ArticleEntropy (Basel, Switzerland)2022
Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Dual-Source Evidence-Driven Semi-Supervised Belief Rule Base for Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- An Imbalanced Fault Diagnosis Method Based on Multi-Sensor Selection and Graph Attention Mechanism.Sensors (Basel, Switzerland) · 2026Article
- A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning.Entropy (Basel, Switzerland) · 2024Article
- Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.
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