Abstract
An ensemble approach of fault diagnosis based on lifting wavelet, multi-way principal component analysis and support vector machines (LW-MPCA-SVM) for batch process is developed in this paper. Firstly, data are preprocessed to remove noise by lifting scheme wavelets, which are faster than first generation wavelets; then multi-way principal component analysis is used to extract feature for high-accurate diagnosis of the batch process, and SVM is used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, LW-MPCA-SVM is applied to diagnose the faults in the simulation benchmark of fed-batch penicillin fermentation process. The results of simulation clearly demonstrate the effectiveness and feasibility of the proposed method, which diagnoses faults more accurately with desirable reliability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Intelligent Engineering and Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
