Title :
Fault detection and isolation for an experimental internal combustion engine via fuzzy identification
Author :
Laukonen, E.G. ; Passino, K.M. ; Krishnaswami, V. ; Luh, G.-C. ; Rizzoni, G.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
fDate :
9/1/1995 12:00:00 AM
Abstract :
Certain engine faults can be detected and isolated by examining the pattern of deviations of engine signals from their nominal unfailed values. In this brief paper, we show how to construct a fuzzy identifier to estimate the engine signals necessary to calculate the deviation from nominal engine behavior, so that we may determine if the engine has certain actuator and sensor “calibration faults”. We compare the fuzzy identifier to a nonlinear ARMAX technique and provide experimental results showing the effectiveness of our fuzzy identification based failure detection and identification strategy
Keywords :
autoregressive moving average processes; failure analysis; fault location; fuzzy set theory; identification; internal combustion engines; actuator calibration faults; failure detection; failure identification; fault detection; fault isolation; fuzzy identification; internal combustion engine; nonlinear ARMAX technique; sensor calibration faults; Actuators; Control systems; Fault detection; Fault diagnosis; Fuels; Internal combustion engines; Mechanical engineering; Temperature sensors; Testing; Vehicle detection;
Journal_Title :
Control Systems Technology, IEEE Transactions on
