Adaptive slow feature analysis - sparse autoencoder based fault detection for time-varying processes
•Slow feature analysis is used to extract the dynamic characteristics of the process and establish a "model update index" to realize the failure judgment of time-varying process models.•Sparse autoencoder is used to extract process features and establish "process monitoring index"...
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| Vydáno v: | Journal of the Taiwan Institute of Chemical Engineers Ročník 142; s. 104599 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2023
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| ISSN: | 1876-1070, 1876-1089 |
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| Abstract | •Slow feature analysis is used to extract the dynamic characteristics of the process and establish a "model update index" to realize the failure judgment of time-varying process models.•Sparse autoencoder is used to extract process features and establish "process monitoring index" to realize process monitoring.•Updated dataset is built using the normal neighbors of online samples. The incremental update strategy is proposed to establish adaptive model in order to describe the dynamic characteristics of time-varying process.
Fault detection and diagnosis technology is of great significance for practical industrial processes. Industrial process characteristics change with time due to various reasons such as changing working conditions. This will cause false alarm or missing alarm of process monitoring.
In this paper, an adaptive slow feature analysis (SFA) - sparse autoencoder (SAE) algorithm is proposed to establish an adaptive model for time-varying process monitoring. Model update index is built based on time-varying characteristics extracted using SFA model. Process monitoring index is built based on sparse characteristics extracted using SAE model. Through online adaptive update strategy, updated monitoring model is realized to adapt to the time-varying characteristics of the process.
The proposed algorithm has good performance on penicillin fermentation process data set and can realize the task of adaptive process monitoring.
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| AbstractList | •Slow feature analysis is used to extract the dynamic characteristics of the process and establish a "model update index" to realize the failure judgment of time-varying process models.•Sparse autoencoder is used to extract process features and establish "process monitoring index" to realize process monitoring.•Updated dataset is built using the normal neighbors of online samples. The incremental update strategy is proposed to establish adaptive model in order to describe the dynamic characteristics of time-varying process.
Fault detection and diagnosis technology is of great significance for practical industrial processes. Industrial process characteristics change with time due to various reasons such as changing working conditions. This will cause false alarm or missing alarm of process monitoring.
In this paper, an adaptive slow feature analysis (SFA) - sparse autoencoder (SAE) algorithm is proposed to establish an adaptive model for time-varying process monitoring. Model update index is built based on time-varying characteristics extracted using SFA model. Process monitoring index is built based on sparse characteristics extracted using SAE model. Through online adaptive update strategy, updated monitoring model is realized to adapt to the time-varying characteristics of the process.
The proposed algorithm has good performance on penicillin fermentation process data set and can realize the task of adaptive process monitoring.
[Display omitted] |
| ArticleNumber | 104599 |
| Author | Shi, Hongbo Tan, Shuai Zhou, Xinjin Song, Bing |
| Author_xml | – sequence: 1 givenname: Shuai surname: Tan fullname: Tan, Shuai email: tanshuai@ecust.edu.cn – sequence: 2 givenname: Xinjin surname: Zhou fullname: Zhou, Xinjin – sequence: 3 givenname: Hongbo surname: Shi fullname: Shi, Hongbo – sequence: 4 givenname: Bing surname: Song fullname: Song, Bing |
