sparse residual k-nearest neighbor distance is proposed, thus the nonlinear and multi-mode features of the process can be captured further by using k-nearest neighbor. Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman(TE) production process.
For multi-mode processes, a new fault detection method employing sparse residual distance(SRD) is proposed in this paper. Firstly, standardized normal multi-mode process data is directly used for sparse decomposition to extract correlation between multi-mode data, and a global detection model is established using the obtained dictionary and corresponding sparse coding, so as to avoid distinguishing modes and highlight data characteristics. Then calculating the approximate value of the normal multi-mode process data to construct the sparse residual space, in which sparse residual k-nearest neighbor distance is proposed, thus the nonlinear and multi-mode features of the process can be captured further by using k-nearest neighbor. Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman(TE) production process.
引文
1 Zhou Dong-Hua, Li Gang, Li Yuan. Data-driven Based Process Fault Detection and Diagnosis Technology. Beijing:Science Press, 2011.(周东华,李钢,李元.数据驱动的工业过程故障诊断技术.北京:科学出版社,2011.)
2 Lu Chun-Hong. Research on Data-driven Fault Detection and Diagnosis Techniques and Their Applications[Ph.D.dissertation], Jiangnan University, China, 2015(卢春红.基于数据驱动的故障检测与诊断技术及其应用研究[博士学位论文],江南大学,中国,2015)
3 Xu Xian-Zhen, Xie Lie, Wang Shu-Qing. Multi-mode process monitoring method based on PCA mixture model. CIESC Journal, 2011, 62(3):743-752(许仙珍,谢磊,王树青.基于PCA混合模型的多工况过程监控.化工学报,2011, 62(3):743-752)
4 Xiong Wei-Li, Guo Xiao-Gen. A process on-line monitoring method based on multi-mode identification. Control and Decision, 2018, 33(3):403-412(熊伟丽,郭校根.一种基于多工况识别的过程在线监测方法研究.控制与决策,2018, 33(3):403-412)
5 Ge Z Q, Song Z H. Bayesian inference and joint probability analysis for batch process monitoring. AIChE Journal,2013, 59(10):3702-3713
6 Zhao C H. Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring. AIChE Journal, 2014, 60(2):559-573
7 Sun Xian-Chang, Tian Xue-Min, Zhang Ni. Multi-mode process fault diagnosis method based on GMM. Computers and Applied Chemistry, 2014, 31(1):33-39(孙贤昌,田学民,张妮.一种基于GMM的多工况过程故障诊断方法.计算机与应用化学,2014, 31(1):33-39)
8 Guo Hong-Jie, Xu Chun-Ling, Shi Hong-Bo. Multimode process monitoring based on local neighborhood standardization strategy. Journal of Shanghai Jiao Tong University,2015, 49(6):868-875, 883(郭红杰,徐春玲,侍洪波.基于局部邻域标准化策略的多工况过程故障检测.上海交通大学学报,2015, 49(6):868-875, 883)
9 Wang Guo-Zhu, Liu Jian-Chang, Li Yuan, Shang LiangLiang. Fault diagnosis of industrial processes based on weighted k-nearest neighbor reconstruction analysis. Control Theory&Applications, 2015, 32(7):873-880(王国柱,刘建昌,李元,商亮亮.加权k最近邻重构分析的工业过程故障诊断.控制理论与应用,2015, 32(7):873-880)
10 Ge Z Q, Song Z H. Bagging support vector data description model for batch process monitoring. Journal of Process Control, 2013, 23(8):1090-1096
11 Zhong Na, Deng Xiao-Gang, Xu Ying. Fault detection method based on LECA for multimode process. CIESC Journal, 2015, 66(12):4929-4940(钟娜,邓晓刚,徐莹.基于LECA的多工况过程故障检测方法.化工学报,2015, 66(12):4929-4940)
12 Ning C, Chen M Y, Zhou D H. Sparse contribution plot for fault diagnosis of multimodal chemical processes. IFACPapersOnLine, 2015, 48(21):619-626
13 Lian Qiu-Sheng, Shi Bao-Shun, Chen Shu-Zhen. Research advances on dictionary learning models, algorithms and applications. Acta Automatica Sinica,2015, 41(2):240-260(练秋生,石保顺,陈书贞.字典学习模型、算法及其应用研究进展.自动化学报,2015, 41(2):240-260)
