Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches. (January 2017)

• Record Type: Journal Article
• Authors: Smith, Doug; Veitch, Brian; Khan, Faisal; Taylor, Rocky
• Is Part Of: Journal of loss prevention in the process industries. Volume 45(2017:Jan.)
• Page Start: 88
• Page End: 101
• Publication Date: 2017-01
• Subjects: FRAM -- Bayesian network -- Accident modeling -- Fault tree analysis -- Safety analysis; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2016.11.016
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Industrial accidents are a major concern for companies and families alike. It is a high priority to all stakeholders that steps be taken to prevent accidents from occurring. In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. The FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures. FRAM can be a valuable tool for industrial safety assessment and to consider industrial safety holistically, by providing a framework to examine the operations in detail. However, operations should be considered using both top-down and bottom-up perspectives and all operational experience to make the most informed safety decisions. Highlights: Comparison of accident modeling approaches. Application of FRAM approach to model industrial accident. A new functional link between safety and accident analysis.