摘要
对发动机气缸失火故障进行实车模拟试验,测量了发动机的机体振动信号及瞬时转速信号,并对其进行了时、频域分析。通过小波分析方法提取了振动信号能量特征,通过复杂度分析方法提取了转速信号的复杂度特征用于故障诊断。根据多传感器信息融合理论,建立了集成神经网络信息融合模型对气缸失火故障进行了诊断。结果表明,发动机机体振动能量特征和转速复杂度特征能够反映气缸失火现象,基于发动机振动和转速信息融合进行气缸失火故障诊断,诊断可靠性较高。
On an armored vehicle, the simulation test of engine misfiring fault was performed, and the cylinder block vibration signal and the instantaneous speed signal of the engine were measured and the time domain and the frequency domain of these two signals were analyzed. The energy of the vibration signal was extracted by using wavelet analyzing technique and the complexity of the speed signal was calculated by using complexity measure. An integrated ANN model for data fusion was developed in order to diagnose the misfi- ring fault using the extracted characteristics of the vibration signal and the speed signal. The result shows that the vibration energy and the speed complexity can be used to reflect the misfiring appearance and it is reliable to diagnose engine misfiring fault based on data fusion of vibration and speed signal.
出处
《内燃机工程》
EI
CAS
CSCD
北大核心
2009年第1期74-79,共6页
Chinese Internal Combustion Engine Engineering
基金
武器装备预研基金项目(9140A27020206JB3502)
