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Intelligent identification of bolt looseness with one-dimensional deep convolutional neural networks

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Abstract

The bolt connection has characteristics of strong bearing capacity, easy maintenance and replacement, but the bolt connection structure often has loose failures during operation due to the detachability of the bolts. Bolts looseness and connection failure will not only affect the normal use of the mechanism, shorten the service life, and even cause casualties. Online monitoring and evaluation of bolt assembly tightness have attracted numerous interest. Automatic feature extraction plays a crucial role in intelligent state monitoring of mechanical systems, which can adaptively learn features from raw data and discover new state-sensitive features. A one-dimensional deep convolutional neural network (1D-DCNN) with eight convolutional layers and five pooling layers is proposed to achieve high precision in identification of bolt looseness. Firstly, the data overlap sampling is used to obtain the sufficient data so as to satisfy the requirements of 1D-DCNN. Then the 1D-DCNN carries out the process of feature extraction, feature selection and classification, which can take the free vibration signal of the bolt connection structure as input, and then fuse the feature extraction and assembly tightness classification process together to realize the intelligent detection of bolts looseness. The validity of the proposed method is verified by the data acquired from the free vibration excitation experiment of the bolt connection rotor of aero-engine. The results show that the adaptively learned features of the 1D-DCNN can represent the complex mapping relationship between the signal and the assembly state, and achieve higher accuracy than other methods.

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References

  1. Abdeljaber, O., Avci, O., Kiranyaz, S.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound. Vibration 388, 154–170 (2017)

    Article  MATH  Google Scholar 

  2. Chen, X., Lin, Q., Luo, C.: Neural feature search: A neural architecture for automated feature engineering. In: Wang, J., Shim, K., Wu, X. (eds.) 19th IEEE International conference on data mining (ICDM), pp. 71–80. IEEE; IEEE Comp Soc, Beijing (2019)

  3. Chen, Z., Li, C., Sanchez, R.-V.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibration 2015, 1–10 (2015)

    MATH  Google Scholar 

  4. Chen, S., Yu, J., Wang, S.: One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization. ISA Trans. 122, 424–443 (2022)

    Article  MATH  Google Scholar 

  5. Ding, X., He, Q.: Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Insrum. Measure. 66(8), 1926–1935 (2017)

    Article  MATH  Google Scholar 

  6. Hashemi, H., Abdelghany, K.: End-to-end deep learning methodology for real-time traffic network management. Computer-aided Civil Infrastruct. Eng. 33(10), 849–863 (2018)

    Article  MATH  Google Scholar 

  7. Ince, T., Kiranyaz, S., Eren, L.: Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans. Indus. Electron. 63(11), 7067–7075 (2016)

    Article  MATH  Google Scholar 

  8. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245, SI), 255–260 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  9. Junfeng, Z., Xiaoli, Z., Qiang, Y.: Dynamic feature learning and assembly tightness intelligent monitoring of bolted joint structure. Mech. Sci. Technol. Aerosp. Eng. 38, 351 (2019)

    MATH  Google Scholar 

  10. Le Cun, Y., Boser, B., Denker, J.S.: Handwritten digit recognition with a back-propagation network. In: Touretzky., D. (ed.) Proceedings of the 2nd international conference on neural information processing systems, vol. 2, pp. 396–404. MIT Press, Cambridge (1989)

  11. Li, X., Wang, S., Zhou, W.: Research on fault diagnosis algorithm based on convolutional neural network. In: 11th International conference on intelligent human-machine systems and cybernetics (IHMSC), vol. 1, pp. 8–12. IEEE Comp Soc, Zhejiang Univ, Hangzhou (2019)

  12. Liu, Z., Xinbo, H., Zhao, L.: Research on online monitoring technology for transmission tower bolt looseness. Measurement 223, 113703 (2023)

    Article  MATH  Google Scholar 

  13. Nguyen, T.-T., Ta, Q.-B., Ho, D.-D.: A method for automated bolt-loosening monitoring and assessment using impedance technique and deep learning. Develop. Built Environ. 14, 100122 (2023)

    Article  MATH  Google Scholar 

  14. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  MATH  Google Scholar 

  15. Salehi, H., Burgueño, R.: Emerging artificial intelligence methods in structural engineering. Eng. Struct. 171, 170–189 (2018)

    Article  MATH  Google Scholar 

  16. Sun, W., Yao, B., Zeng, N.: An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network. Materials 10(7), 790 (2017)

    Article  MATH  Google Scholar 

  17. Wang, B., Zhang, X., Fuyang, A.: Optimization of support vector machine and its application in intelligent fault diagnosis. J. Vibration, Measure Diagn. 37(3), 547–552 (2017)

    MATH  Google Scholar 

  18. Xu, J., Dong, J., Li, H., Zhang, C., Ho, S.C.: Looseness monitoring of bolted spherical joint connection using electro-mechanical impedance technique and BP neural networks. Sensors 19(8), 1906 (2019)

    Article  MATH  Google Scholar 

  19. Xie, S., Ren, G., Zhu, J.: Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings. Sci. Progr. 103(3), 36850420951394 (2020)

    Article  MATH  Google Scholar 

  20. Yuan, S.-F., Chu, F.-L.: Support vector machines and its applications in machine fault diagnosis. J. Vibration Shock. 26(11), 29–3558 (2007)

    MATH  Google Scholar 

  21. Yuan, R., Lv, Y., Kong, Q., Song, G.: Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and bp neural network. Smart Mater. Struct. 28(12), 125001 (2019)

    Article  MATH  Google Scholar 

  22. Zhang, W., Li, C., Peng, G.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439–453 (2018)

    Article  MATH  Google Scholar 

  23. Zhang, W., Peng, G., Li, C.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 90 (2017)

    Article  MATH  Google Scholar 

  24. Zhang, Y., Sun, X., Loh, K.J.: Autonomous bolt loosening detection using deep learning. Struct. Health Monitor.- Int. J. 19(1), 105–122 (2020)

    Article  MATH  Google Scholar 

  25. Zhao, R., Yan, R., Chen, Z.: Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115, 213–237 (2019)

    Article  MATH  Google Scholar 

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Funding

This work was supported by Shaanxi Science and Technology Association (Grant No.2021JM-169), Foundation of Equipment Pre-research Area (Grant No.6141A02033111) and Natural Science Foundation of Shaanxi Province (Grant No.2016JQ5030).

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Yong. Xia. and Junfeng.Zhao. wrote the main manuscript text and prepared figures. All authors reviewed the manuscript.

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Correspondence to XiaoLi Zhang.

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Zhang, X., Xia, Y. & Zhao, J. Intelligent identification of bolt looseness with one-dimensional deep convolutional neural networks. SIViP 19, 158 (2025). https://doi.org/10.1007/s11760-024-03752-7

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