Hazard analysis techniques have been around for many years, and have proven effective in the prevention of incidents and no doubt the saving of lives. Process hazard analysis (PHA) is now fairly robust and regulated, focused on overarching risks associated with the safe handling of hazardous materials and approaches to engineer-out such risks. Occupational hazard analysis (OHA) is keenly focused on human activity, and personal protection in hazardous working conditions. Both approaches are critical - but are often carried out separately, by different parts of an organization, which could result in an incomplete picture of the full set of operational risks in the field. Developing a holistic picture of both past and present dangers calls for a deep exploration of evidence. HAZOPs, PHA's, incident records and investigations provide expert analysis of hazards and mitigating strategies. Near-miss reports and safety observations add a large amount of information as well; the reporting frequency of these “leading indicators” can be both a blessing and a curse, as time and available resources constrain the ability to analyze and detect hazard signals within. As important as analyzing the historical record is for lessons learned, the more recent observations could indicate new hazards or highlight concerning trends. These could feed valuable “real time” information back to operations and maintenance teams to improve risk assessments and task planning. Enter artificial intelligence (AI) as a means to analyze the large amount of written hazard analyses, reports and observations to quickly extract insights around hazardous conditions, activities, incident causes and risk mitigation measures. Trained to understand concepts and contexts in both process and personal safety, AI can provide a natural-language information exploration environment for scanning thousands of documents in seconds and present common themes and related records. Not unlike us humans, AI learns from the past, informs the present and can help reduce risks in the future.

中文翻译：

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人工智能作为危险分析的盟友

危害分析技术已经存在多年，并已被证明在预防事故和挽救生命方面是有效的。过程危害分析 (PHA) 现在相当强大且受到监管，重点关注与安全处理危险材料相关的总体风险以及设计消除此类风险的方法。职业危害分析 (OHA) 专注于人类活动和危险工作条件下的个人保护。这两种方法都很关键——但通常由组织的不同部分单独执行，这可能导致对现场全套操作风险的不完整了解。全面了解过去和现在的危险需要对证据进行深入探索。HAZOP、PHA、事故记录和调查提供了危害和缓解策略的专家分析。险情报告和安全观察也增加了大量信息；这些“领先指标”的报告频率既是福也是祸，因为时间和可用资源限制了分析和检测内部危险信号的能力。与分析历史记录一样重要的是吸取教训，最近的观察可能表明新的危害或突出相关趋势。这些可以将宝贵的“实时”信息反馈给运营和维护团队，以改进风险评估和任务规划。输入人工智能 (AI) 作为分析大量书面危险分析、报告和观察的手段，以快速提取有关危险情况的见解，活动、事故原因和风险缓解措施。人工智能受过培训，可以理解流程和人身安全方面的概念和上下文，可以提供自然语言信息探索环境，可在几秒钟内扫描数千份文档并呈现常见主题和相关记录。与我们人类不同，人工智能从过去学习，为现在提供信息，并有助于降低未来的风险。