Methods for Reliability Improvement and Risk Reduction

Reliability is one of the most important attributes for the products and processes of any company or organization. This important work provides a powerful framework of domain–independent reliability improvement and risk reducing methods which can greatly lower risk in any area of human activity. It reviews existing methods for risk reduction that can be classified as domain–independent and introduces the following new domain–independent reliability improvement and risk reduction methods:

Separation Stochastic separation Introducing deliberate weaknesses Segmentation Self–reinforcement Inversion Reducing the rate of accumulation of damage Permutation Substitution Limiting the space and time exposure Comparative reliability models

The domain–independent methods for reliability improvement and risk reduction do not depend on the availability of past failure data, domain–specific expertise or knowledge of the failure mechanisms underlying the failure modes. Through numerous examples and case studies, this invaluable guide shows that many of the new domain–independent methods improve reliability at no extra cost or at a low cost. 

Using the proven methods in this book, any company and organisation can greatly enhance the reliability of its products and operations. 

Michael Todinov has a background in mechanical engineering, applied mathematics and computer science. He received his PhD and his higher doctorate (DEng) from the University of Birmingham and is currently a professor in mechanical engineering in Oxford Brookes University, UK. Professor Todinov is an internationally acclaimed expert in reliability and risk. In 2017, he received the prestige IMechE award in the area of risk reduction in mechanical engineering. He has published four research monographs and a large number of research papers in the area of reliability and risk. His name is associated with developing theoretical and computational frameworks for analysis and optimisation of repairable flow networks and for reliability analysis based on the cost of failure.