Systematic over-instrumentation

系统化过度检测项目首先集中于在工艺成熟的早期阶段扩展检测设备(传感器、执行器)的方法。后来，当成熟到高级阶段时，减少仪器的方法也是M1的主题。为了更好地理解过程，过程及其子过程都慷慨地配备了传感器，承诺提供信息数据，并配备了执行器，允许对过程产生强大影响。我们把这种传感器和执行器的大规模部署称为“过度仪表化”。贝叶斯最优实验设计(BOED)方法将在附加仪器的选择和参数设置方面进行研究和推广。此外，最优传感器布置(OSP)的方法将被扩展以优化除位置之外的参数设置，并考虑执行器。为了加速相关的计算，神经网络将被用来学习BOED和OSP的推理，以便执行这些方法的快速近似计算。将特别注意将正式的专家知识整合到BOED和OSP中的方法。过程的隐藏变量是其他过程变量的原因，对理解过程起着重要作用；它们被称为潜在混杂因素(LCS)。显然，用传感器(或执行器)观察(或影响)LCS的可能性似乎是更好地控制过程的有力手段。为了发现LCS，将研究一种基于过程执行器对过程量进行低幅度调制的DO算子。通过分析这些信号在整个过程中的传播，应该发现因果关系和LCS，并可以使用足够的传感器(或执行器)来观察(或影响)混杂因素。此外，将研究变分自动编码器(VAE)以发现潜在变量(LV)，目的是修改VAE以使LV获得可被检测的LCS的含义。在迭代改进的高级阶段，减少初始过度检测变得越来越重要。例如，如果一些改进的子过程可以在没有专用控制回路的情况下运行，或者使用更少或更便宜的传感器，则可以减少仪器。过度规范基本上被用作一种中间措施，以便能够在最初不成熟的过程中快速理解、探索和推进。敏感度分析方法(SA)由数据驱动的任意多项式混沌展开(APCE)方法计算，将作为因果分析的方法论补充，以评估变量及其工具的重要性。

The project Systematic over-instrumentation concentrates, firstly, on methods for extending the instrumentation (sensors, actuators) in the early phase of process maturation. For later on, when the maturation arrives at an advanced stage, also methods for the reduction of the instrumentation are a topic of M1. To obtain a better process understanding, the process and its sub-processes are generously equipped with sensors that promise to deliver informative data, and with actuators that allow for a strong influence on the process. We call this massive deployment of sensors and actuators ‘over-instrumentation’. Bayesian Optimal Experimental Design (BOED) methods will be investigated and extended in terms of the choice and parameterisation of additional instrumentation. Also, methods of Optimal Sensor Placement (OSP) will be extended to optimise parameterisation other than location, and also to consider actuators. To accelerate the relevant calculations, neural networks will be used to learn the reasoning of BOED as well as OSP in order to perform fast, approximate computation of these methods. Particular attention will be paid to approaches that allow to integrate formalised expert knowledge into BOED and OSP.Hidden variables of the process, which are causes of other process variables, play an important role to understand a process; they are called Latent Confounders (LCs). Obviously, the possibility of observing (or influencing) LCs with sensors (or actuators) seems to be a strong means to better control the process.For the discovery of LCs, a kind of ‘Do-operator’ will be investigated, based on feeding in low-amplitude modulations of process quantities via process actuators. By analysing the propagation of these signals throughout the process, causal relations and LCs should be discovered, and adequate sensors (or actuators) may be used to observe (or influence) the confounders. Additionally, Variational Autoencoders (VAE) will be investigated to discover Latent Variables (LVs), with the aim of modifying the VAE so that the LVs get the meaning of LCs that can be instrumented.During the advanced stages of iterative improvement, reductions in the initial over-instrumentation become increasingly important. For example, if some of the improved sub-processes could operate without a dedicated control loop, or with fewer or cheaper sensors, the instrumentation can be reduced. Over-instrumentation is essentially used as an intermediate measure to enable rapid understanding, exploration, and advances in the initially immature process. Methods of Sensitivity Analysis (SA), calculated by data-driven methods of arbitrary Polynomial Chaos Expansions (aPCE), will be used as a methodological complement to causality analysis to evaluate the importance of variables and their instrumentation.For the example process Stamp Forming, the methods of over-instrumentation are to be tested, validated, and improved.