Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network

This paper describes an internal combustion engine fault diagnosis system using the manifold pressure of the intake system. The manifold pressure of the engine intake system always demonstrates the engine condition and affects the volumetric efficiency, fuel consumption and performance of internal combustion engines. Manifold pressure is well known to be detrimental to engine system stability and performance and it must be considered during regular maintenance. Conventional engine diagnostic technology using manifold pressure in intake system already exists through analyzing the differences between signals and depends on the experience of the technician. Obviously, the conventional detection is not a precise approach for manifold pressure detection when the engine in operation condition. In the present study, a system consisted of manifold pressure signal feature extraction using discrete wavelet transform (DWT) and fault recognition using the neural network technique is proposed. To verify the effect of the proposed system for identification, both the radial basis function network (RBFN) and generalized regression neural network (GRNN) are used and compared in this study. The experimental results indicated the proposed system using manifold pressure signal as data input is effective for engine fault diagnosis in the experimental engine platform.