Full
Outline
Fault diagnosis methods for traditional distributed aero piston engines require manual involvement，and the diagnostic process is also discontinuous. Fault diagnosis based on one-dimensional convolutional neural networks(1D-CNN) tends to overfit easily and exhibits 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 angle 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.
14
5
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
