Distributed Diagnosis of Sensor and Actuator Faults in Air Handling Units in Multi-Zone Buildings: A Model-Based Approach

The presence of sensing and actuating equipment faults in Heating, Ventilation and Air-Conditioning (HVAC) systems may result in unsatisfactory indoor thermal conditions and significant wasted energy in the building sector. The design of fault diagnosis algorithms for multi-zone buildings is a highly challenging procedure due to the overall complexity of the system. This paper proposes a distributed model based fault diagnosis algorithm for detecting, isolating and identifying faults that can affect the actuating devices of Air Handling Units (AHUs) and the sensor devices. Based on modeling the temperature dynamics of each local AHU and its underlying zone with respect to its neighboring zones, a local fault diagnosis agent that collects local control signals and local and neighboring sensor measurements is designed. Each local diagnosis agent is designed to estimate on-line the states of each AHU. For each estimation, a residual signal is derived that corresponds to the difference between the measured and estimated value at each time step. Considering healthy conditions, the residual is bounded by its corresponding adaptive threshold that is calculated considering the effects of modeling uncertainties. Each corresponding pair of residual and adaptive threshold forms an analytical redundancy relation which indicates the possible presence of one or more faults. Exploiting the dependency of each actuator and sensor fault with each Analytical Redundancy Relation (ARR), a decision logic is designed, that can reveal under certain conditions the location and type of the fault during the operation of the system. The proposed algorithm is evaluated using a simulation example of a primary school building with 25 zones. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a distributed model based fault diagnosis algorithm for detecting and identifying faults in HVAC systems. The algorithm models the temperature dynamics of each local device and its neighboring zones and uses residual signals and adaptive thresholds to determine the presence of faults.