Walter Schön

Interdisciplinary safety analysis of complex socio-technological systems based on the functional resonance accident model: An application to railway trafficsupervision

Abstract This paper presents an application of functional resonance accident models (FRAM) for the safety analysis of complex socio-technological systems, i.e. systems which include not only technological, but also human and organizational components. The supervision of certain industrial domains provides a good example of such systems, because although more and more actions for piloting installations are now automatized, there always remains a decision level (at least in the management of degraded modes) involving human behavior and organizations. The field of application of the study presented here is railway traffic supervision, using modern automatic train supervision (ATS) systems. Examples taken from railway traffic supervision illustrate the principal advantage of FRAM in comparison to classical safety analysis models, i.e. their ability to take into account technical as well as human and organizational aspects within a single model, thus allowing a true multidisciplinary cooperation between specialists from the different domains involved. A FRAM analysis is used to interpret experimental results obtained from a real ATS system linked to a railway simulator that places operators (experimental subjects) in simulated situations involving incidents. The first results show a significant dispersion in performances among different operators when detecting incidents. Some subsequent work in progress aims to make these “performance conditions” more homogeneous, mainly by ergonomic modifications. It is clear that the current human–machine interface (HMI) in ATS systems (a legacy of past technologies that used LED displays) has reached its limits and needs to be improved, for example, by highlighting the most pertinent information for a given situation (and, conversely, by removing irrelevant information likely to distract operators).

Risk assessment based on weak information using belief functions: a case study in water treatment

Whereas probability theory has been very successful as a conceptual framework for risk analysis in many areas where a lot of experimental data and expert knowledge are available, it presents certain limitations in applications where only weak information can be obtained. One such application investigated in this paper is water treatment, a domain in which key information such as input water characteristics and failure rates of various chemical processes is often lacking. An approach to handle such problems is proposed, based on the Dempster-Shafer theory of belief functions. Belief functions are used to describe expert knowledge of treatment process efficiency, failure rates, and latency times, as well as statistical data regarding input water quality. Evidential reasoning provides mechanisms to combine this information and assess the plausibility of various noncompliance scenarios. This methodology is shown to boil down to the probabilistic one where data of sufficient quality are available. This case study shows that belief function theory may be considered as a valuable framework for risk analysis studies in ill-structured or poorly informed application domains

Reliability assessment for multi-state systems under uncertainties based on the Dempster–Shafer theory

This article presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real- world MSSs, an insufficiency of data makes it difficult to estimate precise values for component state probabilities. The proposed approach applies the transferable belief model interpretation of the Dempster–Shafer theory to represent component state beliefs and to evaluate the MSS reliability indices. The example of an oil transmission system is used to demonstrate the proposed approach and it is compared with the universal generating function method. The value of the Dempster–Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts’ opinions as well as their independence.

Transferable belief model for reliability analysis of systems with data uncertainties and failure dependencies

Abstract Dealing with uncertainty adds a further level of complexity to problems of reliability analysis. The uncertainties which impact reliability studies usually involve incomplete or imprecise reliability data and complex failure dependencies. This paper proposes an original methodology based on the transferable belief model (TBM) to include failure dependencies between components in the evaluation of the reliability of the whole system, given both epistemic and aleatory uncertainties. First, based on expert opinion and experimental data, basic probability assignments (BPAs) are assigned to reliability data components. TBM operations are then used to obtain the reliability of the whole system, for series, parallel, series–parallel, parallel–series, and bridge configurations. Implicit, explicit, and discounting approaches are presented for taking account of failure dependencies. Finally, the proposed model is applied to take into account common cause failures (CCFs) in a case study.

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.

An Efficient Method for Reliability Analysis of Systems Under Epistemic Uncertainty Using Belief Function Theory

We present an efficient method based on the inclusion-exclusion principle to compute the reliability of systems in the presence of epistemic uncertainty. A known drawback of belief functions and other imprecise probabilistic theories is that their manipulation is computationally demanding. Therefore, we investigate some conditions under which the measures of belief function theory are additive. If this property is met, the application of belief functions is more computationally efficient. It is shown that these conditions hold for minimal cuts and paths in reliability theory. A direct implication of this result is that the credal state (state of beliefs) about the failing (working) behavior of components does not affect the credal state about the working (failing) behavior of the system. This result is proven using a reliability analysis approach based on belief function theory. This result implies that the bounding interval of the systems reliability can be obtained with two simple calculations using methods similar to those of classical probabilistic approaches. A discussion about the applicability of the discussed theorems for non-coherent systems is also proposed.

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.

Construction of Belief Functions From Statistical Data About Reliability Under Epistemic Uncertainty

Probability theory is well adapted to handle aleatory uncertainties resulting from the variability of failure phenomena. Recently, several uncertainty theories such as belief function theory were introduced in reliability assessments to handle epistemic uncertainties resulting from the lack of knowledge or insufficient data. In this paper, we propose some methods to construct belief functions of reliability parameters of components from statistical data about reliability. The proposed methods consider the parametric estimation of reliability parameters.

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

Abstract Multisensor perception has an important role in robotics and autonomous systems, providing inputs for critical functions including obstacle detection and localization. It is starting to appear in critical applications such as drones and ADASs (Advanced Driver Assistance Systems). However, this kind of complex system is difficult to validate comprehensively. In this paper we look at multisensor perception systems in relation to an alternative dependability method, namely fault tolerance. We propose an approach for tolerating faults in multisensor data fusion that is based on the more traditional method of duplication–comparison, and that offers detection and recovery services. We detail an example implementation using Kalman filter data fusion for mobile robot localization. We demonstrate its effectiveness in this case study using real data and fault injection.

On the study of human reliability in transportation systems of systems

Humans are and will remain one of the critical constituents of a technological system. The study of Human Factors is a broad domain with equally varying applications. Quantification thereof, with a Human Reliability Analysis (HRA) poses considerable challenges and advantages. In increasingly complex modern systems where large resources are allocated towards ensuring systems operational safety, it becomes necessary to analyze the actions of human operator who directly or indirectly influences system reliability. This paper envisages establishing a base towards a HRA model, to address existing issues. Railway systems and Advanced Driver Assistance Systems for automobiles are our application domains; we aim to identify the need of and usability in both. Human considered as a component of the System of Systems for risk assessment allows us to study its impact on system reliability and give feedback to improve system safety.