针对传统单通道振动信号诊断方法只能采集部分信息用于局部诊断，而多通道信号融合权重确定困难、实时性差的问题，提出一种基于深度一维卷积神经网络（Onedimensional Deep Convolutional Neural Network，1DCNN）与双通道信息融合的柴油发动机故障诊断方法。通过搭建柴油发动机预置故障试验台，将传感器配置于发动机不同位置以采集发动机运行过程中的双通道故障信号，分别提取振动信号中的最大值、最小值、峰峰值、均值、整流平均值、方差、标准差、峭度等14个特征，构建特征集矩阵并利用主成分分析（Principal Component Analysis，PCA）进行特征融合，输入深度一维卷积神经网络，实现对发动机不同故障状态的诊断。试验结果表明，该方法可以有效识别发动机不同的故障状态，与单通道信号诊断相比，所提出的双通道信息融合方法在发动机故障诊断中具有更好的效果。

The traditional single-channel vibration signal diagnosis method could only collect partial information for local diagnosis, the multi-channel signal fusion was also very difficult due to the uncertain weight and poor real-time property, and the fault diagnosis method of diesel engine was hence put forward based on one-dimensional deep convolutional neural network（1DCNN） and dual-channel information fusion. By building a diesel engine fault-preset test bench, the dual-channel fault signals during the operation were collected by the sensors of engine different positions and 14 features such as the maximum value, minimum value, peak-to-peak value, mean value, rectified average value, variance, standard deviation and kurtosis were extracted respectively. A feature set matrix was built, the feature fusion was then conducted by using the principal component analysis (PCA), the 1DCNN was further used, and the diagnosis of different engine fault states was realized in the end. The experimental results show that the method can effectively identify different engine fault states. Compared with singlechannel signal diagnosis, the proposed dualchannel information fusion method has better effect in engine fault diagnosis.