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Diesel engine fault diagnosis method based on WACGAN and IRCNN
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Cheng Tang1, 2, Fengrong Bi1, Meng Huang1, Daijie Tang1, Pengfei Shen1, Xiaoyang Bi3
Transactions of CSICE | 2025, 43(3) : 270 - 278
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Transactions of CSICE | 2025, 43(3): 270-278
Diesel engine fault diagnosis method based on WACGAN and IRCNN
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Cheng Tang1, 2, Fengrong Bi1, Meng Huang1, Daijie Tang1, Pengfei Shen1, Xiaoyang Bi3
Affiliations
  • 1.State Key Laboratory of Engines,Tianjin University,Tianjin 300350,China
  • 2.Loncin Motor Company Limited,Chongqing 400052,China
  • 3.School of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,China
Published: 2025-05-25 doi: 10.16236/j.cnki.nrjxb.202503031
Outline
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In order to solve the problems of over-fitting and low accuracy of the vibration data-driven diesel engine fault diagnosis method caused by the lack of data, a diesel engine fault diagnosis method based on Wasserstein auxiliary classifier generative adversarial network(WACGAN) and Inception residual convolutional neural network(IRCNN) was established from two perspectives of fault data enhancement and diagnosis model optimization. Firstly, Wasserstein distance and gradient penalty were introduced into the auxiliary classifier generative adversarial network, and the WACGAN was established to augment the small-scale training set. Then, the inception structure was introduced into the convolutional neural network(CNN) and continuously differentiable exponential linear units(CELU) were used to improve the feature extraction ability of the model, while residual structure was added to avoid the loss of feature information, and IRCNN model was built. Finally, the IRCNN model was trained with the augmented training set to realize the diesel engine fault diagnosis under small samples. The method was validated by diesel engine fault simulation experiments, which can achieve fault recognition rate of 95% using only 10 operating cycles of vibration data in each fault state. Compared with the traditional oversampling algorithm and the CNN algorithm before and after optimization, the method works best when fault samples are scarce and can achieve fault diagnosis accuracy of 95.47%.

diesel engine  /  fault diagnosis  /  generative adversarial network  /  convolutional neural network
唐程, 毕凤荣, 黄盟, 汤代杰, 沈鹏飞, 毕晓阳. 基于WACGAN和IRCNN的柴油机故障诊断方法. 内燃机学报, 2025 , 43 (3) : 270 -278 . DOI: 10.16236/j.cnki.nrjxb.202503031
Cheng Tang, Fengrong Bi, Meng Huang, Daijie Tang, Pengfei Shen, Xiaoyang Bi. Diesel engine fault diagnosis method based on WACGAN and IRCNN[J]. Transactions of CSICE, 2025 , 43 (3) : 270 -278 . DOI: 10.16236/j.cnki.nrjxb.202503031
Year 2025 volume 43 Issue 3
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doi: 10.16236/j.cnki.nrjxb.202503031
  • Receive Date:2024-07-19
  • Online Date:2026-11-06
  • Published:2025-05-25
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  • Received:2024-07-19
  • Revised:2024-12-22
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Affiliations
    1.State Key Laboratory of Engines,Tianjin University,Tianjin 300350,China
    2.Loncin Motor Company Limited,Chongqing 400052,China
    3.School of Mechanical Engineering,Hebei University of Technology,Tianjin 300401,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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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
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