Describes the application of artificial neural networks (ANNs) to the problem of fault diagnosis in an air handling unit. Initially, residuals of system variables that can be used to quantify the dominant symptoms of fault modes of operation are selected. Then defines idealised steady-state patterns of the residuals for each fault mode of operation. The steady-state relationship between the dominant symptoms and the faults is learned by an ANN using the backpropagation algorithm. The trained neural network is applied to experimental data for various faults and successfully identifies each fault.
KEYWORDS: year 1996, Computer programs, expert systems, failure, air handling units, algorithms
Citation: Symposium Papers, Atlanta, GA, 1996
Product Details
- Published:
- 1996
- File Size:
- 1 file , 1.1 MB
- Product Code(s):
- D-16576
