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Equipment Health Assessment Based on Node Embedding

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

Equipment health assessment is a fundamental task in predictive equipment maintenance practice, which aims to predict the health of equipment based on information about the equipment and its operation, thus avoiding unexpected equipment failures. In the current context, equipment health assessment based on sequential deep learning methods is becoming more and more popular, however, such methods ignore the inter-device correlations, leading to their lack of readiness for health assessment of a large number of devices. To address this problem, this paper proposes a node-embedding-based device health assessment method, which creatively introduces a graph model for device health assessment and effectively improves the performance of health assessment. Firstly, this paper proposes a way to define equipment association graphs. Secondly, we introduce the node embedding technique to extract graph information. Finally, an equipment health assessment method based on the equipment association graph is proposed. Experiments show that the proposed method outperforms the existing prevailing methods.

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References

  1. An, D., Kim, N.H., Choi, J.H.: Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab. Eng. Sys. Saf. 133, 223–236 (2015)

    Article  Google Scholar 

  2. Awate, S.P.: Adaptive, nonparametric Markov models and information-theoretic methods for image restoration and segmentation. Ph.D. thesis, School of Computing, University of Utah (2006)

    Google Scholar 

  3. Benkedjouh, T., Medjaher, K., Zerhouni, N., Rechak, S.: Health assessment and life prediction of cutting tools based on support vector regression. J. Intell. Manuf. 26(2), 213–223 (2015)

    Article  Google Scholar 

  4. Chen, C., Liu, Y., Sun, X., Di Cairano-Gilfedder, C., Titmus, S.: An integrated deep learning-based approach for automobile maintenance prediction with gis data. Reliab. Eng. Syst. Saf. 216, 107919 (2021)

    Article  Google Scholar 

  5. Clifton, D.A., Clifton, L.A., Bannister, P.R., Tarassenko, L.: Automated novelty detection in industrial systems. In: Advances of Computational Intelligence in Industrial Systems, pp. 269–296. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-78297-1_13

  6. Coppe, A., Haftka, R.T., Kim, N.H.: Uncertainty Identification of Damage Growth Parameters Using Nonlinear Regression. AIAA J. 49(12), 2818–2821 (2011). https://doi.org/10.2514/1.J051268

    Article  Google Scholar 

  7. Fu, H., Liu, Y.: A deep learning-based approach for electrical equipment remaining useful life prediction. Auton. Intell. Syst. 2(1), 1–12 (2022)

    Google Scholar 

  8. Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity Conference (2016)

    Google Scholar 

  9. Gai, K., Qiu, M., Liu, M., Xiong, Z.: In-memory big data analytics under space constraints using dynamic programming. Fut. Gen. Comput. Syst. 83, 219–227 (2018). https://doi.org/10.1016/j.future.2017.12.033

    Article  Google Scholar 

  10. Gai, K., Zhang, Y., Qiu, M., Thuraisingham, B.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput , Early Access 1–12 (2022). https://doi.org/10.1109/TSC.2022.3192166

  11. Gai, K., et al.: Electronic health record error prevention approach using ontology in big data. In: IEEE 17th HPCC (2015)

    Google Scholar 

  12. Gao, X., Qiu, M.: Energy-based learning for preventing backdoor attack. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) Knowledge Science, Engineering and Management: 15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part III, pp. 706–721. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-10989-8_56

    Chapter  Google Scholar 

  13. Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(1), 226–236 (2011). https://doi.org/10.1109/TIM.2010.2047662

    Article  MathSciNet  Google Scholar 

  14. Jia, Z., Xiao, Z., Shi, Y.: Remaining useful life prediction of equipment based on xgboost. In: The 5th International Conference on Computer Science and Application Engineering. pp. 1–6 (2021)

    Google Scholar 

  15. Li, J., Ming, Z., Qiu, M., Quan, G., Qin, Xiao, C.: Tianzhou: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Archi. 57(9), 840–849 (2011). https://doi.org/10.1016/j.sysarc.2011.03.005

  16. Li, Y., Gai, K., Ming, Z., Zhao, H., Qiu, M.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. Multim. Comput. Commun. Appl. 12(4s), 1–18 (2016). https://doi.org/10.1145/2978575

  17. Liu, C., Tang, D., Zhu, H., Nie, Q.: A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access 9, 49557–49575 (2021). https://doi.org/10.1109/ACCESS.2021.3069256

    Article  Google Scholar 

  18. Liu, J., Pan, C., Lei, F., Hu, D., Zuo, H.: Fault prediction of bearings based on LSTM and statistical process analysis. Reliab Eng. Syst. Saf. 214, 107646 (2021)

    Article  Google Scholar 

  19. Liu, Q., Dong, M., Peng, Y.: A novel method for online health prognosis of equipment based on hidden semi-markov model using sequential monte carlo methods. Mech. Syst. Signal Process. 32, 331–348 (2012)

    Article  Google Scholar 

  20. Malhi, A., Yan, R., Gao, R.X.: Prognosis of defect propagation based on recurrent neural networks. IEEE Trans. Instrum. Meas. 60(3), 703–711 (2011)

    Article  Google Scholar 

  21. Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Process. 83(12), 2481–2497 (2003)

    Article  MATH  Google Scholar 

  22. Niu, J., Gao, Y., et al.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. J. Paralell. Distrib. Comput. 72(12), 1565–1575 (2012)

