针对现有的失火诊断算法存在人工提取特征过程复杂，特征提取准则不确定等缺点，提出了利用缸盖振动信号结合深度学习算法实现柴油机部分失火故障的诊断方法。在不同的负荷及转速工况下进行柴油机的失火故障试验，并将采集到的缸盖振动信号归一化处理后作为训练深度学习算法的数据集。先后设计了卷积神经网络和更深层的残差网络，通过试验对比，发现具有更多层的残差网络可以更好地实现柴油机部分失火故障诊断，达到了较高的故障诊断准确率，并具有良好的泛化性能，可以在较宽的工况范围内有较好的诊断性能。

For the existing shortcomings of current misfire diagnosis algorithm such as complex manual feature extraction process and uncertain feature extraction criteria, a method of diagnosing partial misfire fault for diesel engine was proposed based on the cylinder head vibration signal combined with deep learning algorithm. The misfire fault experiment of diesel engine was carried out under different load and speed conditions, and the collected cylinder head vibration signal was normalized and used as the data set for training the deep learning algorithm. The convolutional neural network and deeper residual network were designed successively. Through the comparison of experiment, it was found that the residual network with more layers could better realize fault diagnosis of diesel engine partial misfire, which had high fault diagnosis accuracy and good generalization performance and hence good diagnosis performance in a wide range of working conditions.