Asset integrity and reliability is one of the 20 elements of Process Safety Management (PSM) as defined by the Center for Chemical Process Safety (CCPS). We combine expert knowledge and data analytics (Artificial Intelligence, Machine Learning, and Keyword Analysis) to create a reaction network for Asset Integrity Management (AIM) and provide a theoretical and practical basis for handling uncertainty in large data sets such as company incident databases. The purpose of the current study is to control and minimize the total number of incidents that occur within an oil and gas operation by applying a multidisciplinary approach to explore and develop AIM. This systematic approach can improve AIM to better understand PSM as a whole and the underlying dynamics ever-present in the system. In this study, AIM is divided into 2 major groups – asset and human factors – and then, in order to get more detailed results, each group is divided into 9 and 5 subcategories, respectively. To analyze the relationships between the different factors of AIM, two score-based (Tabu and Hill Climbing) and one hybrid (Max-Min Hill-Climbing) Bayesian networks are used to develop one final viable solution. The findings of these techniques point towards the same results for reducing incident rates. Four factors related to assets, including construction, testing, inspection, and maintenance, account for more than half the incidents (54.78%). Additionally, there must be a greater emphasis placed on the impact of human factors as they are directly (23.58%) and indirectly (11.10%) responsible for accidents as well as other technological malfunctions. By focusing on AIM which is a key element of PSM, it will be possible to gain a better understanding of one of the most significant and problematic sources of risk in process safety.

资产完整性和可靠性是化学过程安全中心 (CCPS) 定义的过程安全管理 (PSM) 的 20 个要素之一。我们结合专家知识和数据分析（人工智能、机器学习和关键字分析）为资产完整性管理 (AIM) 创建反应网络，并为处理大型数据集（如公司事件数据库）中的不确定性提供理论和实践基础。当前研究的目的是通过应用多学科方法来探索和开发 AIM，控制和最大限度地减少油气作业中发生的事故总数。这种系统的方法可以改进 AIM，以更好地理解 PSM 作为一个整体以及系统中始终存在的潜在动态。在这项研究中，AIM 分为 2 大组——资产和人为因素——然后，为了获得更详细的结果，每组分别分为 9 和 5 个子类别。为了分析 AIM 不同因素之间的关系，使用两种基于分数（禁忌和爬山）和一种混合（最大-最小爬山）贝叶斯网络来开发一种最终可行的解决方案。这些技术的研究结果表明，降低事故率的结果相同。与资产相关的四个因素，包括建设、测试、检查和维护，占事件的一半以上（54.78%）。此外，必须更加重视人为因素的影响，因为人为因素直接（23.58%）和间接（11.10%）对事故和其他技术故障负责。