Process monitoring and fault detection in nonlinear chemical process based on multi-scale Kernel Fisher discriminant analysis

摘要

This paper presents a multi-scale kernel Fisher discriminant analysis (MSKFDA) algorithm combining Fisher discriminant analysis (FDA) and its nonlinear kernel variation with the wavelet analysis. This approach is proposed for investigating the potential integration of wavelets and multi-scale methods with discriminant analysis in nonlinear chemical process monitoring and fault detection system. In this paper, a discrete wavelet transform (DWT) is applied to extract the dynamics of the process at different scales. The wavelet coefficients obtained during the analysis are used as input for the algorithm. By decomposing the process data into multiple scales, MSKFDA analyse the dynamical data at different scales and then restructure scales that containedudimportant information by inverse discrete wavelet transform (IDWT). A monitoring statistic based on Hoteling’s T2ud statistics is used in process monitoring and faultuddetection. The Tennessee Eastman benchmark process is used to demonstrate the performance of the proposed approach in comparison with conventional statistical monitoring and fault detection methods. A comparison in terms of false alarm rate, missed alarm rate and detection delay, indicate that the proposed approach outperform the others and enhanced the capabilities of this approach for the diagnosis of industrial applications.