    Article  Google Scholar 

  23. Pecht, M.: Prognostics and health management of electronics. In: Encyclopedia of Structural Health Monitoring. Wiley (2009)

    Google Scholar 

  24. Qiu, H., Dong, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE IoT J. 8(13), 10327–10335 (2020)

    Google Scholar 

  25. Qiu, H., Kapusta, K., et al.: All-or-nothing data protection for ubiquitous communication: challenges and perspectives. Inf. Sci. 502, 434–445 (2019)

    Article  MathSciNet  Google Scholar 

  26. Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22, 4560–4569 (2020)

    Google Scholar 

  27. Qiu, M., Chen, Z., et al.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Sys. J. 11(2), 813–822 (2014)

    Article  Google Scholar 

  28. Qiu, M., Jia, Z., et al.: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. of Signal Proc, Syst. 46, 55–73 ((2007)

    Google Scholar 

  29. Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. In: ACM Symposium on Applied Computing, pp. 1637–1641 (2009)

    Google Scholar 

  30. Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE 6th International Conference on BigDataSecurity, pp. 94–99 (2020)

    Google Scholar 

  31. Qiu, M., Qiu, H., et al.: Secure data sharing through untrusted clouds with blockchain-enabled key management. In: 3rd SmartBlock Conference on Smart BlockChain (SmartBlock), pp. 11–16 (2020)

    Google Scholar 

  32. Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In: 2021 IEEE 19th International Conference on Embedded and Ubiquitous Computing (EUC), pp. 25–34 (2006)

    Google Scholar 

  33. Qiu, M., Xue, C., et al.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE Design, Automation Exhibition Conference, pp. 1–6 (2007)

    Google Scholar 

  34. Qiu, M., Yang, L., Shao, Z., Sha, E.: Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE Trans. Very Large Scale Inetegr. 18(3), 501–504 (2009)

    Article  Google Scholar 

  35. Qiu, M., et al.: RNA nanotechnology for computer design and in vivo computation. Philos. Trans. R.Soc. A: Math., Phy. Eng. Sci. 371(2000), 20120310 (2013)

    Article  Google Scholar 

  36. Qiu, M., Xue, C., Shao, Z., Sha, E.H.M.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: 2007 Design, Automation & Test in Europe Conference & Exhibition, pp. 1–6. IEEE (2007)

    Google Scholar 

  37. Roberts, S.J.: Novelty detection using extreme value statistics. IEE Proc. Vis. Image Signal Process. 146(3), 124–129 (1999)

    Article  Google Scholar 

  38. Seeger, M.: Gaussian processes for machine learning. Int. J. Neural Syst. 14(02), 69–106 (2004)

    Article  Google Scholar 

  39. Si, X.S., Wang, W., Hu, C.H., Chen, M.Y., Zhou, D.H.: A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech. Syst. Signal Process. 35(1–2), 219–237 (2013)

    Article  Google Scholar 

  40. Sutharssan, T., Stoyanov, S., Bailey, C., Yin, C.: Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms. J. Eng. 2015(7), 215–222 (2015)

    Article  Google Scholar 

  41. Wang, B., Lei, Y., Li, N., Yan, T.: Deep separable convolutional network for remaining useful life prediction of machinery. Mech. Syst. Signal Process. 134, 106330 (2019)

    Article  Google Scholar 

  42. Wang, D., Liu, K., Zhang, X.: A generic indirect deep learning approach for multisensor degradation modeling. IEEE Trans. Autom. Sci. Eng. 19, 1924–1940 (2021)

    Google Scholar 

  43. Wang, X., Balakrishnan, N., Guo, B., Jiang, P.: Residual life estimation based on bivariate non-stationary gamma degradation process. J. Stat. Comput. Simul. 85(2), 405–421 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  44. Wilson, A., Adams, R.: Gaussian process kernels for pattern discovery and extrapolation. In: International Conference on Machine Learning. pp. 1067–1075. PMLR (2013)

    Google Scholar 

  45. Wu, G., Zhang, H., Qiu, M., Ming, Z., Li, J., Qin, X.: A decentralized approach for mining event correlations in distributed system monitoring. J. Parallel Distrib Compu. 73(3), 330–340 (2013)

    Article  MATH  Google Scholar 

  46. Yan, J., Liu, Y., Han, S., Qiu, M.: Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine. Renew Sustain Energy Rev. 27, 613–621 (2013)

    Article  Google Scholar 

  47. Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., Wei, M.: A review on deep learning applications in prognostics and health management. IEEE Access 7, 162415–162438 (2019)

    Article  Google Scholar 

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Acknowledgements

This work was supported by State Grid Zhoushan Electric Power Supply Company of Zhejiang Power Corporation under grant No. B311ZS220002 (Research on hyperautomation for information comprehensive inspection).

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Authors and Affiliations

  1. State Grid Zhoushan Electric Power Supply Company of Zhejiang Power Corporation, Zhejiang, 316021, Zhoushan, China

    Jian Li, Xiao Chen, Chao Zhang, Hao Wu, Xin Yu, Shiqi Liu & Haolei Wang

Authors
  1. Jian Li
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  2. Xiao Chen
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  3. Chao Zhang
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  4. Hao Wu
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  5. Xin Yu
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  6. Shiqi Liu
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  7. Haolei Wang
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Corresponding author

Correspondence to Haolei Wang .

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Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Li, J. et al. (2023). Equipment Health Assessment Based on Node Embedding. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_11

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  • Online ISBN: 978-3-031-28124-2

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