Dependability Evaluation of Real Railway Interlocking Device

This paper describes the architecture of a safety system of the railway's interlocking equipment, which has been developed for Czech railways. The system will be used for the railway crossing gate. This system is based on FPGA blocks and has to fulfill the requirements for a fault tolerant system with a fail-safe function. The dual logic and TMR principle are used to increase its dependability. Several self-test and self-diagnostics features are used, such as an LFSR based built-in self-test, the FPGA readback and 1 out of 2 error detection codes. The functional logic uses a majority correction and the FPGA box reprogramming to precede the failure. The reliability analyses, models and reliability characteristics calculations of this system are described. Markov chain models are used for the reliability analyses. The TMR principles for fault tolerant system and the dual-TMR logic have been used in our design and both attempts are compared.

Fatigue reliability analysis under repeated inspections is carried out for the six structural members of bulk carrier. Crack initiation and propagation properties of the members, probability of crack detection by the visual inspection, target failure probabilities of the members and etc., which are essential information in the reliability analysis, are collected by the enquete asked to the naval engineers. Markov Chain Model is employed in the reliability analysis, in which fatigue failure is defined when crack length exceeds 200 mm or 500 mm.In the first half of the paper, the enquete results are summarized. The fatigue characteristics of the six members, the probability of crack detection by visual inspection, and the target reliability level of the members during 20 years' usage are made clear based on the expert's experiences. In the second half, reliability analyses are carried out using the enquete information. Effect of repeated inspections at the present condition on fatigue reliability, and the inspection intervals necessary to achieve the target failure probability are investigated for the six members.

In consideration of some drawbacks of stochastic reliability method in theoretical and practical aspects, as well as the existence of insufficient and vague data in actual equipments design, the possbilistic reliability models of typically general systems are constructed in this paper. Characterizing the systems(components) failure behavior by possibility measures in place of probability measures, treating the systems (components) lifetime as fuzzy variables in the possibility space, and introducing possibilistic approaches into system reliability modeling and analysis, the possibilistic reliability theories are advanced based on possibilistic measurements. By expanding the universe of discourse, the derivation of system reliability models is simplified without loss of the nature of the problems to be solved, and proofs and calculations of possibilistic reliability of typical systems including series, parallel, series-parallel, parallel-series, and cold standby systems are much more straightforward than the traditional ones. The detailed application examples in system reliability analysis verify a possibilistic design framework on the general reliability models background, which provide some technical supports and guarantees for possibilistic reliability design and possibilistic reliability management in the case of limited statistical data.

Simple SummaryUnderstanding and predicting vector population and related disease dynamics, is crucial for gaining insight into the abundance and dynamics of arthropod disease vectors, and for the design of effective vector control strategies. Several mathematical and standard epidemiological models have been proposed in studying vector transmitted infectious disease dynamics. However, most models are of deterministic nature and are not able to estimate other relevant metrics such as the probability of vector population emergence as well as the probability and expected time to reach certain population and/or infection state. Here we are focusing on stochastic modeling of mosquito abundance data using weather driven Markov chains (MCs) and are particularly interested in estimating transition probabilities (TPs) between different population levels. A MC model is based on the assumption that the future state of the variable is only dependent on the present state and is suitable in cases of short and noisy data characterized by a complex and random behavior. The aim is to introduce and generalize a formulation of conditional Markov chain models (CMSs) for predicting probability transition estimates of arthropod vector populations. In this context, first we present the basic principles and assumptions behind Markov chain modeling approach, with an intuitive interpretation of the integration of conditional Markov chains (CMCs) and then demonstrate the usefulness of the approach in predicting the abundance of Culex sp. We conclude that the conditional Markov chain technique is recommended as viable for modeling populations that explicit random dynamics and predict their future evolution. Although, the Markov models generated in this work provide an accurate abstraction of the vector disease progress observed within the dataset used for their generation, we envision the current approach as an entry point into the medical entomology literature and methods for predicting arthropod vector diseases dynamics.Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs.

