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Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems

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Abstract

Fault detection and diagnosis (FDD) framework is one of safety aspects that is important to the industrial sector to ensure its high-quality production and processes. However, the development of FDD system in chemical process systems could have difficulties, e.g. highly nonlinear correlation within the variables, highly complex process, and an enormous number of sensors to be monitored. These issues have encouraged the development of various approaches to increase the effectiveness and robustness of the FDD framework, such as the wavelet transform analysis, where it has the advantage in extracting the significant features in both time and frequency domain. It has motivated us to propose an extension work of the multi-scale KFDA method, where we have modified it with the implementation of Parseval’s theorem and the application of ANFIS method to improve the performance of the fault classification. In this work, through the implementation of Parseval’s theorem, the observation of fault features via the energy spectrum and effective reduction in DWT analysis data quantity can be accomplished. The extracted features from the multi-scale KFDA method are used for fault diagnosis and classification, where multiple ANFIS models were developed for each designated fault pattern to increase the classification accuracy and reduce the diagnosis error rate. The fault classification performance of the proposed framework has been evaluated using a benchmarked Tennessee Eastman process. The results indicated that the proposed multi-scale KFDA-ANFIS framework has shown the improvement with an average of 87.02% in classification accuracy over the multi-scale PCA-ANFIS (78.90%) and FDA-ANFIS (70.80%).

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Acknowledgements

The authors would like to thank the Universiti Sains Malaysia (USM) for the ASTS fellowship programme, and University of Malaya (UM) and the Ministry of Higher Education (MOHE) Malaysia for their supporting in this work.

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Authors and Affiliations

  1. School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    Norazwan Md Nor

  2. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Norazwan Md Nor, Mohd Azlan Hussain & Che Rosmani Che Hassan

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  1. Norazwan Md Nor
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  2. Mohd Azlan Hussain
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Md Nor, N., Hussain, M.A. & Che Hassan, C.R. Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems. Neural Comput & Applic 32, 9283–9297 (2020). https://doi.org/10.1007/s00521-019-04438-9

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