Fault Detection Method Using Multi-mode Principal Component Analysis Based on Gaussian Mixture Model for Sewage Source Heat Pump System

摘要

This paper presents an algorithm for fault detection of a sewage heat pump system by designing multi-mode principal component analysis with Gaussian mixture model. If the heat pump system fails, the loss of energy and time is enormous, therefore the fault detection of the system is important. For this purpose, this study proposes a fault detection method using multi-mode principal component analysis with Gaussian mixture model. The data were clustered into multi-mode of Gaussian on principal component subspace. Based on the multi-model, the values of Hotelling's T-2 and SPE were calculated and used for the fault detection as indexes that are compared performance with clustering model using k-means and k-medoids algorithm as well as conventional PCA. Actual data of the sewage heat pump were used to verify the proposed method. The results of the fault detection performance show that the proposed model shows the best performance of fault detection among the conventional, k-means, and k-medoids PCA models.