A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

被引:21
作者
Yang, Daoguang [1 ]
Karimi, Hamid Reza [1 ]
Gelman, Len [2 ]
机构
[1] Politecn Milan, Dept Mech Engn, I-20156 Milan, Italy
[2] Univ Huddersfield, Sch Comp & Engn, Huddersfield HD1 3DH, W Yorkshire, England
关键词
Convolutional Neural Network; rotating machinery; fuzzy fusion; fault diagnosis; CLASSIFICATION;
D O I
10.3390/s22020671
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.
引用
收藏
页数:17
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