Health index-based prognostics for remaining useful life predictions in electrical machines

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 4, 2016, Pages 2633-2644

  Yang, Feng ^a   Habibullah, Mohamed Salahuddin ^a   Zhang, Tianyou ^b   Xu, Zhao ^c   Lim, Pin ^d   Nadarajan, Sivakumar ^d  

^a INSTITUTE OF HIGH PERFORMANCE COMPUTING   (Singapore)

^b ACCENTURE   (United States)

^c NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^d ROLLS ROYCE SINGAPORE PTE LTD   (Singapore)

Author keywords

Electrical Machine; Health Index; Prognostics; Remaining Useful Life

Indexed keywords

FORECASTING; HEALTH; SYSTEMS ENGINEERING; THERMAL AGING;

ACCELERATED THERMAL AGING; DATA-DRIVEN PROGNOSTICS; ELECTRICAL MACHINE; HEALTH INDICES; PROGNOSTICS; RELIABILITY AND AVAILABILITY; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES;

ELECTRIC MACHINERY;

EID: 84963826974     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2016.2515054     Document Type: Article

References (38)

1. S. Yin, X. Li, H. Gao, and O. Kaynak, "Data-based techniques focused on modern industry: An overview," IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 657-667, Jan. 2015.
2. M. Riera-Guasp, J. Antonino-Daviu, and G.-A. Capolino, "Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art," IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1746-1759, Mar. 2015.
3. V. Hodge, S. O'Keefe, M. Weeks, and A. Moulds, "Wireless sensor networks for condition monitoring in the railway industry: A survey," IEEE Trans. Intell. Transp. Syst., vol. 16, no. 3, pp. 1088-1106, Jun. 2015.
4. H. Oh, B. Han, P. McCluskey, C. Han, and B. Youn, "Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review," IEEE Trans. Power Electron., vol. 30, no. 5, pp. 2413-2426, May 2015.
5. C. Yang et al., "Screening of false induction motor fault alarms produced by axial air ducts based on the space-harmonic-induced current components," IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1803-1813, Mar. 2015.
6. E. Fournier, A. Picot, J. Regnier, M. Yamdeu, J.-M. Andrejak, and P. Maussion, "Current-based detection of mechanical unbalance in an induction machine using spectral kurtosis with reference," IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1879-1887, Mar. 2015.
7. M. Hamadache, D. Lee, and K. Veluvolu, "Rotor speed-based bearing fault diagnosis (RSB-BFD) under variable speed and constant load," IEEE Trans. Ind. Electron., vol. 62, no. 10, pp. 6486-6495, Oct. 2015.
8. F. Ahmadzadeh and J. Lundberg, "Remaining useful life estimation: Review," Int. J. Syst. Assur. Eng. Manage., vol. 5, no. 4, pp. 461-474, 2014.
9. S. Liu, Y. Wang, and F. Tian, "Prognosis of underground cable via online data-driven method with field data," IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7786-7794, Dec. 2015.
10. Y. Qian and R. Yan, "Remaining useful life prediction of rolling bearings using an enhanced particle filter," IEEE Trans. Instrum. Meas., vol. 64, no. 10, pp. 2696-2707, Oct. 2015.
11. M. E. Sharp, "Prognostic approaches using transient monitoring methods," Ph.D. dissertation, Univ. Tennessee, Knoxville, TN, USA, 2012.
12. M. Yu, D. Wang, and M. Luo, "Model-based prognosis for hybrid systems with mode-dependent degradation behaviors," IEEE Trans. Ind. Electron., vol. 61, no. 1, pp. 546-554, Jan. 2014.
13. S. Biswas, A. Srivastava, and D. Whitehead, "A real-time data-driven algorithm for health diagnosis and prognosis of a circuit breaker trip assembly," IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3822-3831, Jun. 2015.
14. T. Loutas, D. Roulias, and G. Georgoulas, "Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic e-support vectors regression," IEEE Trans. Reliab., vol. 62, no. 4, pp. 821-832, Dec. 2013.
15. E. Strangas, S. Aviyente, and S. Zaidi, "Timecfrequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet ac motors," IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4191-4199, Dec. 2008.
16. K. Goebel, B. Saha, and A. Saxena, "A comparison of three data-driven techniques for prognostics," in Proc. 62nd Meeting Soc. Mach. Fail. Prev. Technol. (MFPT), Virginia Beach, VA, USA, 2008, pp. 119-131.
