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

基于混合动态贝叶斯信念网络的化工过程故障检测与诊断工具

• 批准号: RGPIN-2014-06651
• 负责人: Imtiaz, Syed
• 金额: $ 1.46万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567952
• 关键词: fault; detection; diagnosis; tool; chemical

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.

多年来，以最低成本制造最高质量产品的竞争使过程工业变得非常复杂。复杂的操作更容易受到过程干扰和设备故障的影响。完全依赖人工操作员是操作员的负担，并使工厂处于危险之中。由于故障范围广和加工厂规模大，因此出现了困难。例如，在大型过程工厂中，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/他的知识、经验以及在给定时间的精神和身体状态。许多实证结果表明，人类的直觉判断和决策可能远远不是最佳的，而且随着复杂性和压力的增加，它会进一步恶化。工业统计数据显示，70%的工业事故是由于人为错误造成的。在故障检测和诊断文献中，已经提出了许多定量和定性的故障检测和诊断方法。然而，这些检测和诊断方法未能在行业中产生影响。这些方法大多能够早期发现故障，但对故障的根本原因诊断较差。这些方法在诊断中缺乏透明度，不包含过程知识，缺乏允许操作员输入的灵活性，并且不提供缓解措施。因此，尽管有许多专家系统可用，但实际上，异常事件仅由过程操作员管理。我们的目标是开发一个全面的决策支持系统，通过吸收各种定量检测和诊断工具的信息，访问相关的过程知识，并帮助构建决策的过程，来补充人类的认知决策过程。建议的决策支持系统的骨干将基于混合动态贝叶斯信念网络（混合DBBN）。混合DBBN是一种灵活的结构，允许集成来自过程的离散和连续信息。网络的结构捕获已知的过程交互;使用历史数据计算网络的条件概率。该网络将动态更新的基础上，从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。它将把定量诊断信息与过程知识和操作员输入同化，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措施，它将使用基于风险的标准来选择最合适的操作员措施。该研究将推进贝叶斯信念网络理论，以充分代表化学过程，如循环回路的过程中，因果关系随时间的变化，以及离散和连续信息的同化。详细的方法将开发用于构建混合DBBN过程故障诊断。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。本文的研究工作将对DBBN的理论和应用做出开创性的贡献。这种新的统一的方法将提供清晰和简洁的诊断信息，并建议操作员采取适当的行动，从而使过程操作更安全。这有可能产生重大的经济影响;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中具有竞争优势，并挽救生命。