Fault Diagnosis Based on Fusion of Residuals and Data for Chillers

Abstract

Feature data refer to direct measurements of specific features, while feature residuals represent the deviations between these measurements and their corresponding benchmark values. Both types of information offer unique insights into the system’s behavior. However, conventional diagnostic systems often struggle to effectively integrate and utilize both types of information concurrently. To address this limitation and improve diagnostic performance, a hybrid method based on the Bayesian network (BN) is proposed. This method enables the parallel fusion of feature residuals and feature data within a unified diagnostic model, and a comprehensive framework for developing this hybrid method is also given. In the hybrid BN, the symptom layer consists of residual nodes representing feature residuals and data nodes representing measured feature data. By applying the proposed method to two chillers and comparing it with state-of-the-art existing methods, we demonstrate its effectiveness and superiority. The results highlight that the proposed method not only accommodates the absence of either type of information but also leverages both of them to enhance diagnostic performance. Compared to using a single type of node, the hybrid method achieves a maximum improvement of 24.5% in diagnostic accuracy, with significant enhancements in F-measure observed for refrigerant leakage fault (34.5%) and excessive lubricant fault (32.8%), respectively.

Application of Data-Driven Methods for Heating Ventilation and Air Conditioning Systems

“…Data driven methods are widely used in fault diagnosis, pattern recognition, scheme optimization, and data analysis research for HVAC systems [12,13]. Wang et al [14] proposed a fault diagnosis model based on Bayesian networks. The proposed approach achieves the fusion of feature residuals and feature data in a single fault diagnosis model.…”

Performance Evaluation of Chiller Fault Detection and Diagnosis Using Only Field-Installed Sensors

“…Support vector data description (SVDD) is the third typical data-driven approach, and its application to FDD in the HVAC field can be found in works by Zhao et al [19], Chen et al [20], and Zhang et al [21]. Bayesian network (BN) is the fourth typical data-driven approach, and its application to FDD of HVAC systems is demonstrated in works by Wang et al [22,23], Ng et al [24], Li et al [25], and Liu et al [26]. Lastly, random forest (RF) represents the fifth typical data-driven approach, and its application to FDD of HVAC systems can be observed in works by Han et al [15] and Li et al [27].…”