17. C. Hu, B. D. Youn, P. Wang, and J. T. Yoon, "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliab. Eng. Syst. Saf., vol. 103, pp. 120-135, 2012.
18. P. Baraldi, F. Mangili, and E. Zio, "A kalman filter-based ensemble approach with application to turbine creep prognostics," IEEE Trans. Reliab., vol. 61, no. 4, pp. 966-977, Dec. 2012.
19. T. Wang, J. Yu, D. Siegel, and J. Lee, "A similarity-based prognostics approach for remaining useful life estimation of engineered systems," in Proc. Int. Conf. Prognostics Health Manage. (PHM'08), Oct. 2008, pp. 1-6.
20. A. M. Riad, H. K. Elminir, and H. M. Elattar, "Evaluation of neural networks in the subject of prognostics as compared to linear regression model," Int. J. Eng. Technol., vol. 10, no. 6, pp. 52-56, Dec. 2010.
21. K. Liu, N. Gebraeel, and J. Shi, "A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis," IEEE Trans. Autom. Sci. Eng., vol. 10, no. 3, pp. 652-664, Jul. 2013.
22. Z. Xi, R. Jing, P.Wang, and C. Hu, "A copula-based sampling method for data-driven prognostics and health management," Reliab. Eng. Syst. Saf., vol. 132, pp. 2-82, Dec. 2013.
23. S. Zaidi, S. Aviyente, M. Salman, K.-K. Shin, and E. Strangas, "Prognosis of gear failures in dc starter motors using hidden markov models," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1695-1706, May 2011.
24. J. Sun, H. Zuo, W. Wang, and M. G. Pechtb, "Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance," Mech. Syst. Signal Process., vol. 28, pp. 585-596, Apr. 2012.
25. D.Wang and P.W. Tse, "Prognostics of slurry pumps based on a movingaverage wear degradation index and a general sequential monte carlo method," Mech. Syst. Signal Process., vols. 56-57, pp. 213-229, May 2015.
26. W. Liao and Y. Wang, "Data-driven machinery prognostics approach using in a predictive maintenance model," J. Comput., vol. 8, no. 1, pp. 225-231, Jan. 2013.
27. E. Bechhoefer, D. He, and P. Dempsey, "Gear health threshold setting based on a probability of false alarm," in Proc. Annu. Conf. Prognostics Health Manage. Soc., Montreal, QC, Canada, Sep. 2011, pp. 275-281.
28. D. He, E. Bechhoefer, P. Dempsey, and J. Ma, "An integrated approach for gear health prognostics," in Proc. AHS Int. 68th Annu. Forum Technol. Disp., Fort Worth, TX, USA, May 2012.
29. B. R. Upadhyaya, A. S. Erbay, and J. P. McClanahan, "Accelerated aging studies of induction motors and fault diagnostics," Maintenance Reliability Center, Univ. Tennessee, Knoxville, TN, USA, Tech. Rep., 1997.
30. IEEE Standard Test Procedure for Evaluation of Systems of Insulating Materials for Random-Wound AC Electric Machinery, IEEE Standard 117-2015, 1974 [Online]. Available: Http://standards.ieee.org/develop/project/117.html
31. L. Malambo and C. Heatwole, "A multitemporal profile-based interpolation method for gap filling nonstationary data," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 252-261, Jan. 2016.
32. Q. Wu, X. Yang, and Q. Zhou, "Pattern recognition and its application in fault diagnosis of electromechanical system," J. Inf. Comput. Sci., vol. 9, no. 8, pp. 2221-2228, 2012.
33. M. Me, "A review on applications of the wavelet transform technique in spectral analysis," J. Appl. Comput. Math., vol. 4, no. 4, pp. 1-6, 2015.
34. L. J. Hadjileontiadis, Lung Sounds: An Advanced Signal Processing Perspective. San Rafael, CA, USA: Morgan & Claypool, 2008.
35. S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ, USA: Prentice-Hall, 1999.
36. A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel, "Metrics for offline evaluation of prognostic performance," Int. J. Prognostics Health Manage., vol. 1, pp. 4-23, 2010.
37. J. A. Mendes-Moreira, C. Soares, A. M. Jorge, and J. F. D. Sousa, "Ensemble approaches for regression: A survey," ACM Comput. Surv., vol. 45, no. 1, Art. 10, 40 pp., Dec. 2012.
38. The MathWorks Inc., Matlab R2014b Document [Online]. Available: Http://www.mathworks.com/help/nnet/ref/trainlm.html, retrived on: Feb. 11, 2015.