Reliability engineering and management primarily focuses on preventing and minimizing failures and their consequences, whether those failures gravely threaten safety, reduce mission effectiveness, or simply increase costs. Most engineering disciplines and systems can effectively use this body of knowledge. But statistical analysis of data is perhaps the area of reliability engineering that discourages the most people.
This book and associated Excel ® spreadsheet tools present basic probabilistic and statistical methods or tools used to extract information from reliability data to make sound decisions. The methods and tools presented can be applied across most technologies and industries, and the information and insights gained by doing so can help reduce whole-of-life costs, improve operating effectiveness, and improve safety. The intent of this book is to support people to get started to analyse reliability data, and to act as a refresher for those with more knowledge and experience.
The structure of the book is as follows. First, some underlying concepts are presented, to provide a necessary basis for later chapters. Then (often missing or provided as an afterthought in many reliability textbooks) a chapter covers the need to check whether the data set reflects independent and identical distributions (IID), for non-repairable items and especially for repairable items (and options to consider if they are not IID). Assuming IID can be reasonably assumed, the next part of the book outlines selecting the most appropriate probability distribution to represent the data, or whether no common distribution should be assumed and ‘non-parametric’ analysis only should be performed.
Assuming parametric models can be reasonably applied, the book then provides appropriate mathematical and Excel spreadsheet formulas to estimate parameters and confidence bounds (uncertainty) for the most common probability distributions used in reliability analysis. The final chapters provide an overview of analysing degradation, and introduce some more advanced reliability engineering techniques.
This book does not cover in any detail the planning and management of a reliability program, or with other reliability modeling or analysis techniques (apart from providing a foundation for understanding some of these). Further, this book focuses primarily on hardware and system reliability.
What are problems your book can help technical professionals solve?
- Reduce whole-of-life costs through optimising preventive maintenance and spare parts
- Improve mission or operations effectiveness
- Improve safety
What are important Features of your book and the Specific Benefits a buyer can expect to derive from those Features?
- Feature: Discusses potentially different data analysis approaches for non-repairable items and for repairable items
- Benefit: Avoid obtaining misleading conclusions from using the wrong method
- Feature: Includes Excel tools for General Renewal Process (GRP) analysis, Weibull analysis and assessing fits to common probability distributions
- Benefit: Skipping the number crunching allows focus on understanding and decision-making
- Feature: Little prior knowledge assumed.
- Benefit: Allows people to get started to analyse reliability data (and to act as a refresher for those with more knowledge and experience)
What are the audiences at which this book is aimed? How will they use this book?
- Anyone charged with improving equipment reliability – The book provides practical methods and tools to analyse reliability data and make sound decisions on reliability improvement focus areas
- Anyone charged with optimising logistics support – The book provides practical methods and tools to analyse reliability data and make sound decisions about spare parts requirements
- Reliability engineering students – Given that statistical analysis of data is perhaps one of the most difficult areas of reliability engineering, by providing practical methods and tools in a straightforward way the book can form a vital supplement for introductory reliability engineering courses.
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