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Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.
Czasopismo
Rocznik
Tom
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art. no. 165926
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
- School of information science and technology, Northwest University, China
- College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, China
autor
- College of mechanical and electrical engineering, Wenzhou University, China
autor
- Zhejiang Meishuo Electric Technology Co., Ltd, China
autor
- Zhejiang Meishuo Electric Technology Co., Ltd, China
autor
- Zhejiang Meishuo Electric Technology Co., Ltd, China
autor
- Zhejiang Meishuo Electric Technology Co., Ltd, China
autor
- College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, China
autor
- College of mechanical and electrical engineering, Wenzhou University, China
autor
- College of mechanical and electrical engineering, Wenzhou University, China
Bibliografia
- 1. Yan S, Shao HD, Xiao YM, Liu B, Wan JF. Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robotics and Computer-Integrated Manufacturing 2023; 79: 102441, https://doi.org/10.1016/j.rcim.2022.102441.
- 2. Wang HC, Sun W, Sun WF, Ren Y. A novel tool condition monitoring based on Gramian angular field and comparative learning. International Journal of Hydromechatronics 2023; 6(2): 93-107, https://doi.org/10.1504/IJHM.2022.10048957.
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- 5. Pimenov D Y, Bustillo A, Wojciechowski S, et al. Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. Journal of Intelligent Manufacturing 2023; 34: 2079-2121, https://doi.org/10.1007/s10845-022-01923-2.
- 6. Selvaraj V, Xu Z, Min S. Intelligent Operation Monitoring of an Ultra-Precision CNC Machine Tool Using Energy Data. International Journal of Precision Engineering and Manufacturing- Green Technology 2022; 10: 59-69, https://doi.org/10.1007/s40684-022-00449-5.
- 7. Zhen D, Li DK, Feng GJ, et al. Rolling bearing fault diagnosis based on VMD reconstruction and DCS demodulation. International Journal of Hydromechatronics 2022; 5(3): 205-225, https://doi.org/10.1504/IJHM.2022.10048012.
- 8. Xiao YM, Shao HD, Han SY, et al. Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE-ASME Transactions on Mechatronics 2022; 27(6): 5254-5263, https://doi.org/10.1109/TMECH.2022. 3177174.
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- 12. Ma M, Mao Z. Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction. IEEE Transactions on Industrial Informatics 2021; 17(3): 1658-1667, https://doi.org/10.1109/TII.2020.2991796.
- 13. Zhou YQ, Kumar A, Parkash C, et al. Development of entropy measure for selecting highly sensitive WPT band to identify defective components of an axial piston pump. Applied Acoustics; 2023; 203: 109225, https://doi. org/10.1016/j.apacoust.2023.109225.
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- 15. Zhang J, Kong X, Cheng L, et al, Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform multiscale feature fusion and improved channel attention mechanism. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2023: 25(1), 16, https://doi.org/10.17531/ein.2023.1.16.
- 16. Cheng YW, Lin MX, Wu J, et al. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowledge-Based Systems 2021; 216: 106796, https://doi.org/10.1016/j.knosys.2021.106796.
- 17. Navarro-Devia JH, Chen Y, Dao DV, et al. Chatter detection in milling processes- a review on signal processing and condition classification. International Journal of Advanced Manufacturing Technology 2023, https://doi.org/10.1007/s00170-023-10969-2.
- 18. Yi L, Jiang YJ, Zhang QC, et al.Remaining useful life prediction with insufficient degradation datbased on deep learning approach. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(4): 745-756, https://doi.org/10.17531/ein.2021.4.17.
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- 22. Zhuang FZ, Qi ZY, Duan KY, et al. A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE 2021; 109(1): 43-76, https://doi.org/10.1109/JPROC.2020.3004555.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
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