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

This paper describes the analysis of dependability and predictive reliability. The proposed methodology is based on hierarchical models and the generally acclaimed standard MIL-HDBK 217F. The equipment is a real component of the railway interlocking system in Czech Republic. The equipment is designed for high dependability and with respect of disturbances caused by the near environment. A possible encapsulation using UML to model processes affecting the reliability is shown.

In this thesis, the properties of some non-standard Markov chain models and their corresponding parameter estimation methods are investigated. Several practical applications and extensions are also discussed.
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\nThe estimation of model parameters plays a key role in the real-world applications of Markov chain models. Some widely used estimation methods for Markov chain models are based on the existence of stationary vectors. In this thesis, some weaker sufficient conditions for the existence of stationary vectors for highorder Markov chain models, multivariate Markov chain models and high-order multivariate Markov chain models are proposed. Furthermore, for multivariate Markov chain models, a new estimation method based on minimizing the prediction error is proposed. Numerical experiments are conducted to demonstrate the efficiency of the proposed estimation methods with an application in demand prediction.
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\nHidden Markov Model (HMM) is a bivariate stochastic process such that one of the process is hidden and the other is observable. The distribution of observable sequence depends on the hidden sequence. In a traditional HMM, the hidden states directly affect the observable states but not vice versa. However, in reality, observable sequence may also have effect on the hidden sequence. For this reason, the concept of Interactive Hidden Markov Model (IHMM) is introduced, whose key idea is that the transitions of the hidden states depend on the observable states too. In this thesis, efforts are devoted in building a highorder IHMM where the probability laws governing both observable and hidden states can be written as a pair of high-order stochastic difference equations. We also propose a new model by capturing the effect of observable sequence on the hidden sequence through using the threshold principle. In this case, reference probability methods are adopted in estimating the optimal model parameters, while for unknown threshold parameter, Akaike Information Criterion (AIC) is used. We explore asset allocation problems from both domestic and foreign perspective where asset price dynamics follows autoregressive HMM. The object of an investor is not only to maximize the expected utility of the terminal wealth, but also to ensure that the risk of the portfolio described by the Value-at-Risk (VaR) does not exceed a specified level.
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\nIn many decision processes, fuzziness is a major source of imprecision. As a perception of usual Markov chains, the definition of fuzzy Markov chains is introduced. Compared to traditional Markov chain models, fuzzy Markov chains are relatively new and many properties of them are still unknown. Due to the potential applications of fuzzy Markov chains, we provide some characterizations to ensure the ergodicity of these chains under both max-min and max-product compositions.

The reliability assessment for an automobile crankshaft provides an important understanding in dealing with the design life of the component in order to eliminate or reduce the likelihood of failure and safety risks. The failures of the crankshafts are considered as a catastrophic failure that leads towards a severe failure of the engine block and its other connecting subcomponents. The reliability of an automotive crankshaft under mixed mode loading using the Markov Chain Model is studied. The Markov Chain is modelled by using a two-state condition to represent the bending and torsion loads that would occur on the crankshaft. The automotive crankshaft represents a good case study of a component under mixed mode loading due to the rotating bending and torsion stresses. An estimation of the Weibull shape parameter is used to obtain the probability density function, cumulative distribution function, hazard and reliability rate functions, the bathtub curve and the mean time to failure. The various properties of the shape parameter is used to model the failure characteristic through the bathtub curve is shown. Likewise, an understanding of the patterns posed by the hazard rate onto the component can be used to improve the design and increase the life cycle based on the reliability and dependability of the component. The proposed reliability assessment provides an accurate, efficient, fast and cost effective reliability analysis in contrast to costly and lengthy experimental techniques.

