Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion

Xie, Fengyun; Liu, Hui; Dong, Jiankun; Wang, Gan; Wang, Linglan; Li, Gang

doi:10.3390/machines10121186

Open AccessArticle

Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion

by

Fengyun Xie

^1,2,*,

Hui Liu

¹,

Jiankun Dong

¹,

Gan Wang

¹,

Linglan Wang

¹ and

Gang Li

¹

School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China

²

State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China

^*

Author to whom correspondence should be addressed.

Machines 2022, 10(12), 1186; https://doi.org/10.3390/machines10121186

Submission received: 8 November 2022 / Revised: 6 December 2022 / Accepted: 7 December 2022 / Published: 8 December 2022

(This article belongs to the Section Machines Testing and Maintenance)

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Abstract

The gearbox is an important component of rotating machinery and is of great significance for gearbox fault diagnosis. In this paper, a gearbox fault diagnosis model based on multi-model feature fusion was proposed that addressed the limitations of a single or few features reflecting the gearbox’s fault state. The time–frequency feature of the vibration signal was extracted, and the sensitive feature was selected. The sensitive features were extracted using a one-dimensional convolutional neural network. The parallel fusion method was used to fuse the two domain features as inputs to the support vector machine model. The radial basis kernel function and penalty factor of the support vector machine were optimized by improving the particle swarm optimization algorithm. Finally, the gearbox states were identified using the optimized support vector machine model. The results show that the recognition rate of the proposed model is 98.3%, which is higher than that of other models.

Keywords: convolutional neural network; fault diagnosis; feature fusion; gearbox

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MDPI and ACS Style

Xie, F.; Liu, H.; Dong, J.; Wang, G.; Wang, L.; Li, G. Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion. Machines 2022, 10, 1186. https://doi.org/10.3390/machines10121186

AMA Style

Xie F, Liu H, Dong J, Wang G, Wang L, Li G. Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion. Machines. 2022; 10(12):1186. https://doi.org/10.3390/machines10121186

Chicago/Turabian Style

Xie, Fengyun, Hui Liu, Jiankun Dong, Gan Wang, Linglan Wang, and Gang Li. 2022. "Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion" Machines 10, no. 12: 1186. https://doi.org/10.3390/machines10121186

APA Style

Xie, F., Liu, H., Dong, J., Wang, G., Wang, L., & Li, G. (2022). Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion. Machines, 10(12), 1186. https://doi.org/10.3390/machines10121186

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Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion

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