Information fusion application for engine in condition measurement and fault diagnose
Liang Guihang, Jingnuo Yu, Jian Wang, Jingui Song · 2012
According to a variety of engine malfunctions, a method for fault diagnoses of engine based on the fuzzy logic theory and neural net work is put forward. Applying fuzzy membership functions to depict the fault extent, the system has the characteristic of fast inference speed. The model of engine fault diagnoses is set up by using the state parameter as learning samples. The data that engine state is identified are sample for the model. The results after tested show that it has a great improvement in convenient operation, facilitates to use. This method has more accurately for diagnosis faults of engine. It can improve the veracity for diagnose the fault of engine. And it can also develop the optimal control of engine.