Fault Detection and Diagnostics for Rooftop Air Conditioners

prototype was developed by using the simulation data of a reference heating system. The
prototype was then applied to four heating systems not used during the training phase. Six
categories of fault modes and a reference normal mode were modeled. The paper
demonstrated the feasibility of using ANNs for detecting and diagnosing faults in heating
systems, provided that training data are available which are representative of the behavior
of the system with and without faults. Although the ANN prototype was trained using only
one simulated heating system, it showed good capacity for generalization. Two proposed
structures of network were trained, tested and compared. In their study, a single artificial
neural network performed better than multiple artificial neural networks. This is probably
because a structure composed of a single network learns global knowledge easier than one
composed of two multiple networks. So far, this FDD prototype has been studied only
using simulation data. However, this paper gave no information about the severity of the
faults detected.
Lee, House and Shin (1997) described the architecture for a two-stage artificial neural
network for fault diagnosis in a simulated air handling unit (AHU), and the use of
regression equations for sensor recovery of failed temperature sensors. To simply the
ANN, the AHU was divided into several subsystems. The stage-one ANN was trained to
classify the subsystems in which faults occurr, and the stage-two ANN was trained to
diagnose the cause of faults at the subsystem level. The trained ANNs were applied to
simulation data and shown to be able to identify eleven faults. A regression equation was
used to recover the estimate for the supply air temperature when the supply air
temperature sensor yielded erroneous measurements. The estimates of the sensor
measurement could be used for control purposes during a fault.
2.2.4 Radial basis function
Another black-box modeling technique uses radial basis functions (RBF) that work directly
from data as described by Mees [1992]. The main idea is as follows:
Suppose that by experiment, values y1, K, y of y have been found at x1
, K, x . The
radial basis approximation f (x) fitting the experimental data is defined by
m
m
m
∑
f ( x)
= λ φ(|
x − |)
(8)
i=
1
i
x i
16