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Developed CNN Model with Various Input Scale Data Evaluation for Bearing Faults Prognostics
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<div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87396</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Developed CNN Model with Various Input Scale Data Evaluation for Bearing Faults Prognostics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anas%20H.%20Aljemely">Anas H. Aljemely</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianping%20Xuan"> Jianping Xuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed. <iframe src="https://publications.waset.org/abstracts/142516.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing%20fault%20prognostics" title="bearing fault prognostics">bearing fault prognostics</a>, <a href="https://publications.waset.org/abstracts/search?q=developed%20CNN%20model" title=" developed CNN model"> developed CNN model</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-scale%20evaluation" title=" multiple-scale evaluation"> multiple-scale evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20features" title=" deep learning features"> deep learning features</a> </p> <a 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