Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model
The method based on convolution neural networks (CNNs) has been widely developed and applied to residual life prediction, and many excellent results have been achieved. However, CNN models can only learn feature information relative to size, and it is difficult to extract complex time series feature...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/2/137 |
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| Summary: | The method based on convolution neural networks (CNNs) has been widely developed and applied to residual life prediction, and many excellent results have been achieved. However, CNN models can only learn feature information relative to size, and it is difficult to extract complex time series features from data of long time series. In addition, the existing models still have some problems, such as capturing the correlation of each time series and generating a large amount of redundant information. In order to alleviate the above problems, this study proposes a residual life prediction method of rolling bearings based on a multi-scale improved temporal convolutional network (MITCN) model. It is used to solve problems such as the low accuracy of bearing life prediction and the difficulty of the temporal convolutional network (TCN) model to capture the correlation of each time series. The model adopts the framework of a time convolution network and has good ability to extract time series information. By introducing a multi-scale expanded causal convolution residual structure, improved temporal convolutional network (ITCN) modules with different expansion factors capture information on different time scales and combine soft threshold functions and channel attention mechanisms to adaptively generate thresholds and eliminate redundant information. Finally, the carbon border adjustment mechanism (CBAM) is an attention mechanism used to enhance useful features and suppress useless features, so as to realize the effective fusion of multi-scale features. The IEEE PHM 2012 challenge data set is hereby used to verify the proposed method, which can effectively solve the problem of the low prediction accuracy of the remaining life of bearings. |
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| ISSN: | 2075-1702 |