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      A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

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          Abstract

          Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.

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          Most cited references 35

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          Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

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            Bearing fault diagnosis based on wavelet transform and fuzzy inference

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              Neural-network-based motor rolling bearing fault diagnosis

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                22 February 2017
                February 2017
                : 17
                : 2
                Affiliations
                State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, China; zw1993@ 123456hit.edu.cn (W.Z.); li_chuanhao@ 123456126.com (C.L.); cyh.wne@ 123456gmail.com (Y.C); zhangzhujun36@ 123456126.com (Z.Z.)
                Author notes
                [* ]Correspondence: pgl7782@ 123456hit.edu.cn ; Tel.: +86-451-8640-3820
                Article
                sensors-17-00425
                10.3390/s17020425
                5336047
                28241451
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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