Amplitude‐frequency images‐based ConvNet: Applications of fault detection and diagnosis in chemical processes

Fault detection and diagnosis (FDD) have been major concerns in abnormal event management of chemical processes for decades. Frequency‐wise variations in chemical processes are not considered in most traditional methods, which affects the monitoring performance. An amplitude‐frequency images‐based convolutional neural network (ConvNet) is proposed for FDD in chemical processes. The fast Fourier transform (FFT) is first performed on data slice collected within a period to extract both amplitude‐wise dynamics and frequency‐wise variations, with the results in images. Then, the amplitude‐frequency images are fed into ConvNet for FDD. ConvNet is applied as a binary classifier, in which each classifier corresponds to only one fault. Thus, an expandable framework is provided to incorporate a new fault. The performance of the proposed amplitude‐frequency images‐based ConvNet in FDD is demonstrated in a numerical case and the Tennessee Eastman process.

[1]  Richard P. Wildes,et al.  What Do We Understand About Convolutional Networks? , 2018, ArXiv.

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

[3]  Chudong Tong,et al.  Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach , 2017 .

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

[5]  James F. Davis,et al.  A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment , 2016 .

[6]  Yupu Yang,et al.  Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule , 2018 .

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

[8]  Jian Zhang,et al.  Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network , 2018, IEEE Transactions on Industrial Electronics.

[9]  Sheng Chen,et al.  Noise-resistant joint diagonalization independent component analysis based process fault detection , 2015, Neurocomputing.

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

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  David J. Sandoz,et al.  The application of principal component analysis and kernel density estimation to enhance process monitoring , 2000 .

[13]  Cheng Zhang,et al.  Fault detection method based on principal component difference associated with DPCA , 2018, Journal of Chemometrics.

[14]  Chenglin Wen,et al.  Weighted time series fault diagnosis based on a stacked sparse autoencoder , 2017 .

[15]  Faisal Khan,et al.  Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model , 2019, Chemical Engineering Science.

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

[17]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[18]  Baoping Tang,et al.  Improved discrete Fourier transform algorithm for harmonic analysis of rotor system , 2016 .

[19]  Chenglin Wen,et al.  Representation learning based adaptive multimode process monitoring , 2018, Chemometrics and Intelligent Laboratory Systems.

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

[21]  Chao Yang,et al.  Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes , 2018 .

[22]  Junghui Chen,et al.  Flame Images for Oxygen Content Prediction of Combustion Systems Using DBN , 2017 .

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

[24]  Zhiqiang Ge,et al.  Nonlinear process monitoring based on linear subspace and Bayesian inference , 2010 .

[25]  Zhiqiang Ge,et al.  Semi-supervised Fisher discriminant analysis model for fault classification in industrial processes , 2014 .

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

[27]  Tony R. Martinez,et al.  The general inefficiency of batch training for gradient descent learning , 2003, Neural Networks.

[28]  Qi Xuan,et al.  Multiview Generative Adversarial Network and Its Application in Pearl Classification , 2019, IEEE Transactions on Industrial Electronics.

[29]  Jicong Fan,et al.  Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA , 2014 .

[30]  Xuejin Gao,et al.  Fault detection and diagnosis of chemical process using enhanced KECA , 2017 .

[31]  Yide Wang,et al.  Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter. , 2016, ISA transactions.

[32]  L. A. Rusinov,et al.  Online diagnostics of time‐varying nonlinear chemical processes using moving window kernel principal component analysis and Fisher discriminant analysis , 2017 .

[33]  Dong Shen,et al.  Preliminary-summation-based principal component analysis for non-Gaussian processes , 2015 .

[34]  Zhi-huan Song,et al.  Distributed PCA Model for Plant-Wide Process Monitoring , 2013 .

[35]  Qi Xuan,et al.  Evolving Convolutional Neural Network and Its Application in Fine-Grained Visual Categorization , 2018, IEEE Access.

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

[37]  Chang Ouk Kim,et al.  A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes , 2017, IEEE Transactions on Semiconductor Manufacturing.

[38]  Mani Maran Ratnam,et al.  Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT) , 2017 .

[39]  Majdi Mansouri,et al.  Online reduced kernel principal component analysis for process monitoring , 2018 .

[40]  Jicong Fan,et al.  Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis , 2014, Inf. Sci..

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

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

[43]  Mohamed Azlan Hussain,et al.  Fault diagnosis and classification framework using multi-scale classification based on kernel Fisher discriminant analysis for chemical process system , 2017, Appl. Soft Comput..