Andrew Kelleher

Photo of Andrew Keleher

Andrew Kelleher is a Materials and Systems Reliability Engineer with many years of industrial experience (1999 to 2021) in diverse fields of safety and reliability engineering at renowned companies in Australia, England, and Germany, including: The Welding Institute, ExxonMobil, QinetiQ Aerostructures, Bayer and Covestro.

Andrew founded RAMS Mentat GmbH in 2021 and developed an innovative technical and systems engineering approach that enables the reliability and safety performance of an entire production system to be optimized with consideration of capital investment, operational and maintenance cost constraints.

RAMS Mentat GmbH provides consulting, software, training, and project support with the aim of transforming your reliability improvement journey from a random walk into a chase.

Links:

Company Webpage: https://rams-mentat.com/

LinkedIn: https://www.linkedin.com/in/andrew-kelleher-ab5602127/

Contact Information:

Email: info@rams-mentat.com

Andrew is the author of the Process Plant Reliability Engineering article series.

Whilst most reliability literature is concerned with “product” reliability engineering, the described methods (e.g., Weibull analysis and FMEA) find relatively little application in a process plant environment. At first glance, the reason for this would seem to be the ratio of (many) Assets to (few) Engineers. However, the real reason is much more interesting. It is because the traditional methods were developed for application in “simple” and “complicated” systems, whereas a process plant is a “complex” system. Our response to this situation has been to trivialize the complex system behavior, for example in the form of a risk matrix. An alternative response would be to apply methods suited for application in complex systems. For example, simulation is absolutely necessary to make optimal decisions in complex systems. The aim of this article series is to present concepts, methods, and tools for tackling the reliability engineering problem in the context of a complex stochastic production system.