RESEARCH PAPER
Integrated Multi-Scale Analysis and Advanced Prototype Model for Early Detection of Gearbox Failures Using Infrared Thermal Image Data Under Dynamic Conditions
1
College of Mechanical and Electrical Engineering, WenZhou University, China
Submission date: 2024-07-01
Final revision date: 2024-08-18
Acceptance date: 2024-10-05
Online publication date: 2024-12-14
Publication date: 2024-12-14
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194151
HIGHLIGHTS
- Prototype networks were first applied to infrared thermal images for fault analysis.
- A novel multi-scale module is constructed and incorporated into ProtoNet model.
- The proposed MSPNet can solve the gearbox fault diagnosis with small samples.
KEYWORDS
TOPICS
ABSTRACT
To address the issues of low data quality and poor adaptability in deep learning methods for infrared image analysis in gearbox fault diagnosis, this paper introduces an enhanced deep prototype network model (MSPNet). This model employs a multi-scale strategy to improve fault diagnosis accuracy and algorithm generalization, especially with small sample sizes. First, infrared image data of six fault types under five operating conditions are collected using a rotating test bed. Gaussian noise is added to simulate real operating conditions. Next, the fault data are processed using a multiscale module to extract multiscale fault features and reduce feature value fluctuations. Finally, the proposed model is used to process the image data and is experimentally compared with five other algorithms. The experimental results demonstrate that the proposed method outperforms the other algorithms under various operating conditions.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62201399, 52305124), the Zhejiang Natural Science Foundation of China (Nos. LQ23E050002, LD21E050001), the Basic Scientific Research Project of Wenzhou City (Nos. G2022008, G2023028), the General Scientific Research Project of Educational Department of Zhejiang Province (Nos. Y202249008, Y202249041), the Project funded by China Postdoctoral Science Foundation (Nos. 2023M740988), the Postdoctoral Research Merit Funding Program of Zhejiang Province (Nos. ZJ2023122), the Supported by the Master’s Innovation Foundation of Wenzhou University (Nos. 3162024004106), the Master’s Innovation Foundation of Wenzhou University (Nos. 3162024003067)
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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