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| Keywords | Time-varying process Sparse autoencoder Slow feature analysis Adaptive process monitoring |
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| References | He, Li, Zhang, Xu, Zhu (bib0018) 2021; 70 Li, Yan (bib0005) 2020; 112 Peng, Zhang, Wang, Zhang (bib0017) 2022; 125 Lan, Tong, Shi, Luo (bib0006) 2020; 112 Li, Yue, Valle-Cervantes, Qin (bib0011) 2000; 10 Shang, Yang, Gao, Huang, Suykens, Huang (bib0019) 2015; 61 Wiskott, Sejnowski (bib0016) 2002; 14 Zhang, Yang, Chen, Li (bib0014) 2019; 6 Ge, Song (bib0013) 2008; 16 Shen, Song, Ge (bib0023) 2014; 2014 Chen, Liu (bib0015) 2018; 29 Parzen (bib0020) 1962; 33 Kang (bib0002) 2020; 112 Ge, Song, Gao (bib0003) 2013; 52 Song, Tan, Shi, Zhao (bib0001) 2020; 106 Wold (bib0010) 1994; 23 Cacciarelli, Kulahci (bib0021) 2022; 163 Yin, Ding, Xie, Luo (bib0007) 2014; 61 Dong, Zhang, Huang, Li, Peng (bib0009) 2015; 154 Jin, Fan, Chow (bib0008) 2019; 68 Qin (bib0012) 1998; 22 Qin (bib0004) 2012; 36 Zhang, Jiang, Zhan, Yang (bib0022) 2019; 75 Lan (10.1016/j.jtice.2022.104599_bib0006) 2020; 112 Dong (10.1016/j.jtice.2022.104599_bib0009) 2015; 154 Li (10.1016/j.jtice.2022.104599_bib0011) 2000; 10 Ge (10.1016/j.jtice.2022.104599_bib0003) 2013; 52 Li (10.1016/j.jtice.2022.104599_bib0005) 2020; 112 Song (10.1016/j.jtice.2022.104599_bib0001) 2020; 106 Shen (10.1016/j.jtice.2022.104599_bib0023) 2014; 2014 Yin (10.1016/j.jtice.2022.104599_bib0007) 2014; 61 He (10.1016/j.jtice.2022.104599_bib0018) 2021; 70 Cacciarelli (10.1016/j.jtice.2022.104599_bib0021) 2022; 163 Zhang (10.1016/j.jtice.2022.104599_bib0022) 2019; 75 Zhang (10.1016/j.jtice.2022.104599_bib0014) 2019; 6 Ge (10.1016/j.jtice.2022.104599_bib0013) 2008; 16 Wold (10.1016/j.jtice.2022.104599_bib0010) 1994; 23 Kang (10.1016/j.jtice.2022.104599_bib0002) 2020; 112 Wiskott (10.1016/j.jtice.2022.104599_bib0016) 2002; 14 Jin (10.1016/j.jtice.2022.104599_bib0008) 2019; 68 Shang (10.1016/j.jtice.2022.104599_bib0019) 2015; 61 Parzen (10.1016/j.jtice.2022.104599_bib0020) 1962; 33 Qin (10.1016/j.jtice.2022.104599_bib0012) 1998; 22 Chen (10.1016/j.jtice.2022.104599_bib0015) 2018; 29 Qin (10.1016/j.jtice.2022.104599_bib0004) 2012; 36 Peng (10.1016/j.jtice.2022.104599_bib0017) 2022; 125 |
| References_xml | – volume: 36 start-page: 220 year: 2012 end-page: 234 ident: bib0004 article-title: Survey on data-driven industrial process monitoring and diagnosis publication-title: Annu Rev Control – volume: 52 start-page: 3543 year: 2013 end-page: 3562 ident: bib0003 article-title: Review of recent research on data-based process monitoring publication-title: Ind Eng Chem Res – volume: 61 start-page: 181 year: 2015 ident: bib0019 article-title: Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis publication-title: AIChE J – volume: 33 start-page: 1065 year: 1962 end-page: 1076 ident: bib0020 article-title: On estimation of a probability density function and mode publication-title: Ann Math Stat – volume: 125 start-page: 371 year: 2022 end-page: 383 ident: bib0017 article-title: Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients publication-title: ISA Trans – volume: 70 start-page: 1 year: 2021 end-page: 8 ident: bib0018 article-title: Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder publication-title: IEEE Trans Instrum Meas – volume: 154 start-page: 77 year: 2015 end-page: 85 ident: bib0009 article-title: Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process publication-title: Neurocomputing – volume: 68 start-page: 3128 year: 2019 end-page: 36136 ident: bib0008 article-title: Fault detection for rolling-element bearings using multivariate statistical process control methods publication-title: IEEE Trans Instrum Meas – volume: 14 start-page: 715 year: 2002 end-page: 770 ident: bib0016 article-title: Slow feature analysis: unsupervised learning of invariances publication-title: Neural Comput – volume: 112 