Title :
Entropy-based Grey Correlation Fault Diagnosis Prediction Model
Author :
Ying, Zhao ; Lifang, Kong ; Guoliang, He
Author_Institution :
Air Force Logistic Acad., Xuzhou, China
Abstract :
In order to solve the fault diagnosis problem of automobile engine, the thesis puts forward an entropy-based grey correlation fault diagnosis prediction model. In light of the momentary of oil parameter for automobile engine, entropy-based data fusion can determine the weight of each factor in comprehensive evaluation. Then it makes forecast by grey correlation and evaluation of system oil. The result indicates that, the model is reliable, with strong generalization ability and higher failure recognition rate than that of the single models.
Keywords :
automobiles; entropy; fault diagnosis; forecasting theory; grey systems; internal combustion engines; sensor fusion; automobile engine; comprehensive evaluation; entropy-based data fusion; entropy-based grey correlation fault diagnosis prediction model; failure recognition rate; fault diagnosis problem; oil parameter; system oil evaluation; Automobiles; Correlation; Engines; Entropy; Fault diagnosis; Indexes; Standardization; entropy; fault diagnosis; grey correlation; oil parameter;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
Conference_Location :
Nanchang, Jiangxi
Print_ISBN :
978-1-4673-1902-7
DOI :
10.1109/IHMSC.2012.117
