Fault diagnosis with between mode similarity analysis reconstruction for multimode processes

Abstract In this paper, a new approach on between mode similarity analysis (BMSA) reconstruction is proposed. Compared with the traditional method, similarity between different modes, higher-order and independent statistics are considered to separate independent subspaces, which contain increased subspace, decreased subspace, unchanged subspace and residual subspace. And further, the fault amplitude and fault directions are accurately extracted from the independent components to realize fault reconstruction, thus fault feature is highlighted, and the diagnosis performance is improved. Fault diagnosis indices are developed based on BMSA reconstruction for various fault alarms. The proposed method is applied to penicillin fermentation process, and is compared to traditional multiple modeling method. Experiment results show that the proposed method can more accurately diagnose fault than traditional multiple modeling method.

[1]  B. Flury,et al.  Two generalizations of the common principal component model , 1987 .

[2]  Johan Trygg,et al.  K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space , 2008, BMC Bioinformatics.

[3]  Rongrong Sun,et al.  Fault isolation for multimode process , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[4]  Zhiqiang Ge,et al.  Two-level PLS model for quality prediction of multiphase batch processes , 2014 .

[5]  S. Joe Qin,et al.  Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures , 2013 .

[6]  Jin Cao,et al.  PCA-based fault diagnosis in the presence of control and dynamics , 2004 .

[7]  In-Beum Lee,et al.  Fault identification for process monitoring using kernel principal component analysis , 2005 .

[8]  Jianbin Qiu,et al.  Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults , 2017, Autom..

[9]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[10]  Zhiqiang Ge,et al.  Improved kernel PCA-based monitoring approach for nonlinear processes , 2009 .

[11]  Du-Ming Tsai,et al.  Defect Detection in Solar Modules Using ICA Basis Images , 2013, IEEE Transactions on Industrial Informatics.

[12]  Jie Zhang,et al.  BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS , 2007 .

[13]  Lei Liu,et al.  A multivariate statistical combination forecasting method for product quality evaluation , 2016, Inf. Sci..

[14]  Biao Huang,et al.  Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference , 2016, IEEE Transactions on Industrial Electronics.

[15]  Chun-Chin Hsu,et al.  An Adaptive Forecast-Based Chart for Non-Gaussian Processes Monitoring: With Application to Equipment Malfunctions Detection in a Thermal Power Plant , 2011, IEEE Transactions on Control Systems Technology.

[16]  Chonghun Han,et al.  Real-time monitoring for a process with multiple operating modes , 1998 .

[17]  Ma Yao,et al.  Fault detection of batch processes based on multivariate functional kernel principal component analysis , 2015 .

[18]  Chunhui Zhao,et al.  Between-Mode Quality Analysis Based Multimode Batch Process Quality Prediction , 2014 .

[19]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .

[20]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[21]  Zheng Liu,et al.  Learning-based super resolution using kernel partial least squares , 2011, Image Vis. Comput..

[22]  Zhang Yingwei,et al.  Modeling and monitoring of multimode process based on between-mode relative analysis , 2015, 2015 34th Chinese Control Conference (CCC).

[23]  Zhiqiang Ge,et al.  Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring , 2013 .

[24]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[25]  Huijun Gao,et al.  Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures , 2016, IEEE Transactions on Control Systems Technology.

[26]  A. J. Morris,et al.  Performance monitoring of a multi-product semi-batch process , 2001 .

[27]  Jianbin Qiu,et al.  State Estimation in Nonlinear System Using Sequential Evolutionary Filter , 2016, IEEE Transactions on Industrial Electronics.