با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
شبکه های نورونی - neuron-networks
سال انتشار:
2020
عنوان انگلیسی مقاله:
Ensemble sparse supervised model for bearing fault diagnosis in smart manufacturing
ترجمه فارسی عنوان مقاله:
گروه نظارت پراکنده برای تشخیص خطا در تولید هوشمند ، مدل نظارت پراکنده
منبع:
Sciencedirect - Elsevier - Robotics and Computer Integrated Manufacturing, 65 (2020) 101920. doi:10.1016/j.rcim.2019.101920
نویسنده:
Fengli Zhanga,⁎, Jianxing Yana, Peilun Fua, Jinjiang Wanga,⁎, Robert X. Gaob
چکیده انگلیسی:
Machinery fault diagnosis is of great significance to improve the reliability of smart manufacturing. Deep
learning based fault diagnosis methods have achieved great success. However, the features extracted by different
models may vary resulting in ambiguous representation of the data, and even wasted time with manually selecting
the optimal hyperparameters. To solve the problems, this paper proposes a new framework named
Ensemble Sparse Supervised Model (ESSM), in which a typical deep learning model is treated as two phases of
feature learning and model learning. In the feature learning phase, the original data is represented to be a feature
matrix as non-redundant as possible by applying sparse filtering. Then, the feature matrix is fed into the model
learning phase. Regularization, dropout and rectified linear unit (ReLU) are used in the models neurons and
layers to build a sparse deep neural network. Finally, the output of the sparse deep neural network provides
feedback to the first phase to obtain better sparse features. In the proposed method, hyperparameters need to be
pre-specified and a python library of talos is employed to finish the process automatically. The proposed method
is verified using the bearing data provided by Case Western Reserve University. The result demonstrates that the
proposed method can capture the effective pattern of data with the help of sparse constraints and simultaneously
provide convenience for the operators with assuring performance.
Keywords: Sparse representation | Deep learning | Fault diagnosis
قیمت: رایگان
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