Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.
Virtual technology is used for simulation analysis of engine camshaft bearing-loosening fault. Firstly, dynamic model of engine powertrain and its valve-train is established, and then the model parameters could be set to simulate the camshaft bearing loosening fault, so the vibration acceleration signals on engine cylinder head can be obtained by simulation calculation. Then by analyzing and comparing with the vibration signals in the normal state, camshaft bearing-loosening fault features are extracted. The analytical result based-on model simulation and vibration signal is used to guide the actual engine fault diagnosis.
Engine mechanical fault usually causes abnormal change of the body surface vibration signal. Cylinder surface vibration signals under normal condition, piston knocking fault condition and main bearing wear fault condition are analyzed with wavelet packet decomposition method, relative energy value of each frequency band can be calculated and then be regarded as the input vector to form the training sample, BP neural network model is used to identify the fault state, test data shows that this method can effectively recognize the fault types.
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