Siqi Qiu

Modeling of ERTMS Level 2 as an SoS and Evaluation of its Dependability Parameters Using Statecharts

In this paper, we consider the European Rail Traffic Management System (ERTMS) as a System-of-Systems (SoS) and propose modeling it using Unified Modeling Language statecharts. We define the performance evaluation of the SoS in terms of dependability parameters and average time spent in each state (working state, degraded state, and failed state). The originality of this work lies in the approach that considers ERTMS Level 2 as an SoS and seeks to evaluate its dependability parameters by considering the unavailability of the whole SoS as an emergent property. In addition, human factors, network failures, Common-Cause Failures (CCFs), and imprecise failure and repair rates are taken into account in the proposed model.

Availability assessment of railway signalling systems with uncertainty analysis using Statecharts

In this paper, we propose an original simulation approach to evaluate the availability of systems in the presence of state uncertainty which arises from incompleteness or imprecision of knowledge and data. This approach is based on a simulation method combining the belief functions theory and the Statecharts. Then we propose a Statechart model of a railway signalling system, European Rail Traffic Management System (ERTMS) Level 2 considering state uncertainty, and evaluate its availability according to the RAMS requirements defined in the railway standards. Finally we propose a sensitivity analysis to estimate the state uncertainty of which constituent system has the most significant influence on the state uncertainty of the entire ERTMS Level 2.

A quantitative model for the risk evaluation of driver-ADAS systems under uncertainty

In this paper, a quantitative model is proposed to assess the probability of accidents occurring in driver-Advanced Driver Assistance Systems (ADAS) under uncertainty using Valuation-Based System (VBS). Two kinds of uncertainties are analyzed: data uncertainty related to the states of components, and model uncertainty related to the system structure. The components and the system structure are modeled using variables, spaces of variables, and a set of valuations represented by basic probability assignments (bpas). Besides, the positive influence of learning and cooperation processes is also quantified. Finally, the proposed method is applied to a real use case: the Car Navigation System (CNS).

Extended LK heuristics for the optimization of linear consecutive-k-out-of-n: F systems considering parametric uncertainty and model uncertainty

Abstract The optimization of the consecutive-k-out-of-n system is to find the optimal assignment of components that maximizes the system reliability. Many efforts have been devoted to the component assignment problems (CAPs) for such systems, however, uncertainties about the reliability data of components and the system structure have never been deeply studied in CAPs. Due to the insufficiency of relative data and system complexities, uncertainties inevitably exist in many real engineering problems. This paper extends the LK heuristic using a non-probabilistic graphical model called evidential network, and interval-valued importance measures to perform the optimization of linear consecutive-k-out-of-n: F systems under parametric uncertainty (related to the reliability data of components) and model uncertainty (related to the system structure). The extended LK heuristics are applied to an oil pipeline system and an illustrative example system to show their feasibility and applicability. The originality of this work lies in the extension of the CAPs to consider different kinds of uncertainties and the proposition of heuristics to find the near optimal assignment of components under uncertainties according to the preference level of the decision maker.

A valuation-based system approach for risk assessment of belief rule-based expert systems

Abstract Belief rules extend traditional IF-THEN rules to represent vagueness, incompleteness, and nonlinear causal relationships by assigning belief degrees to singletons or the universe of all possible values that the consequents of rules can take. First, this paper extends belief rules by assigning belief degrees to the subsets of all possible values that the consequents of rules with interval-valued rule weights can take. Then, this paper proposes a Valuation-Based System (VBS) approach for the modeling and risk assessment of extended belief rule-based expert systems. Finally, the proposed VBS approach is applied to two use cases for evaluating the occurrence probabilities of accidents: one is a car equipped with Automated Speed Control (ASC) using values from experts, and the other is hazardous material (hazmat) transportation accidents using real statistical data.

Dynamic Valuation-Based System for reliability assessment of systems

Abstract This paper describes dynamic Valuation-Based System (VBS) for reliability assessment of systems under uncertainty. The reliability data and dependencies between components are represented using variables, sample spaces of variables, a set of valuations represented by probabilities, and basic probability assignments (bpas) that map sample spaces of sets of variables to the set of valuations. The uncertainties considered here are related to the states of components and their dependencies. The imprecise reliability of systems under uncertainty is estimated by an interval composed of upper and lower bounds. The proposed dynamic VBS approach is finally applied on a valve system and compared to the classical Bayesian Network approach.