Abstract
This chapter describes an overview of the probabilistic physics-of-failure for applications to reliability engineering problems. As reliability engineering experts face situations where system and component reliability failure data are lacking or with the poor quality, a powerful modeling approach is to relay on the underlying processes and phenomena that lead to failures. Originally derived from chemistry, mechanics, and metallurgy, the processes that lead to failures are called failure mechanisms that include phenomena such as fatigue, creep, and corrosion. Physics-of-failure is an empirically based mathematical and analytical approach to modeling these underlying processes of failures. Due to limitations of information and test data available for the understanding of these processes, the PoF-based reliability should include formal accounting of the uncertainties. The physics-of-failure methods in reliability engineering that consider uncertainties lead us to the probabilistic physics-of-failure. This chapter covers some important analytical and practical aspects of the probabilistic physics-of-failure modeling, including some examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Modarres, M., Amiri, M., & Jackson, C. (2017). Probabilistic physics of failure approach to reliability: Modeling, accelerated testing, prognosis and reliability (p. 288). Wiley-Scrivener.
Jaynes, E. T. (1965). Gibbs vs. Boltzmann Entropies. American Journal of Physics, 33, 391–398.
Dowling, N. (2012). Mechanical behavior of materials: Engineering methods for deformation, fracture, and fatigue (4th ed., p. 960). Boston: Pearson.
Dodson, G., & Howard, B. (1961). High stress aging to failure of semiconductor devices. In Proceedings of the Seventh National Symposium on Reliability and Quality Control. Philadelphia, PA.
Haggag, A., McMahon, W., Hess, K., Cheng, K., Lee, J., & Lyding, J. (2000). A probabilistic-physics-of-failure/short-time-test approach to reliability assurance for high-performance chips: Models for deep-submicron transistors and optical interconnects. In Proceedings of IEEE Integrated Reliability Workshop (pp. 179–182).
Hall, P., & Strutt, J. (2003). Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: A parametric study. Reliability Engineering & System Safety, 80(3), 233–242.
Azarkhail, M., & Modarres, M. (2007). A novel Bayesian framework for uncertainty management in physics-based reliability models. In Proceedings of ASME International Mechanical Engineering Congress and Exposition. Seattle, WA.
Matik, Z., & Sruk, V. (2008). The physics-of-failure approach in reliability engineering. In Proceedings of IEEE International Conference on Information Technology Interfaces (pp. 745–750). Dubrovnik, Croatia.
O’Connor, A. N., Modarres, M., & Mosleh, A. (2019). Probability distributions used in reliability engineering (p. 214). College Park: Center for Risk and Reliability.
Meeker, W., & Escobar, L. (1998). Statistical methods for reliability data (p. 712). Hoboken: Wiley.
NIST/SEMATECH e-Handbook of Statistical Methods. Downloaded from https://www.itl.nist.gov/div898/handbook/, on May 12, 2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Modarres, M. (2021). Probabilistic Physics-of-Failure Approach in Reliability Engineering. In: Misra, K.B. (eds) Handbook of Advanced Performability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-55732-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-55732-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55731-7
Online ISBN: 978-3-030-55732-4
eBook Packages: EngineeringEngineering (R0)