An overview of fault tree analysis and its application in model based dependability analysis

By Sohag Kabir. Expert systems with applications, 2017

389 citations recorded by the source corpus.

I provide an overview of the Fault Tree Analysis method.I review different extensions of fault trees.A number of model-based dependability analysis approaches are reviewed.I outline the future outlook for model-based dependability analysis. Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty.

Subfields: Safety, Risk, Reliability and Quality, Software, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0661bff87369b5c62497993c578ccb60a9dac038

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Risk, Reliability and Quality

1. Shuangyao Zhao
2. Xiaoan Tang
3. Xiaonong Lu
4. Ting Huang
5. Qiang Zhang

In Software

1. Shuangyao Zhao
2. Xiaoan Tang
3. Xiaonong Lu
4. Ting Huang
5. Qiang Zhang

In Statistics and Probability

1. Shuangyao Zhao
2. Xiaoan Tang
3. Xiaonong Lu
4. Ting Huang
5. Qiang Zhang

Citations in the corpus

• A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems (2021, methodology): “Traditional fault diagnosis methods, such as physics of failure (Li et al. 2018a, b; Yang et al. 2013; Zhu et al. 2016) and fault tree analysis (Kabir 2017), generally focus on the operating mechanism or theoretical analysis of systems.”