Human element forms an inevitable part of maintenance activity and gets affected by a variety of interacting factors, ranging from environmental, organizational, job factors, and so on to personal characteristics, which bring in inherent variability in its reliability. Assessment of impact of these factors is, therefore, critical for human reliability estimation in maintenance. In every probabilistic risk, safety or maintenance analysis, human reliability does act as an effective aspect to assess implications of various aspects of the human performance. But the main constraint with various human reliability analysis methods is in judging the important human performance influencing factors. Because of high degree of uncertainty and variability that characterizes the plant maintenance environment, it is proposed to use the soft computing technique of fuzzy cognitive maps in exploring the importance of performance shaping factors in maintenance scenario. For this purpose, the maintenance environment is modeled in terms of factors affecting human reliability using cognitive maps. The causal relationships among these factors are explored and simulations performed to quantify its effect on the human reliability. The applicability of the methodology is demonstrated through an example. Copyright © 2013 John Wiley & Sons, Ltd.
A complex system, be it a manufacturing system or otherwise, is prone to abnormal functioning, as its units or elements experience unpredictable functional variation. Determining the likely root causes of its undesirable functional events is required to carry out cause analysis of the system from functional point of view. Although the manufacturers provide some information in the manual in this regard, yet these are not conclusive and fall much short in guiding the users in functional cause analysis. Structure plays an important role in this objective. In this article, a procedure for functional cause analysis of a complex manufacturing system through structure is presented using digraph models. 'Function event digraph' is defined for a complex manufacturing system at its various hierarchical levels by considering its input and output functions and their interrelations. A top or undesired functional event for the system is identified from its digraph model, and its root causes are obtained by developing 'functional cause analysis tree'. The suggested approach helps designers and practicing engineers in functional cause analysis of manufacturing systems and thus leading to reliability enhancement and sustenance. An example of a complex CNC (computer numerical control) grinding machine is illustrated to demonstrate the methodology.
This paper will provide a practical overview, based on the authors' field lifecycle experiences, to the consideration of Human Factors in hazard analyses to support the design and operation of oil and gas installations. In the development of oil and gas projects, human factors issues is informally addressed through the design development but recently the US Bureau of Safety and Environmental Enforcement's (BSEE) promulgated the rule on Safety Environment Management Systems (SEMS), API RP 75, in 2010, which now formally recognizes human factor during design lifecycle. In particular, for the element of hazard analysis the SEMS regulation states, "human factors should be considered in this analysis". Over the last 10–15 years, there have been industry papers that have discussed this topic at a high level, mostly about integration with Hazards and Operability Studies (HAZOPs). However, Oil & Gas Producers (OGP) Report No. 454, Human Factors Engineering in Projects (2011) provides appropriate guidance within the HAZOP framework to address human factors in hazard analysis. Despite this history and guidance, it is evident that the lessons learned during the application of HFE in design continue to evolve with more engineering design. This paper discussed the OGP report and other guidance on Human Factors in hazard analysis along with practical lessons learned and challenges from the authors' experiences on major offshore design projects on a range of hazard analyses such as HAZOPs, Hazard Identification Studies (HAZIDs), Qualitative Risk Assessments (QRAs) and Escape, Evacuation and Rescue Analyses (EERAs), etc. The challenge continues in integrating more HFE during hazard and risk management activities in all engineering design activities. This paper has provided the impetus, guidance and momentum to address the human element in hazard analysis during the design of oil and gas facilities; this is just "the tip of the spear".
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