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Automatic feature constructing from vibration signals for machining state monitoring

Author

Listed:
  • Yang Fu

    (Huazhong University of Science and Technology)

  • Yun Zhang

    (Huazhong University of Science and Technology)

  • Huang Gao

    (Huazhong University of Science and Technology)

  • Ting Mao

    (Huazhong University of Science and Technology)

  • Huamin Zhou

    (Huazhong University of Science and Technology)

  • Ronglei Sun

    (Huazhong University of Science and Technology)

  • Dequn Li

    (Huazhong University of Science and Technology)

Abstract

Machining state monitoring is an important subject for intelligent manufacturing. Feature construction is accepted to be the most critical procedure for a signal-based monitoring system and has attracted a lot of research interest. The traditional manual constructing way is skill intensive and the performance cannot be guaranteed. This paper presented an automatic feature construction method which can reveal the inherent relationship between the input vibration signals and the output machining states, including idling moving, stable cutting and chatter, using a reasonable and mathematical way. Firstly a large signal set is carefully prepared by a series of machining experiments followed by some necessary preprocessing. And then, a deep belief network is trained on the signal set to automatically construct features using the two step training procedure, namely unsupervised greedily layer-wise pertaining and supervised fine-tuning. The automatically extracted features can exactly reveal the connection between the vibration signal and the machining states. Using the automatic extracted features, even a linear classifier can easily achieve nearly 100% modeling accuracy and wonderful generalization performance, besides good repeatability precision on a large well prepared signal set. For the actual online application, voting strategy is introduced to smooth the predicted states and make the final state identification to ensure the detection reliability by taking consideration of the machining history. Experiments proved the proposed method to be efficient in protecting the workpiece from serious chatter damage.

Suggested Citation

  • Yang Fu & Yun Zhang & Huang Gao & Ting Mao & Huamin Zhou & Ronglei Sun & Dequn Li, 2019. "Automatic feature constructing from vibration signals for machining state monitoring," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 995-1008, March.
  • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1302-x
    DOI: 10.1007/s10845-017-1302-x
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Litak, Grzegorz & Sen, Asok K. & Syta, Arkadiusz, 2009. "Intermittent and chaotic vibrations in a regenerative cutting process," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 2115-2122.
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    Cited by:

    1. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    2. Jianfeng Tao & Chengjin Qin & Dengyu Xiao & Haotian Shi & Xiao Ling & Bingchu Li & Chengliang Liu, 2020. "Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1243-1255, June.
    3. Yu Wang & Mingkai Zhang & Xiaowei Tang & Fangyu Peng & Rong Yan, 2022. "A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1483-1502, June.

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