Abstract
Many systems experience gradual degradation while simultaneously being exposed to a stream of random shocks of varying magnitude that eventually cause failure when a shock exceeds the residual strength of the system. This failure mechanism is found in diverse fields of application. Lee and Whitmore Shock-degradation failure processes and their survival distributions. Manuscript submitted for journal publication, 2016) presented a family of system failure models in which shock streams that follow a Fréchet process are superimposed on a degrading system described by a stochastic process with stationary independent increments. They referred to them as shock-degradation failure models. In this article, we discuss applications of these models and investigate practical issues and extensions that help to make these models more accessible and useful for studies of system failure. This family has the attractive feature of defining the failure event as a first passage event and the time to failure as a first hitting time (FHT) of a critical threshold by the underlying stochastic process. FHT models have found use in many real-world settings because they describe the failure mechanism in a realistic manner and also naturally accommodate regression structures. This article discusses a variety of data structures for which this model is suitable, as well as the estimation methods associated with them. The data structures include conventional censored survival data, data sets that combine readings on system degradation and failure event times, and data sets that include observations on the timing and magnitudes of shocks. This assortment of data structures is readily handled by threshold regression estimation procedures. Predictive inferences and risk assessment methods are also available. This article presents three case applications related to osteoporotic hip fractures in elderly women, divorces for cohorts of Norwegian couples, and deaths of cystic fibrosis patients. This article closes with discussion and concluding remarks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aalen OO, Borgan O, Gjessing HK (2008) Survival and event history analysis: a process point of view. Statistics for biology and health. Springer, New York
Aaron SD, Stephenson AL, Cameron DW, Whitmore GA (2015) A statistical model to predict one-year risk of death in patients with cystic fibrosis. J Clin Epidemiol 68(11):1336–1345. doi: 10.1016/j.jclinepi.2014.12.010
Gumbel EJ (1953) Probability table for the analysis of extreme-value data: introduction. Applied mathematics series, vol 22. National Bureau of Standards, United States Department of Commerce, Washington, DC, pp 1–15
He X, Whitmore GA, Loo GY, Hochberg MC, Lee M-LT (2015) A model for time to fracture with a shock stream superimposed on progressive degradation: the Study of Osteoporotic Fractures. Stat Med 34(4):652–663. doi: 10.1002/sim.6356
Lee M-LT, Whitmore GA (2006) Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Stat Sci 21:501–513
Lee M-LT, Whitmore GA (2010) Proportional hazards and threshold regression: their theoretical and practical connections. Lifetime Data Anal 16:196–214
Lee M-LT, Whitmore GA (2016) Shock-degradation failure processes and their survival distributions, manuscript under review
Lee M-LT, Whitmore GA, Rosner BA (2010) Threshold regression for survival data with time-varying covariates. Stat Med 29:896–905
Mamelund S-E, Brunborg H, Noack T. Divorce in Norway 1886–1995 by calendar year and marriage cohort. Technical report 97/19. Statistics Norway
Acknowledgements
Mei-Ling T. Lee is supported in part by NIH Grant R01 AI121259. We thank research colleagues in the fields of osteoporosis, cystic fibrosis and chronic obstructive pulmonary disease for making us aware of the important role that physical traumas and acute exacerbations play in initiating critical medical events in their fields. Given our previous awareness of the importance of shock processes in causing failure in engineering systems, it was not difficult for us to see the shock-degradation process as a general failure mechanism and to anticipate its natural extension to social and economic systems as well.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Lee, ML.T., Whitmore, G.A. (2017). Practical Applications of a Family of Shock-Degradation Failure Models. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-5194-4_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5193-7
Online ISBN: 978-981-10-5194-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)