Fault Detection and Diagnosis Using Tree-Based Ensemble Learning Methods and Multivariate Control Charts for Centrifugal Chillers

Fault detection and diagnosis (FDD) of centrifugal chillers plays an essential role in reducing energy consumption and ensuring safe operation of heating, ventilation and air conditioning systems. In the existing chiller FDD strategy, Data-driven based system modelling methods and statistical analysis-based detection techniques have been widely discussed and applied in recent years. This paper studies the performance of tree-based ensemble learning methods in chiller modelling and analyses multivariate control charts' detection effect. Predictions from the reference model are used in tandem with multivariate control charts to isolate faulty behavior from normal behavior, and then specific failures are identified through the diagnosis model when anomalies are detected. Firstly, in order to establish the optimal chiller reference model and diagnosis model, three tree-based ensemble learning algorithms, namely (i) random forest (RF), (ii) extreme gradient boosting (XGBoost), and (iii) light gradient boosting machine (LightGBM), are evaluated and compared with commonly used support vector machine (SVM). Secondly, Hotelling's T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) are introduced and their performance in chiller fault detection is tested. Finally, the proposed LightGBM-MEWMA method is validated by the experimental data of ASHRAE RP-1043. Results show that the proposed method effectively identifies seven common chiller faults. Especially at low severity levels, the average detection rate is 88.71%, and the diagnosis accuracy rate is 82.3%.