Fault Identification Method of Diesel Engine Based on
Vibration Signal Image Encoding and Deep Learning

Vehicle Engine ›› 2026, Vol. 0 ›› Issue (1) : 88-94.

Fault Identification Method of Diesel Engine Based on Vibration Signal Image Encoding and Deep Learning

LI Xiangli¹,ZANG Jianlin²,XU Ran³,MA Jie¹,YU Tao¹,CHEN Dongfeng¹

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Abstract

Due to the typical nonlinear and non-stationary characteristics of diesel engine vibration signals, there are many challenges in the precise identification of diesel engine faults based on vibration signals. An end-to-end deep learning network framework (ResNet-ConvLSTM-Self-Attention) was proposed based on vibration signals. This framework combined the advantages of multiple models in extracting robust features without manual design. By encoding the vibration data stream images into the model, the framework can effectively identify three typical faults of diesel engines. The framework model underwent hyperparameter optimization, fault identification evaluation, and comparison with and without attention mechanisms, and an identification accuracy of 98.38% was achieved. The research results verified the effectiveness of the diesel engine fault identification model.

Key words

diesel engine / fault identification / deep learning / feature extraction / self-attention mechanism

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LI Xiangli, ZANG Jianlin, XU Ran, MA Jie, YU Tao, CHEN Dongfeng. Fault Identification Method of Diesel Engine Based on Vibration Signal Image Encoding and Deep Learning[J]. Vehicle Engine. 2026, 0(1): 88-94