Assessment of semi-parametric proportional intensity models applied to recurrent failure data with multiple failure types for repairable-system reliability.

Certain systems experience a substantial period of downtime due to performing maintenance following a major failure. This discontinuity in observation time has been a concern in the accuracy of estimating the covariate effect. Therneau and Hamilton (1997) proposed a discontinuous risk-free-intervals method for biomedical applications that could also apply to this engineering problem. This study has recommended the more favorable engineering applications range. Major and minor failure events are commonly seen in industry, but most researchers have pooled them as though they are identical. Lin (1993, 1994) proposed a covariate PI modeling approach to handle multiple failure types. This study has examined covariate PI modeling as an approach for explicit treatment of two recurrent failure types (major and minor).The class of semi-parametric proportional intensity (PI) models applies to recurrent failure event modeling for a repairable system with covariates. Abundant federal funding received in biostatistics/medical research has advanced the PI models to become well developed and widely referenced. PI models for medical applications could also apply to recurring failure/repair data in engineering problems. Wider engineering use of these models requires better understanding of applications, performance, and methods to accommodate important situations such as censoring, maintenance intervals, and multiple failure types.Landers and Soroudi (1991), Qureshi et al. (1994), and Landers et al. (2001) have examined robustness of the Prentice-Williams-Peteson-gap time (PWP-GT) model for the case of an underlying Non-homogeneous Poisson Process (NHPP) with power-law and log-linear intensity functions and complete (uncensored) data. However, the phenomenon of censoring is generally present in field data. This research has extended their work to the important case of right-censorship and has examined other semi-parametric PI models (PWP-total time (PWP-TT), Andersen-Gill (AG), and Wei-Lin-Weissfeld (WLW)).The PWP-GT and AG models prove to outperform the PWP-TT and WLW models in the robustness studies on right-censoring severity and multiple failure types. The results of examining the PI models in the discontinuous risk-free-intervals modeling indicate that the PWP-GT model performs better in the short overhaul duration. The AG model performs consistently well in the small sample size (20) regardless of the overhaul duration in a HPP case

Temporary Disability and Economic Incentives

We investigate the impacts of economic incentives on the duration and outcome of temporary disability insurance (TDI) spells. The analysis is based on a large quasi‐experiment taking place in Norway, involving a complete overhaul of the TDI benefit system. Our findings show that the labour supply of TDI claimants does respond to both the benefit level and the level of local labour demand. The estimated elasticity of the transition rate to employment with respect to the benefit level is −0.33. We also find that the TDI benefit level significantly affects the transition rate to alternative social insurance programmes.acceptedVersio

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Link to publishers version: 10.1016/j.ress.2016.03.004We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating condition

On some inferential problems with recurrent event models

Recurrent events (RE) occur in many disciplines, such as biomedical, engineering, actuarial science, sociology, economy to name a few. It is then important to develop dynamic models for their modeling and analysis. Of interest with data collected in a RE monitoring are inferential problems pertaining to the distribution function F of the time between occurrences, or that of the distribution function G of the monitoring window, and their functionals such as quantiles, mean. These problems include, but not limited to: estimating F parametrically or nonparametrically; goodness of fit tests on an hypothesized family of distributions; efficient of tests; regression-type models, or validation of models that arise in the modeling and analysis of RE. This dissertation work focuses on several inferential problems of significant importance with these types of data. The first one we dealt with is the problem of informative monitoring. Informative monitoring occurs when G contains information about F, and the information is accounted for in the inferential process through a Lehman-type model, 1 - G= (1 -F )ß, so called generalized Koziol-Green model in the literature. We propose a class of inferential procedures for validating the model. The research work proceeds with the development of a flexible, random cells based chi-square goodness of fit test for an hypothesized family of distributions with unknown parameter. The cells are random in the sense that they are cut free, are function of the data, and are not predetermined in advance as is done in standard chi-square type tests. A minimum chi-square estimator is used to construct the test statistic whose power is assessed against a sequence of Pitman-like alternatives. The last problem we considered is that of an efficiency, optimality, and comparison of various statistical tests on RE that are derived in this work and existed in the literature. The efficiency and optimality are obtained by extending the theory of Bahadur and Wieand to RE. Asymptotic properties of the different estimators and or statistics are presented via empirical processes tools. Small sample results using intensive simulation study of the various procedures are presented, and these show good approximation of the truth. Real recurrent event data from the engineering and biomedical studies are utilized to illustrate the various methods --Abstract, page iv

