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- Title
多深度学习模型决策融合的齿轮箱故障 诊断分类方法.
- Authors
陈科; 段伟建; 吴胜利; 邢文婷
- Abstract
Aiming at the problems of incomplete information collected by a single sensor, poor fault tolerance, limitations of a neural network model and difficulty in extracting features by traditional signal processing technology, a gearbox fault diagnosis and classification method based on multi-depth learning model decision fusion was proposed, and a hybrid network model based on conventional neural networks (CNN) and improved stacked denoising autoencoders (SDAE) was constructed. According to the improved Dempster, Shafer(D-S)evidence theory, the decision-level fusion diagnosis was realized. The time-frequency signal was used as the input of CNN and the frequency-domain signal as the input of SDAE. Adam optimization algorithm, dropout and batch normalization technique were used to train the hybrid model. Experimental results show that the accuracy of gear fault diagnosis based on this fusion method is higher than that of single network model CNN and SDAE, which provides a new path for intelligent diagnosis and classification of gear faults.
- Publication
Science Technology & Engineering, 2022, Vol 22, Issue 12, p4804
- ISSN
1671-1815
- Publication type
Article