Fault diagnosis methods for traditional distributed aero piston engines require manual involvement，and the diagnostic process is also disjointed. Fault diagnosis based on one-dimensional convolutional neural networks(1D-CNN) tends to overfit easily and exhibited a single-scale feature extraction issue. In response to the above issues，a modified residual multi-scale convolution block combined with a convolutional attention mechanism and a multi-scale feature fusion classification layer were designed. Based on this，a residual multi-scale convolutional neural network(RMSCNN) was constructed for fault diagnosis of the ignition and fuel injection systems of an aero piston engine. Initially，multi-scale feature information from raw time-domain data was extracted by improving the residual multi-scale convolution block. Subsequently，the network structure with parallel outputs was dimensionally reduced and fused to form a multi-scale feature fusion layer. Finally，Softmax was used for classification recognition. Experimental results show that for a dataset of 16 fault categories of the aero piston engine，including abnormal ignition advance and abnormal fuel injection quantity，RMSCNN achieves a fault diagnosis accuracy of 91.44%. Compared to common networks such as 1D-CNN，GoogLeNet，and ResNet50，it demonstrates superior fault diagnosis performance.