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ABSTRACT: An important consideration in safety analysis and accident prevention is the identification of and response to accident precursors. These off-nominal events are opportunities to recognize potential accident pathogens, identify overlooked accident sequences, and make technical and organizational decisions to address them before further escalation can occur. When handled properly, the identification of precursors provides an opportunity to interrupt an accident sequence from unfolding; when ignored or missed, precursors may only provide tragic proof after the fact that an accident was preventable.In this work, we first provide a critical review of the concept of precursor, and we highlight important features that ought to be distinguished whenever accident precursors are discussed. We address for example the notion of ex-ante and ex-post precursors, identified for postulated and instantiated (occurred) accident sequences respectively, and we discuss the feature of transferability of precursors. We then develop a formal (mathematical) definition of accident precursors as truncated accident sequences within the modeling framework of Discrete Event Systems. Additionally, we examine the related notions of “accident pathogens” as static or lurking adverse conditions that can contribute to or aggravate an accident, as well as “near misses”, “warning signs” and the novel concept of “accident pathway”. While these terms are within the same linguistic neighborhood as “accident precursors”, we argue that there are subtle but important differences between them and recommend that they not be used interchangeably for the sake of accuracy and clarity of communication within the risk and safety community. We also propose venues for developing quantitative importance measures for accident precursors, similar to component importance measures in reliability engineering. Our objective is to establish a common understanding and clear delineation of these terms, and by bringing formal mathematical tools to bear on them, we hope to provide a richer basis and more interpretive possibilities for examining and understanding risk and safety issues.Reliability Engineering [?] System Safety 06/2013; 114:148–154. · 1.90 Impact Factor
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ABSTRACT: The corrective maintenance process can be decomposed into failure and repair processes. Creating a model to capture the corrective maintenance process then requires an accurate estimate of the behavior of these constituent processes. For systems composed of many individual parts, information about failure and repair behavior is more likely to be available at the component level than the system level. Depending on the number of components that comprise the system, modeling each part may become computationally burdensome; in addition, some few components may account for a large portion of the overall system failures.In such a situation, one solution to the modeling burden is aggregation: the mathematical assimilation of many component distributions into a single representative distribution for the group. This paper describes how aggregation may be performed for such a system and how an algorithm may be developed to automate the process. Next, it describes how to simulate an aggregated distribution using a pseudo-random number generator and finally demonstrates these concepts for a sample problem. The first section of the paper introduces corrective maintenance modeling and aggregation; the second section describes aggregation for corrective maintenance; the third explains how to simulate the aggregated distribution; the fourth demonstrates aggregation; and the fifth discusses limitations of the method and concludes.Procedia Computer Science 01/2013; 16:459–468.