- Author
-
Lee, W. Y.
|
House, J. M.
|
Shin, D. R.
- Title
- Fault Diagnosis and Temperature Sensor Recovery for an Air-Handling Unit.
- Coporate
- Korea Institute of Energy Research, Taejon, Korea
National Institute of Standards and Technology, Gaithersburg, MD
- Journal
-
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.