Abstract : This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance costs, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.
[1]
Ten-Huei Guo,et al.
Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine
,
1992
.
[2]
Huibert Kwakernaak,et al.
Linear Optimal Control Systems
,
1972
.
[3]
Vasfi Eldem,et al.
Parametrization of multivariable systems using output injections: Alpha canonical forms
,
1993,
Autom..
[4]
W. C. Merrill,et al.
A real-time implementation of an advanced sensor failure detection, isolation, and accommodation algorithm
,
1984
.
[5]
Jonathan S. Litt,et al.
A simplified dynamic model of the T700 turboshaft engine
,
1995
.
[6]
Jonathan S. Litt.
An expert system to perform on-line controller restructuring for abrupt model changes
,
1990
.
[7]
T.-H. Guo,et al.
Sensor failure detection and recovery by neural networks
,
1991,
IJCNN-91-Seattle International Joint Conference on Neural Networks.
[8]
Mark G. Ballin,et al.
A high fidelity real-time simulation of a small turboshaft engine
,
1988
.