在工业过程监测中，传统的过程监测方法无法提取过程的动态信息，且进行特征选择时没有突出在线故障特征.针对此问题，提出基于在线加权慢特征分析(OWSFA)的故障检测算法.采用慢特征分析(SFA)算法提取过程的本质动态特征；基于正常数据估计出特征阈值，根据松弛系数挑选出在线特征中超过阈值的嫌疑故障特征；引入权重系数，进一步构造基于在线加权的嫌疑故障特征统计量.将提出的OWSFA算法在数值系统和Tennessee Eastman过程进行仿真验证，证实了所提算法的故障检测效果优于主成分分析和SFA算法.OWSFA算法根据故障信息，在线构造加权统计量，加强了动态故障特征在监测模型中的表达.
In industrial process monitoring, traditional process monitoring methods fail to extract process dynamic information and the online fault-related information is not fully utilized in feature selection. To solve these problems, a fault detection method based on online weighted slow feature analysis (OWSFA) is proposed. First, the slow feature analysis(SFA)algorithm is utilized to extract the dynamic features. The threshold of slow feature is estimated based on benchmark data. The online features which exceed the threshold based on the slack factor are selected as the suspected fault features. Then, the monitoring statistics are built based on the weighted suspected fault features by introducing the weights. The proposed OWSFA algorithm is applied in a numerical system and the Tennessee Eastman process, which proves that the proposed method has superiorities compared with principal component analysis and the SFA method. According to the faulty information, the OWSFA algorithm generates the online weighted statistics to enhance the fault features in the monitoring model.
［1］JIANG Q C, YAN X F, HUANG B. Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference［J］. IEEE Transactions on Industrial Electronics, 2016, 63(1): 377-386.
［2］JIANG Q C, YAN X F. Parallel PCA-KPCA for nonlinear process monitoring［J］. Control Engineering Practice, 2018, 80: 17-25.
［3］GE Z Q, CHEN J. Plant-wide industrial process monitoring: A distributed modeling framework［J］. IEEE Transactions on Industrial Informatics, 2015, 12(1): 310-321.
［4］DONG Y N, QIN S J. A novel dynamic PCA algorithm for dynamic data modeling and process monitoring［J］. Journal of Process Control, 2018, 67: 1-11.
［5］HE Y C, ZHOU L, GE Z Q, et al. Dynamic mutual information similarity based transient process identification and fault detection［J］. The Canadian Journal of Chemical Engineering, 2018, 96(7): 1541-1558.
［6］ZHENG H Y, JIANG Q C, YAN X F. Quality-relevant dynamic process monitoring based on mutual information multiblock slow feature analysis［J］. Journal of Chemometrics, 2019, 33(4): 1-16.
［7］CHEN Z W, CAO Y, DING S X, et al. A distributed canonical correlation analysis-based fault detection method for plant-wide process monitoring［J］. IEEE Transactions on Industrial Informatics, 2019, 15(5): 2710-2720.
［8］GE Z Q. Process data analytics via probabilistic latent variable models: A tutorial review［J］. Industrial & Engineering Chemistry Research, 2018, 57(38): 12646-12661.
［9］GE Z Q, LIU Y. Analytic hierarchy process based fuzzy decision fusion system for model prioritization and process monitoring application［J］. IEEE Transactions on Industrial Informatics, 2019, 15(1): 357-365.
［10］JIANG Q C, GAO F R, YI H, et al. Multivariate statistical monitoring of key operation units of batch processes based on time-slice CCA［J］. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1368-1375.
［11］KU W F, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis［J］. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 179-196.
［12］HUANG J, YAN X F. Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference［J］. Chemometrics and Intelligent Laboratory Systems, 2015, 148: 115-127.
［13］SHANG C, YANG F, GAO X Q, et al. Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis［J］. AIChE Journal, 2015, 61(11): 3666-3682.
［14］SHANG C, YANG F, HUANG B, et al. Recursive slow feature analysis for adaptive monitoring of industrial processes［J］. IEEE Transactions on Industrial Electronics, 2018, 65(11): 8895-8905.
［15］GUO F H, SHANG C, HUANG B, et al. Monitoring of operating point and process dynamics via probabilistic slow feature analysis［J］. Chemometrics and Intelligent Laboratory Systems, 2016, 151: 115-125.
［16］ZHANG H Y, TIAN X M, DENG X G. Batch process monitoring based on multiway global preserving kernel slow feature analysis［J］. IEEE Access, 2017, 5: 2696-2710.
［17］汪嘉晨, 唐向红, 陆见光. 轴承故障诊断中特征选取技术［J］. 山东大学学报(工学版), 2019, 49(2): 80-87.
WANG Jiachen, TANG Xianghong, LU Jianguang. Research onfeature selection technology in bearing fault diagnosis［J］. Journal of Shandong University (Engineering Science), 2019, 49(2): 80-87.
［18］ZHOU P, HU X G, LI P P, et al. OFS-Density: A novel online streaming feature selection method［J］. Pattern Recognition, 2019, 86: 48-61.
［19］HUANG J, ERSOY O K, YAN X F. Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring［J］. Chemometrics and Intelligent Laboratory Systems, 2017, 169: 1-11.
［20］LEE J M, YOO C, LEE I B. Statistical process monitoring with independent component analysis［J］. Journal of Process Control, 2004, 14(5): 467-485.
［21］DOWNS J J, VOGEL E F. A plant-wide industrial process control problem［J］. Computers & Chemical Engineering, 1993, 17(3): 245-255.
［22］LYMAN P R, GEORGAKIS C. Plant-wide control of the Tennessee Eastman problem［J］. Computers & Chemical Engineering, 1995, 19(3): 321-331.