Regional index of sustainable economic well-being development project: final report

This report presents results from a development project carried out by nef (the new economics foundation) on behalf of emda (the East Midlands Development Agency) and Natural England, to improve the methodologies used in the calculation of the R-ISEW (Regional Index of Sustainable Economic Well-Being)

Dynamic pricing of general insurance in a competitive market

A model for general insurance pricing is developed which represents a stochastic generalisation of the discrete model proposed by Taylor (1986). This model determines the insurance premium based both on the breakeven premium and the competing premiums offered by the rest of the insurance market. The optimal premium is determined using stochastic optimal control theory for two objective functions in order to examine how the optimal premium strategy changes with the insurer’s objective. Each of these problems can be formulated in terms of a multi-dimensional Bellman equation. In the first problem the optimal insurance premium is calculated when the insurer maximises its expected terminal wealth. In the second, the premium is found if the insurer maximises the expected total discounted utility of wealth where the utility function is nonlinear in the wealth. The solution to both these problems is built-up from simpler optimisation problems. For the terminal wealth problem with constant loss-ratio the optimal premium strategy can be found analytically. For the total wealth problem the optimal relative premium is found to increase with the insurer’s risk aversion which leads to reduced market exposure and lower overall wealth generation

Prognostics and Health Management of Industrial Equipment

ISBN13: 9781466620957Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL)

Probabilistic Life Cycle Assessment of Safety Valves

The objective of this this thesis is to develop a probabilistic model for assessing the life cycle performance of safety valves used in the nuclear piping system. The life cycle performance is quantified in terms of reliability and life cycle cost in a given operating interval of the plant. A key input to the probabilistic lifecycle analysis is the lifetime distribution of the component in question. The second important element is the estimation of costs of inspection and in-service testing of components as well as costs of repairs and replacement of failed components. Based on this information, the life cycle analysis aims to predict the reliability and expected cost of operating a component in a future time interval. This study illustrates how to develop methods and algorithms for probabilistic assessment of the life cycle of safety valves used in the nuclear piping system. For statistically estimated parameters of the probability distributions of lifetime and various costs, historical operating data are required. This study uses about 20 years of historical data obtained from a group of temperature control valves used in the moderator system of a reactor. A maximum likelihood-based method is developed to estimate parameters of the lifetime distribution of a valve. The lifetime is defined as the time of first leakage in the valve since the time of installation. The ML method is based to consider complete and censored lifetime information. The distribution of repairs cost is also estimated by the ML method. The proposed method is applied to predict reliability and life cycle cost for various operating interval. This model can also be used to optimize the overhaul interval of the valve

Elites or Masses? A Structural Model of Policy Divergence, Voter Sorting and Apparent Polarization in U.S. Presidential Elections, 1972-2008

One of the most widely discussed phenomena in American politics today is the perceived increasing partisan divide that splits the U.S. electorate. A central contested question is whether this diagnosis is actually true, and if so, what is the underlying cause. We develop a model that relates the parties’ positions on economic and “cultural” issues, the voters’ ideal positions and the electorate’s voting behavior, and apply the model to U.S. presidential elections between 1972 and 2008. The model allows us to recover candidates’ positions from voter behavior; to decompose changes in the overall political polarization of the electorate into changes in the distribution of voter ideal positions and consequences of elite polarization; and to determine the characteristics of voters who changed their party allegiance.polarization, differentiated candidates, policy divergence, ideology, voter migration