14 Tropp J A, Gilbert A C.. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666
15 Chen Chuan. Fault Detection Alcgorithm for Batch Process Based on Neareast Neighbor Rule[Master thesis], University of Electronic Science and Technology of China, China,2015(陈川.基于近邻规则的间歇过程故障检测算法研究[硕士学位论文],电子科技大学,中国,2015)
16 Ge Z Q, Song Z H. Multimode process monitoring based on Bayesian method. Journal of Chemometrics, 2009, 23(12):636-650
17 Downs J J, Vogel E F. A plant-wide industrial process control problem. Computer&Chemical Engineering, 1993,17(3):245-255
2 Lu Chun-Hong. Research on Data-driven Fault Detection and Diagnosis Techniques and Their Applications[Ph.D.dissertation], Jiangnan University, China, 2015(卢春红.基于数据驱动的故障检测与诊断技术及其应用研究[博士学位论文],江南大学,中国,2015)
3 Xu Xian-Zhen, Xie Lie, Wang Shu-Qing. Multi-mode process monitoring method based on PCA mixture model. CIESC Journal, 2011, 62(3):743-752(许仙珍,谢磊,王树青.基于PCA混合模型的多工况过程监控.化工学报,2011, 62(3):743-752)
4 Xiong Wei-Li, Guo Xiao-Gen. A process on-line monitoring method based on multi-mode identification. Control and Decision, 2018, 33(3):403-412(熊伟丽,郭校根.一种基于多工况识别的过程在线监测方法研究.控制与决策,2018, 33(3):403-412)
5 Ge Z Q, Song Z H. Bayesian inference and joint probability analysis for batch process monitoring. AIChE Journal,2013, 59(10):3702-3713
6 Zhao C H. Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring. AIChE Journal, 2014, 60(2):559-573
7 Sun Xian-Chang, Tian Xue-Min, Zhang Ni. Multi-mode process fault diagnosis method based on GMM. Computers and Applied Chemistry, 2014, 31(1):33-39(孙贤昌,田学民,张妮.一种基于GMM的多工况过程故障诊断方法.计算机与应用化学,2014, 31(1):33-39)
8 Guo Hong-Jie, Xu Chun-Ling, Shi Hong-Bo. Multimode process monitoring based on local neighborhood standardization strategy. Journal of Shanghai Jiao Tong University,2015, 49(6):868-875, 883(郭红杰,徐春玲,侍洪波.基于局部邻域标准化策略的多工况过程故障检测.上海交通大学学报,2015, 49(6):868-875, 883)
9 Wang Guo-Zhu, Liu Jian-Chang, Li Yuan, Shang LiangLiang. Fault diagnosis of industrial processes based on weighted k-nearest neighbor reconstruction analysis. Control Theory&Applications, 2015, 32(7):873-880(王国柱,刘建昌,李元,商亮亮.加权k最近邻重构分析的工业过程故障诊断.控制理论与应用,2015, 32(7):873-880)
10 Ge Z Q, Song Z H. Bagging support vector data description model for batch process monitoring. Journal of Process Control, 2013, 23(8):1090-1096
11 Zhong Na, Deng Xiao-Gang, Xu Ying. Fault detection method based on LECA for multimode process. CIESC Journal, 2015, 66(12):4929-4940(钟娜,邓晓刚,徐莹.基于LECA的多工况过程故障检测方法.化工学报,2015, 66(12):4929-4940)
12 Ning C, Chen M Y, Zhou D H. Sparse contribution plot for fault diagnosis of multimodal chemical processes. IFACPapersOnLine, 2015, 48(21):619-626
13 Lian Qiu-Sheng, Shi Bao-Shun, Chen Shu-Zhen. Research advances on dictionary learning models, algorithms and applications. Acta Automatica Sinica,2015, 41(2):240-260(练秋生,石保顺,陈书贞.字典学习模型、算法及其应用研究进展.自动化学报,2015, 41(2):240-260)
14 Tropp J A, Gilbert A C.. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666
15 Chen Chuan. Fault Detection Alcgorithm for Batch Process Based on Neareast Neighbor Rule[Master thesis], University of Electronic Science and Technology of China, China,2015(陈川.基于近邻规则的间歇过程故障检测算法研究[硕士学位论文],电子科技大学,中国,2015)
16 Ge Z Q, Song Z H. Multimode process monitoring based on Bayesian method. Journal of Chemometrics, 2009, 23(12):636-650
17 Downs J J, Vogel E F. A plant-wide industrial process control problem. Computer&Chemical Engineering, 1993,17(3):245-255