Title :
Artificial neural network for sensor failure detection in an automotive engine
Author :
Xinmin, Zhao ; Xiaochun, Ye ; Chen, Zhang ; Jinwei, Sun
Author_Institution :
Dept. of Electr. Eng., Harbin Inst. of Technol., China
Abstract :
This paper presents a sensor failure detection method with artificial neural network for critical complex equipments, where their dynamic performance usually can not be actually obtained. However, if the number of measured variables is higher than the order of the system, using the inherent redundant relationship among sensors, we can detect the sensor failure and even recover or estimate the correct readings of the failed sensor. Two kinds of artificial neural network were trained to accomplish these aims, one is used to detect the sensor failure and the other is applied to recover the readings of failed sensor. The feasibility of this method was proved with computer simulation through a mathematic model of an automotive engine in a hovercraft
Keywords :
automatic test equipment; computerised instrumentation; detectors; digital simulation; engines; gas turbines; hovercraft; learning (artificial intelligence); neural nets; artificial neural network; automotive engine; computer simulation; correct readings; critical complex equipment; dynamic performance; failed sensor; feasibility; hovercraft; mathematic model; redundant relationship; sensor failure detection; Artificial neural networks; Automotive engineering; Computer simulation; Condition monitoring; Engines; Intelligent networks; Power system modeling; Sensor systems; Space shuttles; Thermal force;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1994. IMTC/94. Conference Proceedings. 10th Anniversary. Advanced Technologies in I & M., 1994 IEEE
Conference_Location :
Hamamatsu
Print_ISBN :
0-7803-1880-3
DOI :
10.1109/IMTC.1994.352099