Stochastic model checking for predicting component failures and service availability

When a component fails in a critical communications service, how urgent is a repair? If we repair within 1 hour, 2 hours, or n hours, how does this affect the likelihood of service failure? Can a formal model support assessing the impact, prioritisation, and scheduling of repairs in the event of component failures, and forecasting of maintenance costs? These are some of the questions posed to us by a large organisation and here we report on our experience of developing a stochastic framework based on a discrete space model and temporal logic to answer them. We define and explore both standard steady-state and transient temporal logic properties concerning the likelihood of service failure within certain time bounds, forecasting maintenance costs, and we introduce a new concept of envelopes of behaviour that quantify the effect of the status of lower level components on service availability. The resulting model is highly parameterised and user interaction for experimentation is supported by a lightweight, web-based interface

On the Statistical Modeling and Analysis of Repairable Systems

We review basic modeling approaches for failure and maintenance data from repairable systems. In particular we consider imperfect repair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where several systems of the same kind are observed, we show how observed covariates and unobserved heterogeneity can be included in the models. We also consider various approaches to trend testing. Modern reliability data bases usually contain information on the type of failure, the type of maintenance and so forth in addition to the failure times themselves. Basing our work on recent literature we present a framework where the observed events are modeled as marked point processes, with marks labeling the types of events. Throughout the paper the emphasis is more on modeling than on statistical inference.Comment: Published at http://dx.doi.org/10.1214/088342306000000448 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Predictive Maintenance Modelling for Through-Life Engineering Services

Predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs

Statistical dependence of pipe breaks on explanatory variables

Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models

Towards Accurate Estimation of Error Sensitivity in Computer Systems

Fault injection is an increasingly important method for assessing, measuringand observing the system-level impact of hardware and software faults in computer systems. This thesis presents the results of a series of experimental studies in which fault injection was used to investigate the impact of bit-flip errors on program execution. The studies were motivated by the fact that transient hardware faults in microprocessors can cause bit-flip errors that can propagate to the microprocessors instruction set architecture registers and main memory. As the rate of such hardware faults is expected to increase with technology scaling, there is a need to better understand how these errors (known as ‘soft errors’) influence program execution, especially in safety-critical systems.Using ISA-level fault injection, we investigate how five aspects, or factors, influence the error sensitivity of a program. We define error sensitivity as the conditional probability that a bit-flip error in live data in an ISA-register or main-memory word will cause a program to produce silent data corruption (SDC; i.e., an erroneous result). We also consider the estimation of a measure called SDC count, which represents the number of ISA-level bit flips that cause an SDC.The five factors addressed are (a) the inputs processed by a program, (b) the level of compiler optimization, (c) the implementation of the program in the source code, (d) the fault model (single bit flips vs double bit flips) and (e)the fault-injection technique (inject-on-write vs inject-on-read). Our results show that these factors affect the error sensitivity in many ways; some factors strongly impact the error sensitivity or SDC count whereas others show a weaker impact. For example, our experiments show that single bit flips tend to cause SDCs more than double bit flips; compiler optimization positively impacts the SDC count but not necessarily the error sensitivity; the error sensitivity varies between 20% and 50% among the programs we tested; and variations in input affect the error sensitivity significantly for most of the tested programs

Assessing the Risk due to Software Faults: Estimates of Failure Rate versus Evidence of Perfection.

In the debate over the assessment of software reliability (or safety), as applied to critical software, two extreme positions can be discerned: the ‘statistical’ position, which requires that the claims of reliability be supported by statistical inference from realistic testing or operation, and the ‘perfectionist’ position, which requires convincing indications that the software is free from defects. These two positions naturally lead to requiring different kinds of supporting evidence, and actually to stating the dependability requirements in different ways, not allowing any direct comparison. There is often confusion about the relationship between statements about software failure rates and about software correctness, and about which evidence can support either kind of statement. This note clarifies the meaning of the two kinds of statement and how they relate to the probability of failure-free operation, and discusses their practical merits, especially for high required reliability or safety