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International Journal of Production Research Volume 52, 2014 - Issue 11
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Articles

Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description

Qingchao JiangKey Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P.R. China
,
Xuefeng YanKey Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P.R. ChinaCorrespondence[email protected]
,
Zhaomin LvKey Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P.R. China
&
Meijin GuoState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, P.R. China
Pages 3273-3286 | Received 26 Jan 2013, Accepted 20 Nov 2013, Published online: 20 Dec 2013

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Jianbo Yu, Shijin Li, Xing Liu, Yanfeng Gao, Shijin Wang & Changhui Liu. (2023) Dynamic convolutional gated recurrent unit attention auto-encoder for feature learning and fault detection in dynamic industrial processes. International Journal of Production Research 61:21, pages 7434-7452.
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Jianbo Yu & Xing Liu. (2022) One-dimensional residual convolutional auto-encoder for fault detection in complex industrial processes. International Journal of Production Research 60:18, pages 5655-5674.
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Chengyi Zhang, Jianbo Yu & Shijin Wang. (2021) Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder. International Journal of Production Research 59:8, pages 2426-2449.
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Yong-Hong Kuo & Andrew Kusiak. (2019) From data to big data in production research: the past and future trends. International Journal of Production Research 57:15-16, pages 4828-4853.
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Mu-Chen Chen, Chun-Chin Hsu, Bharat Malhotra & Manoj Kumar Tiwari. (2016) An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes. International Journal of Production Research 54:17, pages 5208-5218.
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Bhagwan Kumar Mishra & Anupam Das. (2023) Development of a Multi-block Modified Independent Component Analysis based Process Monitoring Strategy. Arabian Journal for Science and Engineering.
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Gopika Lakshmi Priya Palla & Ajaya Kumar Pani. (2023) Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems. Measurement 209, pages 112504.
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Jianbo Yu & Yue Zhang. (2022) Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review. Neural Computing and Applications 35:1, pages 211-252.
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Xiaogang Pan, Long Gao, Yuanyuan Jiao & Zhiwen Chen. (2022) A Dynamic GLR-Based Fault Detection Method for Non-Gaussain Dynamic Processes. Symmetry 14:7, pages 1332.
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Gang Xu, Qianqian Dong & Min Li. (2019) A simulated parameter optimization method–based manifold learning for a production process. Concurrency and Computation: Practice and Experience 34:7.
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Mahdie Eghdami, Hadi Sadoghi Yazdi & Neshat Salehi. (2021) Sparsity‐aware support vector data description reinforced by expectation maximization. Expert Systems 39:1.
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Jianbo Yu & Chengyi Zhang. (2020) Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes. Journal of Process Control 92, pages 119-136.
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Zhichao Li & Xuefeng Yan. (2020) Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models. Soft Computing 24:16, pages 12289-12302.
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Qianqian Dong, Raed Kontar, Min Li, Gang Xu & Jinwu Xu. (2019) A simple approach to multivariate monitoring of production processes with non-Gaussian data. Journal of Manufacturing Systems 53, pages 291-304.
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Jianlin Wang, Weimin Liu, Kepeng Qiu, Huan Xiong & Liqiang Zhao. (2017) Dynamic hypersphere SVDD without describing boundary for one-class classification. Neural Computing and Applications 31:8, pages 3295-3305.
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Zhichao Li & Xuefeng Yan. (2018) Adaptive Selective Ensemble-Independent Component Analysis Models for Process Monitoring. Industrial & Engineering Chemistry Research 57:24, pages 8240-8252.
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Jianlin Wang, Weimin Liu, Kepeng Qiu, Tao Yu & Liqiang Zhao. (2018) Dynamic hypersphere based support vector data description for batch process monitoring. Chemometrics and Intelligent Laboratory Systems 172, pages 17-32.
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Jinlin Zhu, Zhiqiang Ge & Zhihuan Song. (2017) Non-Gaussian Industrial Process Monitoring With Probabilistic Independent Component Analysis. IEEE Transactions on Automation Science and Engineering 14:2, pages 1309-1319.
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Ping Ling, Xiangyang You, Dajin Gao, Tao Gao & Xue Li. (2016) Fast distant support vector data description. Memetic Computing 9:1, pages 3-14.
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Yang Wang, Qingchao Jiang & Jingqi Fu. (2017) Efficient Monitoring of Nonlinear Chemical Processes based on Fault-Relevant Kernel Principal Component Subspace Construction and Bayesian Inference. Journal of Chemical Engineering of Japan JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 50:8, pages 648-656.
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Bei Wang, Xuefeng Yan & Qingchao Jiang. (2016) Independent component analysis model utilizing de-mixing information for improved non-Gaussian process monitoring. Computers & Industrial Engineering 94, pages 188-200.
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Qingchao Jiang, Biao Huang & Xuefeng Yan. (2016) GMM and optimal principal components-based Bayesian method for multimode fault diagnosis. Computers & Chemical Engineering 84, pages 338-349.
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Bei Wang, Xuefeng Yan & Qingchao Jiang. (2015) Loading-Based Principal Component Selection for PCA Integrated with Support Vector Data Description. Industrial & Engineering Chemistry Research 54:5, pages 1615-1627.
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Xiaoyi Liu, Yu Meng, Anzhi Yue, Jingbo Chen & Qingqing Huang. (2014) New normalized difference index for built-up land enhancement using airborne visible infrared imaging spectrometer imagery. Journal of Applied Remote Sensing 8:1, pages 085092.
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