Electrical Engineering

Hongsheng Su This email address is being protected from spambots. You need JavaScript enabled to view it.1,2 and Li Chen1

1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou730070, China
2Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou 730070, China

 

Received: July 29, 2021
Accepted: October 26, 2021
Publication Date: December 6, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0017  


ABSTRACT

Due to rapid scientific and technical developments, condition-based maintenance (CBM) has been broadly applied in modern large-scale wind enterprises. However, in practice, many large-size modern wind plants still primarily use time-based maintenance (TBM) and only use CBM for auxiliary maintenance. Hence, this study proposes a scheme for combining TBM and CBM to maintain the same physical system, which can effectively make full use of the advantages and overcome the shortcomings of the two approaches. To achieve this aim, this study firstly analyzes the general operating characteristics of TBM and CBM and then establishes a unified stochastic differential equation (SDE) model. Moreover, by combining the SDE with field monitoring data, a proportional hazard model (PHM) is then proposed to solve this model. The results show that the stochastic model can better reflect the state of the device than ordinary differential equation. Model analysis shows that TBM depends on global information and possesses a fixed maintenance time-interval, whereas CBM relies only on the local sample track and generates sporadic repair times. This shows that TBM is a planned maintenance activity, whereas CBM is unplanned. And the system possesses better long-term global behavior under TBM, although there are prominent short-term benefits under CBM, indicating that the combination of the two strategies is necessary and effective. Finally, examples of the model’s application verify the effectiveness and correctness of the proposed model, which is suitable for the current maintenance modes in many enterprises.


Keywords: stochastic differential equation(SDE); condition-based maintenance(CBM); time-based maintenance(TBM); proportion hazard model(PHM); combination; state.


