Abstract
An improved multi-scale principal component analysis (MSPCA) is used for fault detection and diagnosis. Improved MSPCA simultaneously extracts both, cross correlation across the variable (principal component analysis (PCA) approach) and auto-correlation within a variable (wavelet approach). The data collected from the industry condition are processed by means of the nonlinear wavelet threshold denoising method. Using wavelets, the individual variable are decomposed into approximations and details at different scales. Contributions from each scale are collected in separate matrices, and a PCA model is then constructed to extract correlation at each scale. According to the analysis of simulation of chemical process, and comparing the improved MSPCA with MSPCA, it shows that the improved MSPCA has enhanced the accuracy of fault detection in process monitoring.
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 callDisclaimer: 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.
