An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information

Abstract

Sensor fault detection is essential to maintain operations of heating, ventilation, and air conditioning systems (HVACs) in buildings. Data-driven sensor fault detection methods are becoming increasingly popular recently. However, for the relatively limited modelling data available in the heating, ventilation, and air conditioning system, it is a major challenge to extract fault-discrimination information (FDI) from limited data to develop a data-driven model with higher fault detection performance and lower false-alarm rate. Therefore, this study proposed an improved Stacking (IStacking) sensor fault detection method using fault-discrimination information. Fault-discrimination-information is extracted from original samples by subtracting the statistics with the threshold of each single model. Four different single models (i.e., principal component analysis, one-class support vector machine, K-Means clustering, and autoencoder) are employed to develop the ensemble learning detection method with relatively high generalization ability using a Stacking training manner. Sensor bias faults from two different sources are used for validation. Area under the ROC (receiver operating characteristic) curve (AUC) and false positive rate (FPR) are used to evaluate the detection performance and false-alarm rate. Results indicated that IStacking outperforms the four single models and traditional Stacking when the added biases are ranged [-4 °C, −2 °C] and [3.7 °C, 4 °C]. Since single models are stacked and fault-discrimination information is used to replace the original input, IStacking averagely increases Area under the ROC curve by 2.85% and decreases false positive rate by 5.76%.