Fault diagnosis and temperature sensor recovery for an air-handling unit

TLDR: In this paper, the authors used a two-stage artificial neural network for fault diagnosis in a simulated air-handling unit, where the first stage identifies the subsystem in which a fault occurs and the second stage detects the specific cause of a fault at the subsystem level.

Abstract: The presence of faults and the influence they have on system operation is a real concern in the heating, ventilating, and air-conditioning (HVAC) community. A fault can be defined as an inadmissible or unacceptable property of a system or a component. Unless corrected, faults can lead to increased energy use, shorter equipment life, and uncomfortable and/or unhealthy conditions for building occupants. This paper describes the use of a two-stage artificial neural network for fault diagnosis in a simulated air-handling unit. The stage one neural network is trained to identify the subsystem in which a fault occurs. The stage two neural network is trained to diagnose the specific cause of a fault at the subsystem level. Regression equations for the supply and mixed-air temperatures are obtained from simulation data and are used to compute input parameters to the neutral networks. Simulation results are presented that demonstrate that, after a successful diagnosis of a supply air temperature sensor fault, the recovered estimate of the supply air temperature obtained from the regression equation can be used in a feedback control loop to bring the supply air temperature back to the setpoint value. Results are also presented that illustrate the evolution of the diagnosismore » of the two-stage artificial neural network from normal operation to various fault modes of operation.« less