标题： 动态工业环境中职业安全风险的建模与缓解 显示英文标题

标题： Modeling and mitigation of occupational safety risks in dynamic industrial environments

摘要： 识别和减轻安全风险在许多行业中至关重要。除了指南和最佳实践外，许多行业已经建立了安全管理系统（SMS），旨在监控和强化良好的安全行为。然而，在利用这些系统收集的数据以稳健地量化各种职业危害所造成的风险方面，分析能力仍然不足。此外，最佳实践和现代SMS无法应对在许多工业环境中常见的动态变化的环境/行为特征。 本文提出了一种方法，通过以数据驱动的方式持续且定量地评估安全风险来解决这些问题。我们的方法的核心是一种直观的分层概率模型，该模型能够解释典型SMS收集到的稀疏且嘈杂的安全数据。开发了一种完全贝叶斯的方法，以便在线校准此模型。之后，经过校准的模型包含了表征不同安全危害所造成风险所需的信息。此外，所提出的模型还可用于自动化决策，例如解决资源分配问题——目标是针对风险缓解——这在资源受限的工业环境中经常遇到的问题。 该方法在模拟测试平台上得到了严格的验证，并在一家石化厂大型维护项目的真实数据上展示了其可扩展性。 显示英文摘要

摘要： Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industrial settings. This article proposes a method to address these issues by enabling continuous and quantitative assessment of safety risks in a data-driven manner. The backbone of our method is an intuitive hierarchical probabilistic model that explains sparse and noisy safety data collected by a typical SMS. A fully Bayesian approach is developed to calibrate this model from safety data in an online fashion. Thereafter, the calibrated model holds necessary information that serves to characterize risk posed by different safety hazards. Additionally, the proposed model can be leveraged for automated decision making, for instance solving resource allocation problems -- targeted towards risk mitigation -- that are often encountered in resource-constrained industrial environments. The methodology is rigorously validated on a simulated test-bed and its scalability is demonstrated on real data from large maintenance projects at a petrochemical plant.