start-page: 137 year: 2020 end-page: 151 ident: bib0002 article-title: Visualization analysis for fault diagnosis in chemical processes using recurrent neural networks - ScienceDirect publication-title: J Taiwan Inst Chem Eng – volume: 75 start-page: 136 year: 2019 end-page: 155 ident: bib0022 article-title: Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring publication-title: J Process Control – volume: 2014 start-page: 1957 year: 2014 end-page: 1962 ident: bib0023 article-title: JITL based local monitoring method for transitions of multiphase batch processes publication-title: Proceedings of the American control conference – volume: 22 start-page: 503 year: 1998 end-page: 514 ident: bib0012 article-title: Recursive PLS algorithms for adaptive data modeling publication-title: ComputChemEng – volume: 106 start-page: 1 year: 2020 end-page: 8 ident: bib0001 article-title: Fault detection and diagnosis via standardized k nearest neighbor for multimode process - ScienceDirect publication-title: J Taiwan Inst Chem Eng – volume: 112 start-page: 322 year: 2020 end-page: 329 ident: bib0005 article-title: Process monitoring using principal component analysis and stacked autoencoder for linear and nonlinear coexisting industrial processes publication-title: J Taiwan Inst Chem Eng – volume: 29 start-page: 10 year: 2018 end-page: 24 ident: bib0015 article-title: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture publication-title: IEEE Trans Neural Netw Learn Syst – volume: 163 year: 2022 ident: bib0021 article-title: A novel fault detection and diagnosis approach based on orthogonal autoencoders publication-title: Comput Chem Eng – volume: 16 start-page: 1427 year: 2008 end-page: 1437 ident: bib0013 article-title: Online monitoring of nonlinear multiple mode processes based on adaptive local model approach publication-title: Control Eng Pract – volume: 61 start-page: 6418 year: 2014 end-page: 6428 ident: bib0007 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron – volume: 10 start-page: 471 year: 2000 end-page: 486 ident: bib0011 article-title: Recursive PCA for adaptive process monitoring publication-title: J Process Control – volume: 112 start-page: 78 year: 2020 end-page: 86 ident: bib0006 article-title: Dynamic statistical process monitoring based on generalized canonical variate analysis publication-title: J Taiwan Inst Chem Eng – volume: 23 start-page: 149 year: 1994 end-page: 161 ident: bib0010 article-title: Exponentially weighted moving principal components analysis and projections to latent structures publication-title: Chemom Intell Lab Syst – volume: 6 start-page: 248 year: 2019 end-page: 257 ident: bib0014 article-title: Incremental deep computation model for wireless big data feature learning publication-title: IEEE Trans Big Data – volume: 61 start-page: 6418 year: 2014 ident: 10.1016/j.jtice.2022.104599_bib0007 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2014.2301773 – volume: 163 year: 2022 ident: 10.1016/j.jtice.2022.104599_bib0021 article-title: A novel fault detection and diagnosis approach based on orthogonal autoencoders publication-title: Comput Chem Eng doi: 10.1016/j.compchemeng.2022.107853 – volume: 2014 start-page: 1957 year: 2014 ident: 10.1016/j.jtice.2022.104599_bib0023 article-title: JITL based local monitoring method for transitions of multiphase batch processes – volume: 36 start-page: 220 year: 2012 ident: 10.1016/j.jtice.2022.104599_bib0004 article-title: Survey on data-driven industrial process monitoring and diagnosis publication-title: Annu Rev Control doi: 10.1016/j.arcontrol.2012.09.004 – volume: 68 start-page: 3128 year: 2019 ident: 10.1016/j.jtice.2022.104599_bib0008 article-title: Fault detection for rolling-element bearings using multivariate statistical process control methods publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2018.2872610 – volume: 125 start-page: 371 year: 2022 ident: 10.1016/j.jtice.2022.104599_bib0017 article-title: Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients