Work

An Improved Fault Diagnosis Method for Diesel Engines Based on Optimized Variational Mode Decomposition and Transformer-SVM

Year: 2026

Type: article

Abstract: Due to the non-stationary and nonlinear characteristics of diesel engine vibration signals, fault features cannot be fully extracted, which limits fault diagnosis performance. To address this issue, an improved fault diagnosis method combining optimized Variational Mode Decomposition with a Transformer and Support Vector Machine is proposed. An improved dung beetle optimization algorithm is employed to obtain optimal parameters for Variational Mode Decomposition. The envelope entropy minimization principle is applied to select the optimal intrinsic mode functions after Variational Mode Decomposition, achieving signal denoising. Analysis of variance is integrated for feature significance testing to screen critical features. The selected features are fed into a Transformer network for training. At the final classification stage, the traditional SoftMax classifier is replaced with a Support Vector Machine classifier.

Source: Processes

DOI: 10.3390/pr14071131

Authors: Xiaoxin Ma, Shuyao Tian, Xianbiao Zhan, Hao Yan, Kaibo Cui

Institutions: North China Institute of Aerospace Engineering, PLA Army Engineering University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896