A Novel Bayesian Framework With Enhanced Principal Component Analysis for Chemical Fault Diagnosis

Fault diagnosis is of great importance to enhance the reliability and security of the complex chemical processes. However, many fault diagnosis methods cannot extract correlation information among attributes in the feature extraction procedure, resulting in the degradation of diagnosis performance. In this article, a novel framework is proposed for fault diagnosis of the chemical processes. First, in the framework, an enhanced kernel principal component analysis (eKPCA) is proposed to obtain the key features of the fault based on Hotelling’s $T^{2}$ and squared prediction error (SPE) statistics. Second, to learn the correlation information of the features, an enhanced naive Bayesian model (eNBM) is constructed with a multivariate Gaussian kernel function. Third, the dragonfly algorithm (DA) is employed to find the optimal smoothing parameter to improve the performance of eNBM. The framework is applied to make a fault diagnosis of Tennessee Eastman (TE) process. The experimental results show that the proposed framework is more effective compared with the already existed traditional approaches, such as deep learning.

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

[2]  Guangquan Zhao,et al.  A novel approach for analog circuit fault diagnosis based on Deep Belief Network , 2018, Measurement.

[3]  Shutao Li,et al.  Deep Hyperspectral Image Sharpening , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Lingling Chen,et al.  Fault Diagnosis of High-Speed Train Bogie Based on Capsule Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[5]  Guang Wang,et al.  A Kernel Least Squares Based Approach for Nonlinear Quality-Related Fault Detection , 2017, IEEE Transactions on Industrial Electronics.

[6]  Bo Feng,et al.  Defect Classification With SVM and Wideband Excitation in Multilayer Aluminum Plates , 2020, IEEE Transactions on Instrumentation and Measurement.

[7]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[8]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[9]  Guang Yang,et al.  Fault Diagnosis of Complex Chemical Processes Based on Enhanced Naive Bayesian Method , 2020, IEEE Transactions on Instrumentation and Measurement.

[10]  Gui Yun Tian,et al.  Quantitative Evaluation of Crack Depths on Thin Aluminum Plate Using Eddy Current Pulse-Compression Thermography , 2020, IEEE Transactions on Industrial Informatics.

[11]  Bo Jin,et al.  Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.

[12]  Mohd Azlan Hussain,et al.  Fault diagnosis of Tennessee Eastman process with multi- scale PCA and ANFIS , 2013 .

[13]  Maciej Kusy,et al.  Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[16]  Carl E. Rasmussen,et al.  Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Sheng Guo,et al.  Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization , 2020, IEEE Transactions on Industrial Electronics.

[18]  Wei He,et al.  Generative Adversarial Networks With Comprehensive Wavelet Feature for Fault Diagnosis of Analog Circuits , 2020, IEEE Transactions on Instrumentation and Measurement.

[19]  G. Baudat,et al.  Feature vector selection and projection using kernels , 2003, Neurocomputing.

[20]  Jinsong Zhao,et al.  Fault Diagnosis of Chemical Processes Using Artificial Immune System with Vaccine Transplant , 2016 .

[21]  Reza Eslamloueyan,et al.  Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process , 2011, Appl. Soft Comput..

[22]  Jicong Fan,et al.  Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods , 2019, IEEE Transactions on Instrumentation and Measurement.

[23]  Richard D. Braatz,et al.  Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes , 2000 .

[24]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[25]  Licheng Jiao,et al.  Hyperspectral imagery classification with deep metric learning , 2019, Neurocomputing.

[26]  Vicente Alarcon-Aquino,et al.  An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks , 2019, IEEE Transactions on Instrumentation and Measurement.

[27]  Di Wu,et al.  LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[28]  Zlatica Marinkovic,et al.  Soft Sensors Based on Deep Neural Networks for Applications in Security and Safety , 2020, IEEE Transactions on Instrumentation and Measurement.

[29]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[30]  S. Joe Qin,et al.  A novel dynamic PCA algorithm for dynamic data modeling and process monitoring , 2017 .

[31]  Yaser Jararweh,et al.  Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks , 2019, IEEE Transactions on Industrial Informatics.