An evolving learning-based fault detection and diagnosis method: Case study for a passive chilled beam system

Traditional fault detection and diagnosis (FDD) methods learn from training data obtained under limited operating conditions, after which they stop learning. In this study, we developed an evolving learning-based FDD method for HVAC systems, which learns as the performance of a building system and its components changes. Specifically, an evolving learning algorithm—growing Gaussian mixture regression—is used to construct both a data-driven model representing normal performance and a transfer function for fault diagnosis. The evolving learning-based FDD method was demonstrated for detecting and diagnosing common faults of passive chilled beam systems. We employ generalized performance indices, such as the deviations between predictions (expectations) and measurements, the differences between two parameters, and other features extracted from parameters. A novel feature selection method was developed for selecting fault signatures. An uncertainty threshold determining whether a performance index was within the range of normal operation influences false alarm rates. By increasing the uncertainty thresholds from zero to two standard deviations, false alarm rates for normal operations were reduced from 14.8% to 1.3% and the percentage of normal operation data categorized as an unknown operation was reduced from 25% to 0%. Eight known faults were detected and diagnosed with an accuracy of 100%. A new fault was first categorized as an unknown fault before evolving. After evolving the transfer function by updating the key parameters of the Gaussian components, the unknown fault was also accurately diagnosed. The evolving learning-based FDD method and novel feature selection method can be employed for detecting and diagnosing common faults of other systems or subsystems in the built environment.