publication-title: ISA Trans doi: 10.1016/j.isatra.2021.06.005 – volume: 154 start-page: 77 year: 2015 ident: 10.1016/j.jtice.2022.104599_bib0009 article-title: Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.12.017 – volume: 6 start-page: 248 year: 2019 ident: 10.1016/j.jtice.2022.104599_bib0014 article-title: Incremental deep computation model for wireless big data feature learning publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2019.2903092 – volume: 112 start-page: 137 year: 2020 ident: 10.1016/j.jtice.2022.104599_bib0002 article-title: Visualization analysis for fault diagnosis in chemical processes using recurrent neural networks - ScienceDirect publication-title: J Taiwan Inst Chem Eng doi: 10.1016/j.jtice.2020.06.016 – volume: 10 start-page: 471 year: 2000 ident: 10.1016/j.jtice.2022.104599_bib0011 article-title: Recursive PCA for adaptive process monitoring publication-title: J Process Control doi: 10.1016/S0959-1524(00)00022-6 – volume: 23 start-page: 149 year: 1994 ident: 10.1016/j.jtice.2022.104599_bib0010 article-title: Exponentially weighted moving principal components analysis and projections to latent structures publication-title: Chemom Intell Lab Syst doi: 10.1016/0169-7439(93)E0075-F – volume: 16 start-page: 1427 year: 2008 ident: 10.1016/j.jtice.2022.104599_bib0013 article-title: Online monitoring of nonlinear multiple mode processes based on adaptive local model approach publication-title: Control Eng Pract doi: 10.1016/j.conengprac.2008.04.004 – volume: 29 start-page: 10 year: 2018 ident: 10.1016/j.jtice.2022.104599_bib0015 article-title: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2017.2716952 – volume: 112 start-page: 78 year: 2020 ident: 10.1016/j.jtice.2022.104599_bib0006 article-title: Dynamic statistical process monitoring based on generalized canonical variate analysis publication-title: J Taiwan Inst Chem Eng doi: 10.1016/j.jtice.2020.07.007 – volume: 22 start-page: 503 year: 1998 ident: 10.1016/j.jtice.2022.104599_bib0012 article-title: Recursive PLS algorithms for adaptive data modeling publication-title: ComputChemEng – volume: 14 start-page: 715 year: 2002 ident: 10.1016/j.jtice.2022.104599_bib0016 article-title: Slow feature analysis: unsupervised learning of invariances publication-title: Neural Comput doi: 10.1162/089976602317318938 – volume: 61 start-page: 181 year: 2015 ident: 10.1016/j.jtice.2022.104599_bib0019 article-title: Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis publication-title: AIChE J doi: 10.1002/aic.14888 – volume: 70 start-page: 1 year: 2021 ident: 10.1016/j.jtice.2022.104599_bib0018 article-title: Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder publication-title: IEEE Trans Instrum Meas – volume: 75 start-page: 136 year: 2019 ident: 10.1016/j.jtice.2022.104599_bib0022 article-title: Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring publication-title: J Process Control doi: 10.1016/j.jprocont.2019.01.008 – volume: 106 start-page: 1 year: 2020 ident: 10.1016/j.jtice.2022.104599_bib0001 article-title: Fault detection and diagnosis via standardized k nearest neighbor for multimode process - ScienceDirect publication-title: J Taiwan Inst Chem Eng doi: 10.1016/j.jtice.2019.09.017 – volume: 33 start-page: 1065 year: 1962 ident: 10.1016/j.jtice.2022.104599_bib0020 article-title: On estimation of a probability density function and mode publication-title: Ann Math Stat doi: 10.1214/aoms/1177704472 – volume: 52 start-page: 3543 year: 2013 ident: 10.1016/j.jtice.2022.104599_bib0003 article-title: Review of recent research on data-based process monitoring publication-title: Ind Eng Chem Res doi: 10.1021/ie302069q – volume: 112 start-page: 322 year: 2020 ident: 10.1016/j.jtice.2022.104599_bib0005 article-title: Process monitoring using principal component analysis and stacked autoencoder for linear and nonlinear coexisting industrial processes publication-title: J Taiwan Inst Chem Eng doi: 10.1016/j.jtice.2020.06.001 |
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