An improved Fisher discriminant analysis algorithm based on Procrustes analysis for adaptive fault recognition

Aiming at the problem of continuous model updating for fault recognition in the time-varying process, a novel method called the Procrustes analysis–based Fisher discriminant analysis was proposed. First, each class of the training data was preprocessed by Procrustes analysis. Second, the new test data were aligned with each class of the training data by Procrustes analysis. Then, all the data were reduced to a low-dimensional space using Fisher discriminant analysis. Finally, the Euclidean distance between the test data and the training data after the Procrustes analysis was calculated, and the class recognition was achieved based on the discriminant principle of Fisher discriminant analysis. Two case studies show that the proposed Procrustes analysis–based Fisher discriminant analysis is superior to the traditional method based on Fisher discriminant analysis, and it can be used for fault recognition in a new and efficient way.

[1]  Edwin Lughofer,et al.  Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills , 2014, Inf. Sci..

[2]  Shaoping Xu,et al.  Fault Diagnosis in Chemical Processes Based on Class-Incremental FDA and PCA , 2019, IEEE Access.

[3]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .

[4]  Zhiqiang Ge,et al.  Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data , 2017, IEEE Transactions on Industrial Informatics.

[5]  Zhiqiang Ge,et al.  Analytic Hierarchy Process Based Fuzzy Decision Fusion System for Model Prioritization and Process Monitoring Application , 2019, IEEE Transactions on Industrial Informatics.

[6]  Fan Yang,et al.  Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes , 2018, IEEE Transactions on Industrial Electronics.

[7]  Yuanyuan Pan,et al.  Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections , 2014 .

[8]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[9]  Shen Jing-yi Huang Wei-ping Liang Jun Zhu Dong-yang.  Fault classification based on modified active learning and weighted SVM , 2017 .

[10]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[11]  T. McAvoy,et al.  Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .

[12]  Lamiaa M. Elshenawy,et al.  Recursive Fault Detection and Isolation Approaches of Time-Varying Processes , 2012 .

[13]  Sridhar Mahadevan,et al.  Manifold alignment using Procrustes analysis , 2008, ICML '08.

[14]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[15]  Xiaoli Meng,et al.  A single-line-to-ground fault diagnosis method in small-current-grounding system based on fuzzy-integral decision fusion technique , 2016, 2016 China International Conference on Electricity Distribution (CICED).

[16]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[17]  Shuai Li,et al.  Dynamic processes monitoring using recursive kernel principal component analysis , 2012 .

[18]  Dražen Slišković,et al.  Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models , 2013, Comput. Chem. Eng..

[19]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[20]  Zhiqiang Ge,et al.  Time Neighborhood Preserving Embedding Model and Its Application for Fault Detection , 2013 .

[21]  Chen Jing,et al.  SVM and PCA based fault classification approaches for complicated industrial process , 2015, Neurocomputing.

[22]  Zhiqiang Ge,et al.  Robust semi-supervised mixture probabilistic principal component regression model development and application to soft sensors , 2015 .

[23]  Marco E. Sanjuan,et al.  An improved weighted recursive PCA algorithm for adaptive fault detection , 2016 .

[24]  W. Cholewa,et al.  Fault Diagnosis: Models, Artificial Intelligence, Applications , 2004 .

[25]  Yi Hu,et al.  Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares , 2013 .

[26]  Zhiqiang Ge,et al.  Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes , 2017 .

[27]  Manabu Kano,et al.  Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem , 2002 .

[28]  Zhiqiang Ge,et al.  Fuzzy decision fusion system for fault classification with analytic hierarchy process approach , 2017 .

[29]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[30]  Zhi-huan Song,et al.  Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification , 2011 .

[31]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[32]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[33]  Hongbo Shi,et al.  Key principal components with recursive local outlier factor for multimode chemical process monitoring , 2016 .

[34]  Li Wang,et al.  Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes , 2015 .

[35]  Wang Pingping,et al.  Manifold alignment for dimension reduction and classification of multitemporal hyperspectral image , 2017 .

[36]  Xuhua Xia,et al.  Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data , 2008, BMC Bioinformatics.