Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis

Abstract

The application of fault detection and diagnosis (FDD) for chillers is very crucial for ensuring the smooth operation, stability, and durability of HVAC systems. Recent FDD models with outstanding diagnostic performance have been proposed for chiller systems. However, the high performance of FDD models is only guaranteed in condition observed data for FDD model construction are not contaminated, i.e., do not contain outliers, which is inevitable during data recording process. To resolve this problem, this paper proposes a novel outlier detection (OD) method named NN-MPPCA. The proposed method uses a neural network (NN) to model a mixture of probabilistic principal component analyzers (MPPCA) framework which parameters are updated via back-propagation. The combining mechanism of MPPCA guarantees NN-MPPCA to fit well complex data distributions, and unseen instances with low reconstruction probability under MPPCA framework are detected as outliers. Comprehensive experimental results of exhaustive comparisons have demonstrated the superiority of NN-MPPCA over other state-of-the-art OD algorithms as well as its effectiveness for chiller FDD application. Under the scenario that datasets contain 20% outliers (i.e., both local outliers and global outliers), the outlier detection performance of NN-MPPCA is highest with F1-scores achieving from 95.4% to 99.7%. Also, with NN-MPPCA applied in the data purification step, FDD models can achieve a diagnostic accuracy up to 99.5%; while they can only reach a diagnostic accuracy of 82.6% at most if contaminated data are directly used.