Rolling bearing fault diagnosis based on wireless sensor network data fusion

Author:

Hu Jie,Deng Sier

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference25 articles.

1. A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions;Wang;Int. J. Prod. Res.,2020

2. Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings;Pan;Int. J. Mech. Sci.,2013

3. Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS;Helmi;IET Electr. Power Appl.,2019

4. Effect of the coupling strength on the nonlinear synchronization of a single-stage gear transmission;González;Nonlinear Dynam.,2016

5. Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS;Helmi;IET Electr. Power Appl.,2019

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1. MARNet: Multi-head attention residual network for rolling bearing fault diagnosis under noisy condition;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2024-07-25

2. Bearing fault diagnosis method based on the Gramian angular field and an SE-ResNeXt50 transfer learning model;Insight - Non-Destructive Testing and Condition Monitoring;2023-12-01

3. Rolling bearing fault diagnosis method by using feature extraction of convolutional time-frequency image;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2023-11-03

4. Research on Data Acquisition and Reconstruction Algorithm of Sensor Layer in Internet of Things Based on BP Neural Network;2023 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC);2023-09-25

5. A New One-Dimensional Convolutional Neural Network Model for Detecting Motor Bearing Failures;Fırat Üniversitesi Mühendislik Bilimleri Dergisi;2023-09-01

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