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Chen H, Cen J, Yang Z, Si W, Cheng H. Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network. ACS OMEGA 2022; 7:34389-34400. [PMID: 36188261 PMCID: PMC9521029 DOI: 10.1021/acsomega.2c04017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.
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Affiliation(s)
- Honghua Chen
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Jian Cen
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Zhuohong Yang
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Weiwei Si
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Hongchao Cheng
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
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2
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Bernal-de-Lázaro JM, Cruz-Corona C, Silva-Neto AJ, Llanes-Santiago O. Criteria for optimizing kernel methods in fault monitoring process: A survey. ISA TRANSACTIONS 2022; 127:259-272. [PMID: 34511263 DOI: 10.1016/j.isatra.2021.08.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 08/27/2021] [Accepted: 08/29/2021] [Indexed: 06/13/2023]
Abstract
Nowadays, how to select the kernel function and their parameters for ensuring high-performance indicators in fault diagnosis applications remains as two open research issues. This paper provides a comprehensive literature survey of kernel-preprocessing methods in condition monitoring tasks, with emphasis on the procedures for selecting their parameters. Accordingly, twenty kernel optimization criteria and sixteen kernel functions are analyzed. A kernel evaluation framework is further provided for helping in the selection and adjustment of kernel functions. The proposal is validated via a KPCA-based monitoring scheme and two well-known benchmark processes.
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Affiliation(s)
- José M Bernal-de-Lázaro
- Department of Automation and Computing, Universidad Tecnológica de La Habana "José Antonio Echeverría", CUJAE, Cuba
| | - Carlos Cruz-Corona
- Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Antônio J Silva-Neto
- Department of Mechanical Engineering, Universidade do Estado do Rio de Janeiro, IPRJ-UERJ, RJ, Brazil
| | - Orestes Llanes-Santiago
- Department of Automation and Computing, Universidad Tecnológica de La Habana "José Antonio Echeverría", CUJAE, Cuba.
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3
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Nonlinear Dynamic Process Monitoring Based on Two-Step Dynamic Local Kernel Principal Component Analysis. Processes (Basel) 2022. [DOI: 10.3390/pr10050925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static process monitoring. For a dynamic process, the expectation value of each variable changes over time, and hence, it cannot be replaced with a constant value. As such, the local data structure in the local kernel principal component analysis is wrong, which causes the model deviation problem. In this paper, we propose a new two-step dynamic local kernel principal component analysis, which extracts the static components in the process data and then analyzes them by local kernel principal component analysis. As such, the two-step dynamic local kernel principal component analysis can handle the nonlinearity and the dynamic features simultaneously.
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4
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Zhu J, Jiang M, Liu Z. Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:227. [PMID: 35009769 PMCID: PMC8749793 DOI: 10.3390/s22010227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/13/2021] [Accepted: 12/27/2021] [Indexed: 06/14/2023]
Abstract
This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.
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Affiliation(s)
- Jinlin Zhu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Muyun Jiang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Zhong Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;
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5
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Zhang G, Yang Q, Li G, Leng J, Yan M. A Satellite Incipient Fault Detection Method Based on Decomposed Kullback-Leibler Divergence. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1194. [PMID: 34573817 PMCID: PMC8472508 DOI: 10.3390/e23091194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/07/2021] [Indexed: 11/20/2022]
Abstract
Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback-Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.
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Affiliation(s)
- Ge Zhang
- Innovation Academy for Microsatellites of CAS, Shanghai 201203, China; (G.Z.); (Q.Y.); (J.L.); (M.Y.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiong Yang
- Innovation Academy for Microsatellites of CAS, Shanghai 201203, China; (G.Z.); (Q.Y.); (J.L.); (M.Y.)
| | - Guotong Li
- Innovation Academy for Microsatellites of CAS, Shanghai 201203, China; (G.Z.); (Q.Y.); (J.L.); (M.Y.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jiaxing Leng
- Innovation Academy for Microsatellites of CAS, Shanghai 201203, China; (G.Z.); (Q.Y.); (J.L.); (M.Y.)
