Fault diagnosis of heating systems using multivariate feature extraction based machine learning classifiers. (July 2020) - Gharsellaoui, Sondes; Mansouri, Majdi; Trabelsi, Mohamed; Refaat, Shady S.; Messaoud, Hassani
Abstract: An essential tool for an effective monitoring of heating systems is the accurate Fault Detection and Diagnosis (FDD). Moreover, the implementation of high-efficiency FDD techniques leads to the reduction of building's energy consumption and the improvement of the comfort level. Thus, this paper proposes an enhanced FDD approach based on multiscale representation, principal component analysis (PCA), and machine learning (ML) classifiers. The main goal of this paper is to merge the benefits of the aforementioned techniques to improve the efficiency of the detection and diagnosis of failures occurring in heating systems. First, a multiscale decomposition (MSD) is used to extract the dynamics of the systems at different scales. The multiscale representation is very suitable for the monitoring of heating systems, which are generally characterized by different dynamics in time and frequency frameworks. Then, the multiscaled data-set are introduced into the PCA to extract more efficient features. Finally, the ML classifiers are applied to the extracted and selected features to address the problem of fault diagnosis. The effectiveness and higher classification accuracy of the developed approach is demonstrated using two examples: synthetic data and simulated data extracted from heating systems.