by Prof. John Andrews
7th April, 2021
Risk Assessments performed on systems across many industrial sectors employ techniques such as Fault Tree Analysis and Event tree analysis which have their foundations back in the 1960/1970s. Since that time technology has advanced and system designs, their operating practice and maintenance strategies are now significantly different to those of the 1970s. Some of the restrictive assumptions such as: constant failure and repair rates for components, component failures being independent and the limited account of maintenance and renewal options in the component failure models employed, reduce the effectiveness of these methodologies to represent modern day systems. In addition, research into the risk prediction techniques has made considerable advances in their capabilities since the 1970s but these advances tend to have addressed each deficiency in isolation. Examples of significant advances are the Binary Decision Diagram (BDD) method of solving Fault Tree structures, improving both accuracy and efficiency, and the Petri net method which has been proven to be an effective means of predict system performance when complex maintenance and renewal strategies are employed. This presentation will describe the motivation, progress and methodologies used on a project to develop the next generation of risk assessment methods which is funded by Lloyd’s Register Foundation. The project aims to update the current risk assessment capabilities using a hybrid approach of methods including BDDs and Petri nets. This research addresses the deficiencies in the current approaches and extends their capabilities to better represent systems employed across the industries. The approaches developed will use the familiar causality structures of fault tree and event tree analysis, removing their traditional assumptions by changing the analysis methodologies employed. Industrial partners from the nuclear, aerospace and railway industries are collaborating on the project to ensure it meets their requirements.