A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction
文献类型：期刊论文
| 作者 | Li, Yuan1,2; Wang, Huanjie1,2 ; Li, Jingwei1,2; Tan, Jie1,2
|
| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 2022-11-15 |
| 卷号 | 22期号:22页码:21806-21815 |
| 关键词 | Hidden Markov models Feature extraction Predictive models Sensors Mathematical models Data models Adaptation models 2-D long short-term memory (2D-LSTM) fault occurrence time (FOT) detection information fusion unit (IFU) remaining useful life (RUL) prediction |
| ISSN号 | 1530-437X |
| DOI | 10.1109/JSEN.2022.3202606 |
| 通讯作者 | Tan, Jie(jie.tan@ia.ac.cn) |
| 英文摘要 | Remaining useful life (RUL) prediction plays a significant role in prognostic and health management (PHM), and it can reduce the cost of unwanted failures and improve the reliability of industrial equipment and systems. In recent years, deep learning and sensor technology have boosted fault detection accuracy. This article proposes a two-stage prediction method based on 2-D long short-term memory (2D-LSTM) fusion networks with multisensor data for RUL prediction. This method first uses the Wilson Amplitude (WAMP) feature to automatically detect the fault occurrence time (FOT) and divide the bearing's degradation process into two stages: health and degradation state. Then a 2D-LSTM fusion network is employed to predict the RUL of bearings, including multiple subnetworks. In each subnetwork, deep temporal features of a single sensor's data are extracted by 2D-LSTM, which can capture both vertical and horizontal dependencies of data. Furthermore, an information fusion unit (IFU) is created to help the model incorporate features captured from each 2D-LSTM subnetwork. Experiments on two real-world bearing datasets show that our model's effectiveness is comparable to that of other existing methods. In addition, ablation studies are performed to verify the requirement and efficacy of each component of our proposed model. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORK ; FAULT DIAGNOSTICS ; MODEL |
| 资助项目 | National Natural Science Foundation of China[U1801263] ; National Natural Science Foundation of China[62003344] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000882008500048 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51260]  |
| 专题 | 综合信息系统研究中心_工业智能技术与系统 |
| 通讯作者 | Tan, Jie |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yuan,Wang, Huanjie,Li, Jingwei,et al. A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction[J]. IEEE SENSORS JOURNAL,2022,22(22):21806-21815. |
| APA | Li, Yuan,Wang, Huanjie,Li, Jingwei,&Tan, Jie.(2022).A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction.IEEE SENSORS JOURNAL,22(22),21806-21815. |
| MLA | Li, Yuan,et al."A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction".IEEE SENSORS JOURNAL 22.22(2022):21806-21815. |
入库方式： OAI收割
来源：自动化研究所
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