Название: Engineering Reliability and Risk Assessment
Автор: Harish Garg, Mangey Ram
Издательство: Elsevier
Серия: Advances in Reliability Science
Год: 2023
Страниц: 272
Язык: английский
Формат: pdf (true)
Размер: 13.5 MB

Engineering Reliability and Risk Assessment explains how to improve the performance of a system using the latest risk and reliability models. Against a backdrop of increasing availability of industrial data, and ever-increasing global commercial competition, the standards for optimal efficiency with minimum hazards keep improving. Topics explained include Effective strategies for the maintenance of the mechanical components of a system, How to schedule necessary interventions throughout the product life cycle, How to understand the structure and cost of complex systems, Planning a schedule to improve the reliability and life of the system, software, system safety and risk informed asset management, and more.

Failure analysis is an important and challenging aspect of the study of complex systems. A system is defined to be consisting of components, subsystems, inputs, and outputs within system boundaries. The inputs provide physical resources and information to the subsystems, which are interacting among each other to produce some outputs. All interactions are assumed to take place within system boundaries. A complex system can be defined as a system structure that is composed of usually a large number of components that have complex interactions. Any failure in performing the required interactions among system components, or any failure in getting the expected output/result, is considered to be contributing to system failure. Thus, analysis of a system with its components is a crucial step in determining the difficulties and complexities that the system will experience at any stage. However, in the real world, performance of both inputs and subsystems is affected by probabilistic uncertainty, and hence, a failure may come with an associated probability. The main goal of this chapter is to evaluate the probability of failure of complex systems, while finding the failure causes using Bayesian Networks (BNs). For any given system with its inputs and subsystems, probabilistic failure analysis depends on finding the probability of not getting the required or estimated output of that system.

The required output of the BN analysis may be the effect that is produced from certain causes (i.e., prediction reasoning), or the determination of the cause responsible for certain results and effects (i.e., diagnostic reasoning), both in probabilistic measures. Thus, determining the causeeeffect relation is an important first step in the probabilistic failure analysis, which allows for better understanding to enhance the system reliability and take decisions for mitigating the negative effects or better enhancing the causes. In this chapter, the graph representation of systems is conducted using BNs, which allow for representing marginal, conditional, and joint probability measures affecting system components; BN analysis provides the ability to decompose a large system into a manageable number of subsystems for their own analysis and in the end aggregating these results to provide the whole system results. Representing systems of engineering applications using BNs is affected by multiple factors that affect the probabilistic quantification process. The aim of this chapter is to reveal the different approaches that facilitate the probabilistic quantification of BNs and hence, facilitate prediction of system failures.