REFERENCES

  1. [1] G. W. E. Council, (2014) “Global wind report 2021."
  2. [2] F. Márquez, A. M. Tobias, J. Pérez, and M. Papaelias, (2012) “Condition monitoring of wind turbines: Techniques and methods" Renewable Energy 46: 169–178.
  3. [3] Z. Hameed, S. H. Ahn, and Y. M. Cho, (2010) “Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation" Renewable Energy 35(5): 879–894.
  4. [4] Z. Tian, T. Jin, B.Wu, and F. Ding, (2011) “Condition based maintenance optimization for wind power generation systems under continuous monitoring" Renewable Energy 36(5): 1502–1509.
  5. [5] P. Takoutsing, R. Wamkeue, M. Ouhrouche, F. Slaoui-Hasnaoui, T. Tameghe, and G. Ekemb, (2014) “Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges" Energies 7(4): 2595–2630.
  6. [6] X. Dong, X. Zhang, K. Zheng, and S. Wang, (2018) “Health status assessment of wind turbine based on combination weighting and cloud model" Acta Energiae Solaris Sinica 39(8): 2139–2146.
  7. [7] Q. Liu and J. Yang. “Fault diagnosis of wind turbine gearbox based on dual-tree complex wavelet and information entropy”. In: 2018 Prognostics and System Health Management Conference (PHM-Chongqing). 2018.
  8. [8] A. Rahman, E. Pasaribu, Y. Nugraha, F. Khair, K. Soebandrija, and D. I. Wijaya, (2020) “Industry 4.0 and society 5.0 through lens of condition based maintenance (CBM) and machine learning of artificial intelligence (MLAI)" IOP Conference Series: Materials Science and Engineering 852(1): 012022.
  9. [9] A. Sjb, A. Bmm, B. Wc, and C. Bv, (2019) “Condition monitoring of a steam turbine generator using wavelet spectrum based control chart" Reliability Engineering & System Safety 184: 13–20.
  10. [10] Y. Amirat, M. Benbouzid, E. Al-Ahmar, B. Bensaker, and S. Turri, (2009) “A brief status on condition monitoring and fault diagnosis in wind energy conversion systems" Renewable & Sustainable Energy Reviews 13(9): 2629–2636.
  11. [11] L. U. Susan, T. U. Yu-Chen, and L. U. Huitian. “Predictive condition-based maintenance for continuous deteriorating systems”. In: International Conference on Quality Reliability (ICQR2005). 2005.
  12. [12] D. Banjevic and A. Jardine, (2007) “Remaining useful life in condition based maintenance: Is it useful" Modelling in industrial maintenance and reliability: 7.
  13. [13] S. Sethiya, (2006) “Condition based maintenance (cbm)" Secy. to CME/WCR/JBP:
  14. [14] H. Li, P. Li, N. Gao, L. Niu, Y. Zhang, Y. Bao, and W. Xu. “Opportunistic maintenance model for wind turbine based on reliability constraint”. In: IOP Conference Series: Materials Science and Engineering. 366. 1. IOP Publishing. 2018, 012026.
  15. [15] L. L. Huang, Y. Fu, Y. Mi, J. L. Cao, and P. Wang, (2017) “A Markov-chain-based availability model of offshore wind turbine considering accessibility problems" IEEE Transactions on Sustainable Energy PP(4): 1–1.
  16. [16] Y. Kang and H. Su, (2015) “Reliability analysis and control strategy design on preventive maintenance of repairable equipment" International Journal of Control and Automation 8(5): 259–280.
  17. [17] H. Su, (2016) “Stochastic model analysis and control strategy design on preventive maintenance based on condition" International Journal of Control and Automation 9(3): 194–214.
  18. [18] H. Su, (2014) “Preventive maintenance model analysis based on condition" International Journal of Security and its Applications 8(4): 353–366.
  19. [19] Y. Kang. “Control strategy study on preventive maintenance of repairable complex power equipment based on condition monitoring". (phdthesis). Lanzhou Jiaotong University, 2015.
  20. [20] E. Byon, L. Ntaimo, and Y. Ding, (2010) “Optimal maintenance strategies for wind turbine systems under stochastic weather conditions" IEEE Transactions on Reliability 59(2): 393–404.
  21. [21] H. Su, D. Wang, and X. Duan, (2020) “Condition maintenance decision of wind turbine gearbox based on stochastic differential equation" Energies 13:
  22. [22] Z. Ma, D.Wang,W. Teng, and Y. Liu, (2019) “Enhanced POM strategy and its application to wind turbines" IET Renewable Power Generation 13(15):
  23. [23] H. Zhao and L. Zhang, (2014) “Preventive opportunistic maintenance strategy for wind turbines based on reliability" Proceedings of the Csee 34(22): 3777–3783.
  24. [24] Z. Tian and H. Liao, (2011) “Condition based maintenance optimization for multi-component systems using proportional hazards model" Reliability Engineering& System Safety 96(5): 581–589.
  25. [25] H. Zhao and L. Cheng, (2016) “Condition based maintenance strategy for gearbox of wind turbine considering multi-attribute value theory" Acta Energiae Solaris Sinica 37(005): 1125–1132.
  26. [26] F. C. Klebaner. Introduction to stochastic calculus with applications. World Scientific Publishing Company, 2012.
  27. [27] D. Perez and Y. Quintana, (2008) “A survey on the Weierstrass approximation theorem" Divulgaciones Matematicas 16(1): 231–247.
  28. [28] N. Chen, Y. Chen, Z. Li, S. Zhou, and C. Sievenpiper, (2011) “Optimal variability sensitive condition based maintenance with a Cox PH model" International journal of production research 49(7): 2083–2100.
  29. [29] A. Ben-Israel, (1966) “A Newton-Raphson method for the solution of systems of equations" Journal of Mathematical analysis and applications 15(2): 243–252.
  30. [30] F. Besnard, K. Fischer, and L. B. Tjernberg, (2012) “A model for the optimization of the maintenance support organization for offshore wind farms" IEEE Transactions on Sustainable Energy 4(2): 443–450.
  31. [31] K. Fischer, F. Besnard, and L. Bertling, (2011) “Reliability-centered maintenance for wind turbines based on statistical analysis and practical experience" IEEE Transactions on Energy Conversion 27(1): 184–195.
  32. [32] F. Ding and Z. Tian, (2011) “Opportunistic maintenance optimization for wind turbine systems considering imperfect maintenance actions" International Journal of Reliability, Quality and Safety Engineering 18(05): 463–481.
  33. [33] W. Jialiang, W. Guangliang, W. Zhenshan, et al. Reliability engineering of wind turbine. Chemical Industry Press, Beijing, 2010.
  34. [34] N. Georgescu-Roegen. The entropy law and the economic process. Harvard university press, 2013.

Latest Articles

Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education Management
Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education ManagementVolume 28, Issue 2 (In Press)

Read more: ...

River bank erosion prediction using multivariable linear regression
River bank erosion prediction using multivariable linear regressionVolume 27, Issue 12 (In Press)

Read more: ...

Optimization of Ultrasound-Assisted Pectin Extraction from Durian Rind
Optimization of Ultrasound-Assisted Pectin Extraction from Durian RindVolume 28, Issue 2 (In Press)

Read more: ...

Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTM
Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTMVolume 28, Issue 2 (In Press)

Read more: ...

Response Prediction Analysis of RC Frame Structures Using Support Vector Machine Algorithm
Response Prediction Analysis of RC Frame Structures Using Support Vector Machine AlgorithmVolume 28, Issue 2 (In Press)

Read more: ...

Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb core
Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb coreVolume 28, Issue 2 (In Press)

Read more: ...

Analyzing Glass Configurations For Energy Efficiency In Building Envelopes: A Comparative
Analyzing Glass Configurations For Energy Efficiency In Building Envelopes: A ComparativeVolume 28, Issue 2 (In Press)

Read more: ...

Assessment of the optimal gold recovery rate through heap leach extraction process from hydrothermal alteration zone ore
Assessment of the optimal gold recovery rate through heap leach extraction process from hydrothermal alteration zone oreVolume 28, Issue 2 (In Press)

Read more: ...

Research on the Time-Varying Relationship between Macroeconomic Variables and the Stock Market Volatility Based on TVP-VAR Model
Research on the Time-Varying Relationship between Macroeconomic Variables and the Stock Market Volatility Based on TVP-VAR ModelVolume 28, Issue 2 (In Press)

Read more: ...