基于深度迁移学习的柴油机故障诊断研究

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Abstract Data-driven intelligent fault diagnosis has lately attracted extensive interest, which deeply relates to the rapid development of big data and deep learning. However, when available fault data is limited, deep learning training is prone to overfitting, and the generalization ability of the trained network is affected. In this paper, an intelligent fault diagnosis method based on deep transfer learning is proposed to solve the problem of small samples. Construct a wide convolution kernel convolutional long short-term memory neural network to improve the feature extraction ability of diesel engine low signal-to-noise ratio fault data, and automatically extract features from the original data to enhance the intelligence of feature learning. A transfer learning scheme is further adopted to transfer the diagnostic knowledge of large-scale labeled source domain data to the target domain network, and improve the learning and classification capabilities of the network in the target domain tasks with small samples. Algorithm evaluation on cross-fault domain and cross- equipment domain, and comparison with traditional deep neural networks, verifies that the proposed transfer learning based on WKCL can effectively improve the performance of small sample diagnosis.

Key words： transfer learning diesel engine fault diagnosis small sample noise immunity

Received: 25 August 2022 Published: 15 November 2023

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	Articles by authors
	SONG Yedong1
	MA Guangwei1
	PEI Guobin2
	ZHANG Junhong2
	3

Cite this article:

SONG Yedong1,MA Guangwei1,PEI Guobin2, et al. Diesel engine fault diagnosis based on deep transfer learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(21): 219-226.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I21/219

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