Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.

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

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Chunhui Zhao,et al.  Online Fault Diagnosis in Industrial Processes Using Multimodel Exponential Discriminant Analysis Algorithm , 2019, IEEE Transactions on Control Systems Technology.

[4]  Richard D. Braatz,et al.  Diagnosis of multiple and unknown faults using the causal map and multivariate statistics , 2015 .

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

[6]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[7]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[8]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[9]  Le Zhang,et al.  Visual Tracking With Convolutional Random Vector Functional Link Network , 2017, IEEE Transactions on Cybernetics.

[10]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[11]  C. L. Philip Chen,et al.  Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System , 2018, Remote. Sens..

[12]  Richard D. Braatz,et al.  A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis , 2015, Comput. Chem. Eng..

[13]  Chunhui Zhao,et al.  Sparse Exponential Discriminant Analysis and Its Application to Fault Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[14]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[15]  Shuang Feng,et al.  Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification , 2020, IEEE Transactions on Cybernetics.

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Chunhui Zhao,et al.  Linearity Evaluation and Variable Subset Partition Based Hierarchical Process Modeling and Monitoring , 2018, IEEE Transactions on Industrial Electronics.

[18]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[19]  Xin Gao,et al.  An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process , 2016, Neurocomputing.

[20]  Geoffrey E. Hinton,et al.  Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[21]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[22]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[23]  C. L. Philip Chen,et al.  A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Zhanpeng Zhang,et al.  A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..

[25]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[26]  Chunhui Zhao,et al.  Online Fault Diagnosis for Industrial Processes With Bayesian Network-Based Probabilistic Ensemble Learning Strategy , 2019, IEEE Transactions on Automation Science and Engineering.

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

[28]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[29]  Kaixiang Peng,et al.  A Quality-Based Nonlinear Fault Diagnosis Framework Focusing on Industrial Multimode Batch Processes , 2016, IEEE Transactions on Industrial Electronics.

[30]  Tie Qiu,et al.  Recurrent Broad Learning Systems for Time Series Prediction , 2020, IEEE Transactions on Cybernetics.

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

[32]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[33]  Yi Qin,et al.  The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind Turbines , 2019, IEEE Transactions on Industrial Electronics.

[34]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[36]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[37]  Hao Wu,et al.  Deep convolutional neural network model based chemical process fault diagnosis , 2018, Comput. Chem. Eng..

[38]  Faisal Khan,et al.  Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network , 2017 .

[39]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.