Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest

Abstract

Fault detection and diagnosis (FDD) can reduce a large amount of energy wasted by faults in variable air volume (VAV) systems. This paper presents a FDD method for detecting and diagnosing multiple faults of VAV terminals using a hybrid method. A self-adaptive zone air-temperature model is presented and used to detect faults of the VAV terminals by comparing the residuals between predicted and measured values with corresponding fault detection thresholds. The model parameters, which are normally constant and determined according to experience from short-period data, are periodically adjusted to improve the model prediction accuracy on the basis of operating data by using a chaos particle swarm optimization algorithm. Moreover, a two-layered random forest-based fault diagnosis method is presented to isolate multiple faults of VAV terminals according to the complexity of the fault symptoms. An expert rule-based fault diagnosis layer is used to isolate VAV terminal faults with simple fault symptoms. A random forest-based fault diagnosis layer is utilized to diagnose VAV terminal faults with complex fault symptoms by adopting a binary decision tree-based multi-label technique. The proposed FDD method is validated using operating data from real VAV systems involving 13 single-fault cases and 6 simultaneous-fault cases. The validation results indicate that the proposed FDD method can accurately detect and diagnose simultaneous multiple faults with only a few independent fault samples and a minimal number of simultaneous-fault samples.