Pyramid LSTM Network for Tool Condition Monitoring
文献类型：期刊论文
| 作者 | Guo, Hao2,3; Lin, Xin1; Zhu, Kunpeng2 |
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2022 |
| 卷号 | 71 |
| 关键词 | Feature extraction Monitoring Milling Computational modeling Hidden Markov models Employee welfare Task analysis Auto-encoder network structure pyramid long short-term memory (LSTM) tool wear monitoring visual analysis |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2022.3173278 |
| 通讯作者 | Zhu, Kunpeng(zhukp@iamt.ac.cn) |
| 英文摘要 | Tool condition monitoring is important to guarantee the product quality and improve the productivity in high-performance computer numerical control (CNC) machining. Due to the harsh working conditions and non-stationary milling process, it is difficult to establish a monitoring model suitable for complex conditions. In this article, a pyramid long short-term memory (LSTM) auto-encoder is proposed to monitor tool wear. Unlike the classic stacked LSTM, the pyramid model is constructed based on the frequency spectrum of cutting signals. Each layer focuses on one periodic scale, and each unit focuses on one periodic fluctuation. The features are compressed layer by layer according to the frequency spectrum. The learned patterns are restricted by the spectrum-based structure, which simplifies the monitoring task and reduces the model complexity. The length of the cutting signal is also no longer limited by the memory capacity of LSTM. In the meantime, the efficiency of long-term signal processing is also greatly improved by reducing the number of units. In addition, the introduction of auto-encoder can further improve the accuracy of the model under complex working conditions through unsupervised learning. The good performance on high-speed milling experiments shows the accuracy of the model under unknown tools and milling parameters. Compared with the classic stacked LSTM, the pyramid LSTM auto-encoder has advantages in computational speed, stability, and prediction accuracy. |
| WOS关键词 | WEAR |
| 资助项目 | Chinese National Key Research and Development Project[2018YFB1703200] ; Chinese Ministry of Science and Technology |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000797417500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Chinese National Key Research and Development Project ; Chinese Ministry of Science and Technology |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/130913]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Zhu, Kunpeng |
| 作者单位 | 1.Wuhan Univ Sci & Technol, Sch Machinery & Automat, Wuhan 430081, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Changzhou 213164, Peoples R China 3.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Hao,Lin, Xin,Zhu, Kunpeng. Pyramid LSTM Network for Tool Condition Monitoring[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71. |
| APA | Guo, Hao,Lin, Xin,&Zhu, Kunpeng.(2022).Pyramid LSTM Network for Tool Condition Monitoring.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71. |
| MLA | Guo, Hao,et al."Pyramid LSTM Network for Tool Condition Monitoring".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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