Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-24T17:51:30.594Z Has data issue: false hasContentIssue false

The Characteristic of Cumulative Damage Study about Electrical Insulation Based on Accelerated Life Tests

Published online by Cambridge University Press:  07 August 2013

Y.-H. Yang*
Affiliation:
Department of Mechanical Engineering, National Central University, Jhongli City, Taiwan 32001, R.O.C.
Y.-T. Tsai
Affiliation:
Department of Mechanical Engineering, De-Lin Institute of Technology, Taipei, Taiwan 23654, R.O.C.
K.-S. Wang
Affiliation:
Department of Mechanical Engineering, National Central University, Jhongli City, Taiwan 32001, R.O.C.
Get access

Abstract

In this study the Maximum Likelihood Estimator is utilized to identify the characteristics of failure of class-H insulation by considering accelerated life test data under censored situations from Nelson. The hazard rate function is considered in terms of the reliability, h(R), so-called AE model. The AE model is used to model the failures which are expressed as the serial connection between three modes, namely the turn, phase, and ground. This is the so-called competing failure. The main concern in the present investigation relates to the characteristic of changes in cumulative damage with temperature. The characteristic of the damage process basically change, with less capability of cumulation. The failure tends to be unpredictable in a constant hazard rate situation in much higher temperature environments. The parameters of the model are related to the temperature and follow the Arrhenius law. The numerical results indicate that the AE model is well fitted to the data and gives more information to identify the failure modes with fewer parameters. This is better than the using Weibull distribution with both parameters varied with temperature. According to the predicted lifetime, the turn needs to be rearranged primarily, followed by the phase. The ground mode only has influence on the failure at much higher temperatures.

Type
Research Article
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Dakin, T. W., “Electrical Insulation Deterioration Treated as a Chemical Rate Phenomenon,” Transactions of the American Institute of Electrical Engineers, 67, pp. 113122 (1948).CrossRefGoogle Scholar
2.Nelson, W., “Graphical Analysis of Accelerated Life Test Data with a Mix of Failure Modes,” IEEE Transactions on Reliability, R-24, pp. 230237 (1975).CrossRefGoogle Scholar
3.Nelson, W., “Theory and Applications of Hazard Plotting for Censored Failure Data,” Technometrics, Special 40th Anniversary Issue, 42, pp. 1225 (2000).Google Scholar
4.Dempster, A. P., Laird, N. M. and Rubin, D. B., “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 39, pp. 138 (1977).Google Scholar
5.Beyler, C. L. and Hirschler, M. M., Thermal Decomposition of Polymers, The SFPE Handbook of Fire Protection Engineering (Section 1, Chapter 7), NFPA, Quincy, Massachusetts 7 (1988).Google Scholar
6.Standard Test Method for Rapid Thermal Degradation of Solid Electrical insulating Materials By Thermogravimetric Method, ASTM D3850 — 94 (reapproved in 2000).Google Scholar
7.Li, L., Bowler, N., Hondred, P. R. and Kesseler, M. R., “Statistical Analysis of Electrical Breakdown Behavior of Polyimide Following Degrading Processes,” IEEE Transactions on Dielectrics and Electrical Insulation, 18, pp. 19551962 (2011).CrossRefGoogle Scholar
8.Nelson, W., Accelerated Testing, Statistical Model, Test Plans, and Data Analyses, John Wiley & Sons, Inc., New York (1990).CrossRefGoogle Scholar
9.Seo, J. H., Jung, M. and Kim, C. M., “Design of Accelerated Life Test Sampling Plans with a Nonconstant Shape Parameter,” European Journal of Operational Research, 197, pp. 659666 (2009).CrossRefGoogle Scholar
10.Yang, Y. H. and Wang, K. S., “Study of the Characteristics About Insulation Damage Based on the Accelerated Life Tests,” Eksploatacja I Niezawodność - Maintenance And Reliability. (accepted)Google Scholar
11.Wang, K. S., Chang, S. T. and Shen, Y. C., “Dynamic Reliability Models for Fatigue Crack Growth Problem,” Journal of Engineering Fracture Mechanics, 54, pp. 543556 (1996).CrossRefGoogle Scholar
12.Wang, K. S., Hsu, F. S. and Liu, P. P., “Modeling the Bathtub Shape Hazard Rate Function in Terms of Reliability,” Reliability Engineering & System Safety, 75, pp. 397406 (2002).CrossRefGoogle Scholar
13.Wang, K. S., Chen, C. S. and Huang, J. J., “Dynamic Reliability Behavior for Sliding Wear of Carburized Steel,” Reliability Engineering & System Safety, 58, pp. 3141 (1997).CrossRefGoogle Scholar
14.Wang, K. S., Lin, W. S. and Hsu, F. S., “A New Approach for Determining the Reliability of Cutting Tool,” International Journal of Advanced Manufacturing Technology, 17, pp. 705709 (2001).CrossRefGoogle Scholar
15.Wang, K. S., “Study of Hazard Rate Functions on the Cumulative Damage Phenomenon”, Journal of Mechanics, 27, pp. 4755. (2011)Google Scholar
16.Rao, S. S., Reliability-Based Design, McGraw-Hill, New York (1993).Google Scholar
17.Kapur, K. C. and Lamberson, L. R., Reliability in Engineering Design, John Wiley & Sons, N.Y. (1977).Google Scholar
18.Winsor, C. P., “The Gompertz Curve as a Growth Curve,” Proceedings of the National Academy of Sciences, 18, pp. 18 (1932).Google Scholar
19.IEEE Standard Test Procedure for Evaluation of Systems of Insulating Materials for Random-Wound AC Electric Machinery (1976).Google Scholar