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

• Published in: Neural computing & applications 2020-07, Vol.32 (13), p.9283-9297
• Main Authors: Md Nor, Norazwan, Hussain, Mohd Azlan, Che Hassan, Che Rosmani
• Format: Article
• Language: English
• Subjects: Adaptive systems; Artificial Intelligence; Artificial neural networks; Classification; Complex variables; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Discriminant analysis; Energy spectra; Fault detection; Fault diagnosis; Feature extraction; Fuzzy logic; Fuzzy systems; Image Processing and Computer Vision; Multiscale analysis; Organic chemistry; Original Article; Performance enhancement; Probability and Statistics in Computer Science; Theorems; Wavelet analysis; Wavelet transforms
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04438-9

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%).