A model predictive early warning system for disturbance type faults

故障检测与诊断（FDD）系统是现代过程工厂不可或缺的一部分。传统上，FDD系统仅在实际信号或经处理的信号已经越过阈值之后才生成警报。虽然这是一个强有力的方法，但在某些情况下，对系统进行任何修改可能为时已晚。这项研究计划的目标是将预测功能纳入FDD系统的早期预警，使运营商有足够的时间采取纠正措施。在建议的研究中，我们专注于扰动型故障。过程系统很容易受到干扰：有些干扰是可测量的，有些则是不可测量的。扰动逐渐扰动过程系统;扰动的影响可以在过程中完全感觉到之前预测。我们提出了一个新的框架FDD，将估计一个未知的干扰进入过程系统，预测其对系统的影响，并最终发出警报，如果影响被认为是显着的。所提出的预测故障检测和诊断框架由两个模块组成：（i）同时输入和状态估计（SISE）模块，以及（ii）滚动时域预测和可行性分析模块。进入过程系统的扰动以及系统内的其他状态通常是不可测量的。SISE模块将估计未知状态和进入系统的干扰。期望最大化（EM）算法将被用来迭代估计的状态和未知的干扰幅度。EM算法具有良好的统计特性，并保证收敛。基于估计的扰动幅度、系统模型和可用的测量，将使用移动时域预测器来预测系统的未来状态。最后，将进行可行性分析，以确定是否有足够的能力，在致动器，以抵消干扰的影响，并保持在操作限制的输出。如果没有可行的解决方案，将发出警报。新型FDD系统有望比传统报警系统更早地发出报警。它将使过程工业以及许多其他工业更安全，并通过提供早期故障指示来提高操作效率。通过这项研究计划，两名博士和两名硕士生将被培养。他们将获得有关过程监测和控制的理论和应用的知识，并对工业标准分布式控制系统（DCS）和监控和数据采集（SCADA）系统进行实践培训。这些技能在加拿大工业中非常受欢迎。因此，拟议的研究计划将大大有助于加拿大经济。

Fault detection and diagnosis (FDD) systems are an integral part of modern process plants. Traditionally, FDD systems generate an alarm only after the actual signal or the processed signal has crossed the threshold. Though this is a robust approach, in some cases it may be too late to make any amendments to the system. The goal of this research program is to incorporate predictive features in FDD systems for early warning, to give operators sufficient time to take corrective actions. In the proposed research, we focus on disturbance-type faults. Process systems are prone to disturbances: some of them are measured while others are unmeasured. Disturbances perturb process systems gradually; the effect of a disturbance can be predicted before its impact is fully felt in the process. We propose a new framework for FDD that will estimate an unknown disturbance entering into a process system, predict its impact on the system, and finally issue an alarm if the impact is deemed significant. The proposed predictive fault detection and diagnosis framework consists of two modules: (i) simultaneous input and state estimation (SISE) module, and (ii) a moving horizon prediction and feasibility analysis module. Disturbances entering process systems, as well as other states within the system, are usually unmeasured. The SISE module will estimate both the unknown states and the disturbance entering the system. An expectation maximization (EM) algorithm will be used to iteratively estimate the states and the unknown disturbance magnitude. The EM algorithm has excellent statistical properties and guarantees convergence. Based on the estimated disturbance magnitude, system model, and available measurements, the future states of the system will be predicted using a moving horizon predictor. Finally, feasibility analysis will be carried out to determine if there is sufficient capacity in the actuators to counteract the disturbance effects and keep the outputs within the operational constraints. An alarm will be issued if there is no feasible solution. The novel FDD system is expected to issue an alarm earlier than the traditional alarm systems. It will make process industries, as well as many other industries, safer and improve the efficiency of operation by providing early indications of faults. Through this research program, two PhD and two Master's students will be trained. They will gain knowledge on the theory and applications of process monitoring and control and hands-on training on industry standard distributed control systems (DCS) and supervisory control and data acquisition (SCADA) system. These are highly sought after skills in Canadian industries. Thus, the proposed research program will contribute significantly to Canadian economy.