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Fault diagnosis of rolling bearings based on CNN and LSTM networks under mixed load and noise

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Abstract

In recent years, the method of building models based on machine learning has achieved good results in the field of bearing fault diagnosis. However, due to the complexity and variability of the actual working environment, the collected rolling bearing vibration data not only comes from different loads, but also contains noise data. The existing models are unable to adapt to all operating environments and their fault diagnosis capabilities are significantly reduced especially when the collected data is noisy. In order to achieve higher fault diagnosis accuracy and robustness under different work conditions, a new fault diagnosis model 1LWCNNLSTM (One-layer wide convolutional and long-short term memory network) is proposed, which is a hybrid model based on convolutional neural network (CNN) and long-short term memory network (LSTM). Firstly, the model extracts features from the raw data using a wide convolutional kernel to attenuate the effect of noise, then fuses the features extracted from different convolutional kernels to generate a new sequence, and finally uses LSTM to learn the features in the new sequence. The impact of the model parameters is analyzed through extensive experiments and the proposed model has higher diagnostic accuracy under mixed load and noise when compared with existing models. Further analyses of model classification details through visualization techniques and confusion matrices demonstrate the high usability of the model. The experimental results show that the model proposed has better load generalization capability and noise immunity for the vibration data coming from the complex working environments.

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Data availability

Previously reported [CWRU] data were used to support this study and are available at [https://engineering.case.edu/bearingdatacenter/12k-drive-end-bearing-fault-data]. These prior studies (and datasets) are cited at relevant places within the text as references [16].

Previously reported [Jiangnan University] data were used to support this study and are available at [https://www.52phm.cn/blog/detail/52]. These prior studies (and datasets) are cited at relevant places within the text as references [11].

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  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    HaiBin Sun & YueGuang Fan

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Sun, H., Fan, Y. Fault diagnosis of rolling bearings based on CNN and LSTM networks under mixed load and noise. Multimed Tools Appl 82, 43543–43567 (2023). https://doi.org/10.1007/s11042-023-15325-w

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  • DOI: https://doi.org/10.1007/s11042-023-15325-w

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