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- Title
面向微小振动故障诊断的匹配小波深度迁移学习.
- Authors
张莹; 彭庭威; 罗睿敏
- Abstract
This paper proposed a matching wavelet depth model transfer learning algorithm to solve the problems of low efficiency of feature identification and limited number of samples, which exist in traditional methods for small vibration fault diagnosis. Firstly, using Morse continuous wavelets to capture small changes in a one-dimensional fault signal by matching and up-dimensioning the signal to obtain a visual enhanced feature image. Then, this algorithm effectively transfers the source domain model of the depth transfer network. This model has efficient image learning experience and can reduce the number of training samples in the target domain. Finally, optimize the parameters of the process for this algorithm based on limited data in model transfer. The algorithm has proven to be highly generalisable, allowing the detection and localisation of minute features in multiple operating conditions and effectively reducing the reliance on data, greatly improving the speed of computing and diagnostic accuracy.
- Subjects
MACHINE learning; FAULT diagnosis; PROBLEM solving; WAVELET transforms; DATA modeling; IMAGE registration
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2023, Vol 40, Issue 8, p2417
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2023.01.0006