There are several reasons why maintenance managers need to ascertain, from time to time, the reliability profile of their production machines that are subject to constant use. This study furnishes the justifications by analyzing a 12-year maintenance data classified under machine time, downtime, idle time, running time, among others. Markov chain model as well as some reliability models – availability, mean time to failure, mean time between failures, hazard rate (λ) – were used to analyze the bulky data obtained from a soft drink bottling company based in Lagos, Nigeria. Our results suggest that the long run maintenance practice of the organization has caused between 87-94% of the machines/equipment to be in idle state while between 6-13% to be due for retirement. It is our candid opinion that if urgent action is not taken to whittle down the lingering trend the company will get to the brink of collapse. Keywords: Transition matrix, Absorbing state, Catastrophic Failure, Matrix Utilization Factors Nigerian Journal of Technological Research

One notable advantage of Model-Driven Architecture (MDA) method is that software developers could do sufficient analysis and tests on software models in the design phase, which helps construct high confidence on the expected software behaviors and performance, especially for safety-critical real-time software. Most existing literature of reliability analysis ignores the effects from those deadline requirements of tasks which are critical properties for real-time software and thus cannot be ignored. Considering the contradictory relationship between the deadline requirements and time costs of fault tolerance in real-time tasks, in this paper, we present a novel reliability model, which takes schedulability as one of the major factors affecting the reliability, to analyze reliability of the task execution model in real-time software design phase. The tasks in this reliability model has no restrictions on their distributions and thus could be distributed on a multiprocessor or on a distributed system. Furthermore, the tasks also define arrival rates of faults and fault-tolerant mechanisms to model the occurrences of non-permanent faults and the corresponding time costs of fault handling. By analyzing the probability of tasks still being schedulable in the worst-case execution scenario with faults occurring, reliability and schedulability are combined into an unified analysis framework, and two algorithms for reliability analysis are given. To make this reliability model more pragmatic, we also present an estimation technique for estimating the fault arrival rate of each task. We show through two case studies respectively the detailed derivation process under static-priority scheduling in a multiprocessor system and in the design process of avionics software, and then analyze the factors affecting the reliability analysis by setting up simulation experiments. When no assumptions of fault occurrences made on the task model, this reliability model regresses to a generic schedulability model.

ABSTRACTQuantitative damage detection in composite single lap joints during fatigue is difficult and not practical with many traditional experimental methods. The objective of this study is to combine a new experimental analysis method with existing stochastic models that is capable of taking the scatter inherent in composite fatigue data into account for better representation of damage evolution in single‐lap joints. This study presents a new quantitative approach where thermoelastic stress analysis (TSA), a remote sensing and non‐destructive evaluation technique, is used for in situ continuous monitoring for fatigue damage detection of Fibre Reinfoced Polymeric (FRP) S2‐glass/E733FR single‐lap joints. Image analysis algorithms are developed to capture the debonding fronts, and Markov Chain models (MCMs) are developed to capture the fatigue damage evolution in composite lap‐joints based on the experimental analysis. The TSA damage index at the last fatigue cycle is used to calibrate the MCMs, and damage predictions using the MCMs are then examined at different fatigue cycles. Methods to construct a predictive stochastic S–N curve utilizing the MCMs are discussed. The proposed IR‐TSA with MCMs is shown to be very effective in predicting the damage evolution and allowed constructing S–N curves that take the variability into account for damage in composite single‐lap joints.

Fusion reactors like the International Thermonuclear Experimental Reactor (ITER) are generally complex systems, which demand reliability analysis. Common-Cause Failures (CCF) are increasingly important in the reliability analysis of these systems because of the widespread redundancy or similar components in them. However, despite the wide research in CCF, there has been little research on the handling of asymmetries of CCF that is inevitable in ITER. A concept of Multi-Common-Cause Failures (MCCF) and its key assumptions are discussed in this paper. On the basis of MCCF and the assumptions, a transition method named Common-Cause Breakdown Structure (CCBS) was designed to manage the asymmetrical CCF. The CCBS method can be easily applied to most fault tree analysis codes because the CCF treated by CCBS can be handled by traditional CCF models. A redundant system example was modeled and calculated in the reliability and probabilistic safety analysis program RiskA developed by FDS Team. The analysis results for water pumps redundant system applied in Tokamak cooling water system show that CCBS method is adequate and effective.

Reliability analysis of hierarchical computer-based systems subject to common-cause failures

Reliability and safety analysis for systems of fusion device

We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60%). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.