| | - Mubiao Yan
- Innovation Academy for Microsatellites of CAS, Shanghai 201203, China; (G.Z.); (Q.Y.); (J.L.); (M.Y.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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7
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Xiao Y, Shi H, Wang B, Tao Y, Tan S, Song B. Adaptive Manifold Discriminative Distribution Alignment for Fault Diagnosis of Chemical Processes. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c00873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yutang Xiao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Hongbo Shi
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Boyu Wang
- Department of Computer Science and the Brain Mind Institute, University of Western Ontario, London, Ontario N6A 5B7, Canada
| | - Yang Tao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Shuai Tan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Bing Song
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Pani AK. Non-linear process monitoring using kernel principal component analysis: A review of the basic and modified techniques with industrial applications. BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2021. [DOI: 10.1007/s43153-021-00125-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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9
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Zhang C, Zheng X, Li Y. A Novel Fault Detection and Diagnosis Scheme Based on Independent Component Analysis-Statistical Characteristics: Application on the Tennessee Eastman Benchmark Process. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2021. [DOI: 10.1252/jcej.20we045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Cheng Zhang
- College of Science, Shenyang University of Chemical Technology
| | - Xiaofang Zheng
- College of Information Engineering, Shenyang University of Chemical Technology
| | - Yuan Li
- College of Information Engineering, Shenyang University of Chemical Technology
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10
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Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions. SENSORS 2021; 21:s21093226. [PMID: 34066598 PMCID: PMC8124480 DOI: 10.3390/s21093226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 04/29/2021] [Accepted: 05/02/2021] [Indexed: 12/03/2022]
Abstract
Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of the vibration signal, this article proposes a novel multilocation and multikernel scale learning framework based on deep convolution encoder (DCE) and bidirectional long short-term memory network (BiLSTM). The procedure of the proposed method using a cascade structure is developed in three stages. In the first stage, each parallel branch of the multifeature learning combines the skip connection and the DCE, and uses different size kernels. The multifeature learning network can automatically extract and fuse global and local features from different network depths and time scales of the raw vibration signal. In the second stage, the BiLSTM as the feature protection network is designed to employ the internal calculating data of the forward propagation and backward propagation at the same network propagation node. The feature protection network is used for further mining sensitive and complementary features. In the third stage of bearing diagnosis, the classifier identifies the fault types. Consequently, the proposed network scheme can perform well in generalization capability. The performance of the proposed method is verified on the two kinds of bearing datasets. The diagnostic results demonstrate that the proposed method can diagnose multiple fault types more accurately. Also, the method performs better in load and speed adaptation compared with other intelligent fault classification methods.
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11
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Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote. ELECTRONICS 2020. [DOI: 10.3390/electronics9111952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.
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12
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Cai P, Deng X. Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis. ISA TRANSACTIONS 2020; 105:210-220. [PMID: 32466844 DOI: 10.1016/j.isatra.2020.05.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 05/02/2023]
Abstract
In order to detect the incipient faults of nonlinear industrial processes effectively, this paper proposes an enhanced kernel principal component analysis (KPCA) method, called multi-block probability related KPCA method (DMPRKPCA). First of all, one probability related nonlinear statistical monitoring framework is constructed by combining KPCA with Kullback Leibler divergence (KLD), which measures the probability distribution changes caused by small shifts. Second, in view of the problem that the traditional KLD ignores the dynamic characteristic of process data, the dynamic KLD component is designed by applying the exponentially weighted moving average approach, which highlights the temporal data changes in the moving window. Third, considering that the holistic KLD component may submerge the local statistical changes, a multi-block modeling strategy is designed by dividing the whole KLD components into two sub-blocks corresponding to the mean and variance information, respectively. Case studies on one numerical system and the simulated chemical reactor demonstrate the superiority of the DMPRKPCA method over the conventional KPCA method.
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Affiliation(s)
- Peipei Cai
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.
| | - Xiaogang Deng
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.
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13
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Zhang H, Deng X, Zhang Y, Hou C, Li C. Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23832] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hanyuan Zhang
- School of Information and Electrical Engineering Shandong Jianzhu University Jinan China
| | - Xiaogang Deng
- College of Control Science and Engineering China University of Petroleum (East China) Qingdao China
| | - Yunchu Zhang
- School of Information and Electrical Engineering Shandong Jianzhu University Jinan China
| | - Chuanjing Hou
- School of Information and Electrical Engineering Shandong Jianzhu University Jinan China
| | - Chengdong Li
- School of Information and Electrical Engineering Shandong Jianzhu University Jinan China
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14
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Deng X, Zhang Z. Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description. SENSORS 2020; 20:s20164599. [PMID: 32824350 PMCID: PMC7472344 DOI: 10.3390/s20164599] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/10/2020] [Accepted: 08/13/2020] [Indexed: 11/17/2022]
Abstract
As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.
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