基于改进段角加速度和神经网络的柴油机失火诊断研究

针对失火故障中存在的高速轻载诊断困难,失火程度无法判别的问题,通过对比分析正常状态与失火情况下瞬时转速的特征,发现缩短段角加速度段长度,能够有效提升特征对失火故障的敏感度,同时,用神经网络方法代替阈值规则,能够很好地利用各缸特征值间的联系诊断失火。基于此,提出一种改进段角加速度和神经网络相结合的失火故障诊断方法。该方法能够实现对全转速范围单缸完全失火的诊断,且利用二级诊断的方式可以对失火程度进行有效判别,在高速轻载工况依旧具有很好的准确率。同时,提出的方法在学习阶段所需数据量小,适用于发动机失火故障的在线诊断。

Diagnosis of Misfire Fault of Diesel Engines Based on Segment Angular Acceleration and Neural Network

This paper focus on the diagnosis problem of the misfire and the misfire degree caused at high speed and low loads, compare and analyse the transient speed characteristics under normal and misfire conditions, and find it can effectively improve characteristics sensitivity of engine misfire by shortening the segment length of angular acceleration speed. According to the analysis on the misfire fault diagnosis, it is found that the threshold value method is replaced with the neural network method can effectively diagnose the misfire by employing the relationship between different cylinder characteristic value. Based on the above, a improved misfire diagnosis method of combination of improved segment angular acceleration with the neural network is proposed finally. This method can carry out accurate single cylinder misfire diagnosis over the full speed range, and can determine the misfire degree effectively with the second level diagnosis. And the new approach has a good accuracy rate of misfire fault diagnosis. Meanwhile, it requires less data in the network learning phase and is easy to grasp, which is suitable for on-line diagnosis of engine misfire faults.