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Open Access

ARTICLE

Research on Gearbox Fault Diagnosis Method Based on Multi-Dimensional Feature Extraction and Random Forest

Yu Zhang1,2,#, Shihan Tan1,#, Guangyao Lian2, Congying Dun3, Qiwei Hu1,*, Chiming Guo1,*
1 Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, China
2 No. 32181 Unit of PLA, Xian, China
3 Army Command Academy, Nanjing, China
* Corresponding Author: Qiwei Hu. Email: email; Chiming Guo. Email: email
# These authors contributed equally to this work as the first author

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081931

Received 12 March 2026; Accepted 22 April 2026; Published online 25 May 2026

Abstract

Gearboxes are critical components in the transmission systems of various mechanical equipment. Subjected to complex and harsh operating conditions for a long time, they suffer from a high failure rate and potentially severe consequences. Traditional fault diagnosis methods are limited by problems such as noise interference, and can hardly meet the requirements in terms of diagnostic accuracy, generalization ability, and reliability. To tackle the deficiencies of traditional gearbox fault diagnosis methods, including insufficient utilization of features, poor generalization under small-sample conditions, and weak model interpretability, this paper proposes a fault diagnosis method based on multi-dimensional feature extraction and Random Forest (RF). This method integrates intelligent computing, data-driven approaches, and mechanical structural health monitoring. First, fault feature analysis is conducted from multiple dimensions including time domain, frequency domain, and envelope domain, and visualization verification is implemented using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Then, the Random Forest (RF) algorithm is used for dataset training and testing, obtaining a stable diagnostic model with strong generalization ability. Finally, experimental analyses verify the effectiveness and superiority of the proposed method. The research results possess high application potential and practical value in improving the performance of gearbox fault diagnosis.

Keywords

Fault diagnosis; feature extraction; principal component analysis; gearbox; random forest

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