Fault Diagnosis and Temperature Sensor Recovery for an Air-Handling Unit.
Fault Diagnosis and Temperature Sensor Recovery for an
Air-Handling Unit.
(1324 K)
Lee, W. Y.; House, J. M.; Shin, D. R.
ASHRAE Transactions, Vol. 103, No. 1, [pages unknown],
1997.
Keywords:
heating; ventilation; air conditioning; sensors; fault
diagnosis; neural network; equations; temperature
Abstract:
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 neural
networkd. 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 equaiton 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 diagnosis of the
two-stage artificial neural network from normal
operation to various fault modes of operation.
Building and Fire Research Laboratory
National Institute of Standards and Technology
Gaithersburg, MD 20899