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Learning from accidents: Analysis of multi-attribute events and implications to improve design and reduce human errors

机译:从事故中学习:分析多属性事件及其影响,以改善设计并减少人为错误

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摘要

© 2015 Taylor & Francis Group, London.High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed.
机译:©2015 Taylor&Francis Group,London。高科技事故可能是在多个活动故障和潜在条件的复杂交互作用下发生的,并且最近的重大事故调查越来越强调人为错误或人为因素的重要作用。潜在条件可能具有很长的潜伏期,这意味着在人为错误触发事故序列之前,系统中可能会嵌入许多设计失败。因此,有必要仔细研究持久的设计缺陷与人为错误行为之间的关系,以此作为将事故降到最低的可能方法。这项研究将通过将人工神经网络方法应用于专有的多属性事故数据集来解决这个复杂的问题,以揭示人为错误和设计失败之间的多维关系。解释聚类和数据挖掘结果可进一步洞察设计中嵌入的潜在条件。然后讨论了支持设计失败预防方案开发的含义。

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