A model predictive early warning system for disturbance type faults

故障检测和诊断(FDD)系统是现代流程工厂不可或缺的一部分。传统上，FDD系统只有在实际信号或处理后的信号超过阈值后才会发出警报。虽然这是一种强有力的方法，但在某些情况下，对该制度进行任何修改可能为时已晚。这项研究计划的目标是在FDD系统中加入预测功能以进行早期预警，让运营商有足够的时间采取纠正措施。在所提出的研究中，我们将重点放在干扰型故障上。过程系统容易受到干扰：其中一些是可测量的，而另一些则是不可测量的。干扰逐渐扰乱过程系统；在过程中充分感受到干扰的影响之前，可以预测干扰的影响。我们提出了一种新的FDD框架，它将估计进入过程系统的未知干扰，预测其对系统的影响，并在影响被认为严重时发出警报。所提出的预测故障检测与诊断框架由两个模块组成：(I)同时输入和状态估计(SISE)模块和(Ii)滚动预测和可行性分析模块。进入过程系统的干扰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。将使用期望最大化(EM)算法来迭代估计状态和未知扰动幅度。EM算法具有良好的统计特性，保证了算法的收敛。基于估计的扰动大小、系统模型和可用的测量，将使用滚动水平预测器来预测系统的未来状态。最后，将进行可行性分析，以确定执行器中是否有足够的能力来抵消干扰影响，并将输出保持在操作约束范围内。如果没有可行的解决方案，就会发出警报。这种新型的FDD系统预计将比传统的报警系统更早地发出警报。它将使流程工业以及许多其他行业变得更安全，并通过提供故障的早期迹象来提高运行效率。通过该研究项目，将培养两名博士生和两名硕士生。他们将获得过程监测和控制的理论和应用方面的知识，以及关于工业标准分布式控制系统(DCS)和监控和数据采集(SCADA)系统的实践培训。这些技能在加拿大的行业中备受追捧。因此，拟议的研究计划将对加拿大经济做出重大贡献。

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.