Reliability Assessment of Legacy Safety-Critical Systems Upgraded with Fault-Tolerant Off-the-Shelf Software

This paper presents a new way of applying Bayesian assessment to systems, which consist of many components. Full Bayesian inference with such systems is problematic, because it is computationally hard and, far more seriously, one needs to specify a multivariate prior distribution with many counterintuitive dependencies between the probabilities of component failures. The approach taken here is one of decomposition. The system is decomposed into partial views of the systems or part thereof with different degrees of detail and then a mechanism of propagating the knowledge obtained with the more refined views back to the coarser views is applied (recalibration of coarse models). The paper describes the recalibration technique and then evaluates the accuracy of recalibrated models numerically on contrived examples using two techniques: u-plot and prequential likelihood, developed by others for software reliability growth models. The results indicate that the recalibrated predictions are often more accurate than the predictions obtained with the less detailed models, although this is not guaranteed. The techniques used to assess the accuracy of the predictions are accurate enough for one to be able to choose the model giving the most accurate prediction

Assessing the reliability of diverse fault-tolerant software-based systems

We discuss a problem in the safety assessment of automatic control and protection systems. There is an increasing dependence on software for performing safety-critical functions, like the safety shut-down of dangerous plants. Software brings increased risk of design defects and thus systematic failures; redundancy with diversity between redundant channels is a possible defence. While diversity techniques can improve the dependability of software-based systems, they do not alleviate the difficulties of assessing whether such a system is safe enough for operation. We study this problem for a simple safety protection system consisting of two diverse channels performing the same function. The problem is evaluating its probability of failure in demand. Assuming failure independence between dangerous failures of the channels is unrealistic. One can instead use evidence from the observation of the whole system's behaviour under realistic test conditions. Standard inference procedures can then estimate system reliability, but they take no advantage of a system’s fault-tolerant structure. We show how to extend these techniques to take account of fault tolerance by a conceptually straightforward application of Bayesian inference. Unfortunately, the method is computationally complex and requires the conceptually difficult step of specifying 'prior' distributions for the parameters of interest. This paper presents the correct inference procedure, exemplifies possible pitfalls in its application and clarifies some non-intuitive issues about reliability assessment for fault-tolerant software

Reliability Analysis of Discrete Lifetime Distributions

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Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.Comment: Published at http://dx.doi.org/10.1214/088342306000000330 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Continuous low-dose antibiotic prophylaxis for adults with repeated urinary tract infections (AnTIC): a randomised, open-label trial

Funder: UK National Institute for Health Research. Open Access funded by Department of Health UK Acknowledgments We thank all the participants for their commitment to the study, Sheila Wallace for updating the systematic review, members of the Trial Steering Committee and members of the Data Monitoring Committee for their valuable guidance. We thank the National Health Service organisations, principal investigators and local research staff who hosted and ran the study at site. We thank the Health Technology Assessment Programme of the UK NIHR for funding the study (no. 11/72/01). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the UK Government Department of Health. A full report of the study30 has been published by the NIHR Library.Peer reviewedPublisher PD

STATISTICAL ISSUES IN EFFICACY EVALUATION FOR COMPANION ANIMAL DRUG DEVELOPMENT

Companion animals, commonly called pets, are animals such as dogs, cats, and horses. The companion animal drug market has expanded rapidly in recent years. Two major points of focus in companion animal drug development are therapeutics and parasiticides. From a statistics point of view, experimental design, experimental unit determination, sample size estimation and reestimation, treatment design, data transformation, multiple testing, and proper modeling are major statistical issues when efficacy evaluation in a companion animal study is conducted. These major statistical issues are addressed using two clinical studies as examples: Reconcile® (Fluoxetine) for the treatment of separation anxiety in dogs and Comfortis® (Spinosad) for the control of fleas in dogs

Superimposed Renewal Processes in Reliability

This paper reviews the existing literature on the superimposed renewal process, with its foci on probabilistic and statistical properties, statistical inference, and applications in reliability analysis and maintenance policy optimisation. It then proposes future research topics

Incorporating anthropogenic influences into fire probability models : effects of human activity and climate change on fire activity in California

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state's fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change

Retrospective Evaluation of the Five-Year and Ten-Year CSEP-Italy Earthquake Forecasts

On 1 August 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP) launched a prospective and comparative earthquake predictability experiment in Italy. The goal of the CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented eighteen five-year and ten-year earthquake forecasts to the European CSEP Testing Center at ETH Zurich. We considered the twelve time-independent earthquake forecasts among this set and evaluated them with respect to past seismicity data from two Italian earthquake catalogs. In this article, we present the results of tests that measure the consistency of the forecasts with the past observations. Besides being an evaluation of the submitted time-independent forecasts, this exercise provided insight into a number of important issues in predictability experiments with regard to the specification of the forecasts, the performance of the tests, and the trade-off between the robustness of results and experiment duration. We conclude with suggestions for the future design of earthquake predictability experiments.Comment: 43 pages, 8 figures, 4 table