A fault detection and diagnosis tool for chemical processes based on hybrid-dynamic Bayesian belief network

多年来，以最低的成本制造最高质量的产品的竞争使流程工业变得非常复杂。复杂的操作更容易受到工艺混乱和设备故障的影响。完全依赖人工操作员是操作员的过重负担，并将工厂置于危险之中。困难的出现是因为故障的范围很广，加工厂的规模也很大。例如，在大型加工厂中，每秒可能记录多达1500个过程变量。此外，操作者的效率取决于她/他在给定时间的知识、经验以及心理和身体状态。许多经验结果表明，人类的直觉判断和决策可能远远不是最优的，而且随着复杂性和压力的增加，它甚至会进一步恶化。工业统计数据显示，70%的工业事故是人为错误造成的。在故障检测与诊断的文献中，已经提出了许多定性和定量的故障检测与诊断方法。然而，这些检测和诊断方法未能在行业内产生影响。这些方法大多能够及早发现故障，但对故障根本原因的诊断能力较差。这些方法在诊断中缺乏透明度，没有纳入过程知识，缺乏灵活性以允许操作员输入，并且没有提供缓解措施。因此，尽管有许多专家系统可用，但在现实中，异常事件完全由过程操作员管理。 我们的目标是开发一个全面的决策支持系统，通过吸收来自各种定量检测和诊断工具的信息，获取相关过程知识，并帮助构建决策过程，来补充人类认知决策过程。所提出的决策支持系统的主干将基于混合动态贝叶斯信念网络。混合DBBN是一种灵活的结构，允许集成来自流程的离散和连续信息。网络结构捕获已知的过程交互作用；使用历史数据计算网络的条件概率。网络将根据来自各种单变量和多变量故障检测和诊断工具的检测和诊断信息进行动态更新。它将吸收过程知识和操作员输入的定量诊断信息，以得出对故障的最合理解释。最后，如果有许多不同的替代缓解行动，它将使用基于风险的标准来选择最合适的操作员行动(S)。 这项研究将推动贝叶斯信任网络理论的发展，以充分描述化学过程，如过程中的循环循环、因果关系随时间的变化以及离散和连续信息的同化。将开发详细的方法来构建用于过程故障诊断的混合DBBN。将使用广泛的模拟研究和实验室规模的工艺设备以及工业案例研究来验证方法。 本文的研究将为DBBN的理论和应用做出原创性的贡献。这种新的统一方法将给出清晰而简洁的诊断信息，并向操作员建议适当的操作，从而使过程操作更安全。这有可能对经济产生重大影响；研究表明，仅美国的石化行业每年就损失300亿美元。从长远来看，这将使加拿大加工业在业务上具有竞争优势，并拯救生命。

The race to manufacture products of the highest quality at lowest cost has made process industries very complex over the years. Complex operations are more vulnerable to process upsets and equipment breakdown. Complete reliance on human operators is an overburden on the operators and puts the plants at risk. The difficulty arises due to the broad scope of faults and the size of the process plants. For example, in a large process plant there may be as many as 1500 process variables recorded every second. Furthermore, the efficiency of the operator depends on her/his knowledge, experience,and mental and physical state at a given time. Many empirical results have shown that human intuitive judgment and decision making can be far from optimal, and it deteriorates even further with complexity and stress. Industrial statistics show 70% of industrial accidents are due to human error. In the fault detection and diagnosis literature many quantitative and qualitative fault detection and diagnosis methods have been proposed. However, these detection and diagnosis methods have failed to make an impact in the industry. Most of these methods are able to detect faults early but diagnosis of the root cause of fault is poor. These methods lack transparency in the diagnosis, do not incorporate process knowledge, lacks flexibility to allow for operator’s input, and do not offer mitigation actions. As a result, although many expert systems are available, in reality, abnormal events are solely managed by process operators. Our goal is to develop a comprehensive decision-support system that will supplement the human cognitive decision-making process by assimilating information from various quantitative detection and diagnostic tools, accessing relevant process knowledge, and aiding the process of structuring the decision. The backbone of the proposed decision support system will be based on hybrid dynamic Bayesian belief network (hybrid-DBBN). The hybrid-DBBN is a flexible structure that allows integrating discrete and continuous information from a process. The structure of the network captures the known process interactions; conditional probabilities of the network are calculated using historical data. The network will be updated dynamically, based on the detection and diagnostic information from various univariate and multivariate fault detection and diagnosis tools. It will assimilate quantitative diagnostic information with process knowledge, and operator input to derive the most reasonable explanation for the fault. Finally, if there are many different alternative mitigating actions, it will use a risk based criteria to select the most appropriate operator action (s). The proposed research will advance the theory of Bayesian belief network to adequately represent chemical processes, such as, cyclic loops in a process, change of causality with time, and assimilation of discrete and continuous information. Detailed methodologies will be developed for constructing hybrid-DBBN for process fault diagnosis. Methodologies will be validated using extensive simulation studies and laboratory scale process equipment, and industrial case studies. The proposed research will make original contributions to the theory and application of DBBN. This new unified approach will give clear and concise diagnostic information and suggest appropriate action to the operator and thus make process operations safer. This has the potential to make significant economic impact; research suggests that the petrochemical industry in the US alone loses 30 billion dollars annually. In the long run, this will give the Canadian process industry a competitive edge in the business and save lives.