کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11263857 1645964 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath-Geva clustering algorithm without principal component analysis and data label
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath-Geva clustering algorithm without principal component analysis and data label
چکیده انگلیسی
Most deep learning models such as stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) are used for fault diagnosis with a data label. These models are applied to extract the useful features with several hidden layers, then a classifier is used to complete the fault diagnosis. However, these fault diagnosis classification methods are only suitable for tagged datasets. Actually, many datasets are untagged in practical engineering. The clustering method can classify data without a label. Therefore, a method based on the SDAE and Gath-Geva (GG) clustering algorithm for roller bearing fault diagnosis without a data label is proposed in this study. First, SDAE is selected to extract the useful feature and reduce the dimension of the vibration signal to two or three dimensions direct without principal component analysis (PCA) of the final hidden layer. Then GG is deployed to identify the different faults. To demonstrate that the feature extraction performance of the SDAE is better than that of the SAE and EEMD with the FE model, the PCA is selected to reduce the dimension of eigenvectors obtained from several previously hidden layers, except for the final hidden layer. Compared with SAE and ensemble empirical mode decomposition (EEMD)-fuzzy entropy (FE) models, the results show that as the number of the hidden layers increases, all the fault samples under different conditions are separated better by using SDAE rather than those feature extraction models mentioned. In addition, three evaluation indicators such as PC, CE, and classification accuracy are used to assess the performance of the method presented. Finally, the results show that the clustering effect of the method presented, and its classification accuracy are superior to those of the other combination models, including the SAE-fuzzy C-means (FCM)/Gustafson-Kessel (GK)/GG and EEMD-fuzzy entropy FE-PCA-FCM/GK/GG.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 73, December 2018, Pages 898-913
نویسندگان
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