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Supervised process monitoring and fault diagnosis based on machine learning methods

The International Journal of Advanced Manufacturing Technology volume 102, pages 2321–2337 (2019)Cite this article

Abstract

Data-driven techniques have been receiving considerable attention in the industrial process monitoring field due to their major advantages of easy implementation and less requirement for the prior knowledge and process mechanism. Principal component analysis (PCA) method is known as a popular method for monitoring and fault detection in industrial systems but as it is basically a linear method. However, most practical systems are nonlinear. To make the extension to nonlinear systems, kernel PCA (KPCA) method has been proposed for process modeling and monitoring. We present in this paper an online reduced rank optimized KPCA (RR-KPCA) technique for fault detection in order to extend the advantages of the KPCA models to online processes. Following the fault detection, the identification of the variables correlated to the fault occurred is of great importance. For this purpose, it is proposed to extend the approaches of localization by partial PCA and by elimination in the linear case to the nonlinear case, by exploiting the solution of reduction of the dimension of the kernel matrix in the feature space. The partial RR-KPCA and the elimination sensor identification (ESI-RRKPCA) are generated based on the static RR-KPCA and the online RR-KPCA methods. The idea of these approaches is to generate partial RR-KPCA models with reduced sets of variables. In other words, their goal is to generate indices of fault detection sensitive to certain faults and insensitive to others. The proposed fault isolation methods are applied for monitoring an air quality monitoring network (AIRLOR) data.

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Authors and Affiliations

  1. National Engineering School of Monastir, University of Monastir, 5019, Monastir, Tunisia

    Hajer Lahdhiri & Okba Taouali

  2. National Engineering School of Sousse, MARS Research Laboratory, LR17ES05, University of Sousse, 4011, Hammam Sousse, Tunisia

    Maroua Said

  3. ISITCom, MARS Research Laboratory, LR17ES05, University of Sousse, 4011, Hammam Sousse, Tunisia

    Khaoula Ben Abdellafou

  4. Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

    Khaoula Ben Abdellafou & Okba Taouali

  5. Department of Electronics, University Badji Mokhtar, B.P. 12, 23000, Annaba, Algeria

    Mohamed Faouzi Harkat

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  1. Hajer Lahdhiri
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  2. Maroua Said
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  3. Khaoula Ben Abdellafou
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  4. Okba Taouali
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  5. Mohamed Faouzi Harkat
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Correspondence to Okba Taouali.

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Lahdhiri, H., Said, M., Abdellafou, K.B. et al. Supervised process monitoring and fault diagnosis based on machine learning methods. Int J Adv Manuf Technol 102, 2321–2337 (2019). https://doi.org/10.1007/s00170-019-03306-z

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  • DOI: https://doi.org/10.1007/s00170-019-03306-z

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