Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales ro...

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Bibliographic Details
Published in:	Shock and vibration Vol. 2021; no. 1
Main Authors:	Zhang, Xiaochen, Cong, Yiwen, Yuan, Zhe, Zhang, Tian, Bai, Xiaotian
Format:	Journal Article
Language:	English
Published:	Cairo Hindawi 2021 John Wiley & Sons, Inc Hindawi Limited
Subjects:	Accuracy Analysis Artificial neural networks Bearings Condition monitoring Data processing Fault detection Fault diagnosis Feature extraction Methods Neural networks Performance degradation Principal components analysis Roller bearings Time series
Online Access:	Get full text
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Summary:	Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN-AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.
ISSN:	1070-9622 1875-9203
DOI:	10.1155/2021/6660243