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).
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An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
یک روش تشخیص خطای هوشمند با استفاده از آموزش غیرقابل نگهداری به سوی داده های مکانیکی بزرگ-2016 Intelligent fault diagnosis is a promising tool
to deal with mechanical big data due to its ability in rapidly
and efficiently processing collected signals and providing
accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted
depending on prior knowledge and diagnostic expertise.
Such processes take advantage of human ingenuity but
are time-consuming and labor-intensive. Inspired by the
idea of unsupervised feature learning that uses artificial
intelligence techniques to learn features from raw data,
a two-stage learning method is proposed for intelligent
diagnosis of machines. In the first learning stage of the
method, sparse filtering, an unsupervised two-layer neural
network, is used to directly learn features from mechanical
vibration signals. In the second stage, softmax regression is employed to classify the health conditions based
on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing
dataset, respectively. The results show that the proposed
method obtains fairly high diagnosis accuracies and is
superior to the existing methods for the motor bearing
dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes
intelligent fault diagnosis handle big data more easily.
Index Terms: Intelligent fault diagnosis | mechanical big data | softmax regression | sparse filtering | unsupervised feature learning |
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