System reliability analysis and assessment by means of graph models issued from Hasse diagram

For system that can be described by a reliability structure function, we present an approach to assess the systems reliability relying on concepts of graph models. This approach exploits the partial order relation on the set of system components’ states which is represented by the Hasse diagram. Representing the system state on the Hasse diagram, we obtain a ordered graph of system states and, so, this approach is a first step to unify the modeling of systems represented both by structure functions or stochastic processes. The monotony property of the reliability structure function of coherent systems allows us to automatically obtain this ordered graph of system states from only the knowledge of the minimal path-sets or minimal cut-sets of the system that can be obtained previously by logic differential calculus or other methods. In a first time, this approach is developed in the case of binary systems for which it gives a way to obtain a minimal disjoint form of their structure functions based on a reduction method of the ordered graph. Also, this ordered graph allows to develop an algorithm to directly obtain the analytical expression of system reliability without resorting to an intermediate Boolean polynomial. Next, this algorithm is extended to the case of non-coherent systems which no longer respect the monotony property of the structure function. Thereafter these graphs and this approach are extended to multi-state systems. The system probabilities to be in each of its progressive (perfect or degraded) operating states can be assessed using a proposed extension of the algorithm defined for binary systems, and consequently reliability of multi-state systems can be determined also. These algorithms are applied to some case studies, for both binary and multi-state systems, coherent or non-coherent, and the results compared with those computed using standard reliability block diagram or fault tree models.

Quantification of system reliability using the survival signature

The structure function describes the functioning of a system dependent on the states of its components, and is central to theory of system reliability. The survival signature is a summary of the structure function which is sufficient to derive the system's reliability function. Since its introduction in 2012, the survival signature has received much attention in the literature, with developments on theory, computation and generalizations.
This presentation will be an introductory overview of the survival signature, including some recent developments. We will also discuss considerations and challenges for practical use of survival signatures for large systems and networks.

Making Reliability Engineering SMARTER with Computational Intelligence

Computational Intelligence has a huge potential for improving reliability of systems and the safety of systems. Promising cost savings and efficiency in practical working environments it can facilitate tremendous progress in engineering systems. But this is not just a matter of making better CI methods; it has to fit in with daily practice and add value there. This paper addresses this point by briefly discussing of a vision of digital safety delivery for the future, enterprise architecture as a means to assess the added value of tools and a fairly straightforward approach to help CI designers assess the value of their systems in the wider enterprise environment: the SMARTER mnemonic for the design of projects.

Big Accidents and Markov’s Chains Based Assessment of Safety Critical IT-Systems

Objective of the talk is to discuss the influence of Big Data Analytics (BDA), Internet of Things (IoT)) on safety and security of human and industry domains. The benefits and limitations of application of BDA and IoT technologies in safety critical systems to avoid, monitor and minimize consequences of severe accidents are analysed. Markov’s chain (MC) based techniques for assessment of safety critical IT-systems (parametrization, models development and simulation results) and problems caused by complexity and accuracy are discussed. The examples of MC applications for safety critical systems (reactor trip system, accident monitoring system of NPP, IoT based health system) are considered.

Methods of reliability analysis based on logic differential calculus

Logic differential calculus is a mathematical methodology developed for analysis of dynamic properties of Boolean and logic functions. This methodology can also be used in reliability analysis to analyze properties of structure function. In this way, it allows us to find situations in which a component of the system is critical for operation of the system. Based on knowledge of these situations, we can evaluate importance of the system components and identify those that have the greatest or the least influence on system operation.
In this lecture, we will focus on the basic terms of reliability analysis and logic differential calculus. We will present how Boolean and logic derivatives can be computed based on the structure function and how they can be used in evaluation of importance of the system components.

Generation of Structure Function Based on Uncertain Data by Fuzzy Decision Tree

Any system analysis requires a mathematical representation of the system. In reliability engineering, such representation is called mathematical model of the system. One of these models is the structure function. The structure function determines system performance according to the states of its components. In this paper, the new method of structure function construction is considered. The fuzzy decision tree is used to transform data about real-world system behavior to structure function. The main novelty of this method is in the handling of incompletely specified and uncertain data.

New Challenges and Opportunities in Reliability Engineering of Complex Technical Systems

In this article, we discuss the impacts of technological transformations currently at work on reliability engineering of complex technical systems.
We consider transformations both in systems and in means to study them.
We review challenges to meet in order to manage the current technological paradigm shift.
We advocate the potential benefits of the so-called model-based approach in probabilistic risk assessment.
We exemplified this approach by presenting the S2ML+X modeling technology.