A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD

The vibration signal from the planetary gearbox exhibits nonlinear and impulsive characteristics amidst strong noise, impeding the effective extraction of fault information and compromising the accuracy of fault diagnosis. To address this challenge, a fault diagnosis method rooted in feature mode de...

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Bibliographic Details
Published in:Shock and vibration 2024-08, Vol.2024
Main Authors: Bie, Fengfeng, Ding, Xueping, Li, Qianqian, Zhang, Yuting, Huang, Xinyue
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Decomposition
Deep learning
Effectiveness
Entropy
Fault diagnosis
Feature extraction
Foot-and-mouth disease
Gearboxes
Genetic algorithms
Kurtosis
Mathematical optimization
Methods
Mutation
Neural networks
Particle swarm optimization
Signal processing
Support vector machines
Vibration
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Summary:The vibration signal from the planetary gearbox exhibits nonlinear and impulsive characteristics amidst strong noise, impeding the effective extraction of fault information and compromising the accuracy of fault diagnosis. To address this challenge, a fault diagnosis method rooted in feature mode decomposition (FMD) is proposed. Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. Subsequently, a convolutional neural network integrated with the support vector machine model (CNN-SVM) is established, leveraging the convolutional neural network for feature extraction. Ultimately, SVM iteratively optimized by the particle swarm optimization (PSO) algorithm, serves as the classification technique. Simulation and experiment results demonstrate the effectiveness of this method in extracting and identifying fault information within planetary gearboxes.
ISSN:1070-9622
1875-9203
DOI:10.1155/2024/2140227