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      Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

      1 , 1 , 2 , 2 , 1
      Shock and Vibration
      Hindawi Limited

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          Abstract

          Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification.

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          Most cited references21

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          Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

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            An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

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              • Record: found
              • Abstract: not found
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              Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

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                Author and article information

                Contributors
                Journal
                Shock and Vibration
                Shock and Vibration
                Hindawi Limited
                1875-9203
                1070-9622
                October 1 2020
                October 1 2020
                : 2020
                : 1-14
                Affiliations
                [1 ]College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
                [2 ]Faculty of Agriculture, University of South Bohemia, Studentska 1668, Czech Republic
                Article
                10.1155/2020/8857307
                978e46bc-5c54-4118-9b87-c6a2a78cb4cd
                © 2020

                https://creativecommons.org/licenses/by/4.0/

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