Systematic over-instrumentation

系统过度仪表化项目首先集中于在过程成熟的早期阶段扩展仪表（传感器、执行器）的方法。稍后，当成熟达到高级阶段时，减少仪器的方法也是M1的主题。为了更好地理解过程，过程及其子过程大量配备了传感器，保证提供信息数据，并配备了执行器，允许对过程产生强大的影响。我们将这种传感器和执行器的大规模部署称为“过度仪表化”。贝叶斯优化实验设计（BOED）方法将在额外仪器的选择和参数化方面进行研究和扩展。此外，优化传感器放置（OSP）的方法将扩展到优化除位置以外的参数化，并考虑执行器。为了加速相关计算，将使用神经网络来学习BOED和OSP的推理，以便对这些方法进行快速、近似的计算。将特别注意将正式的专家知识纳入BOED和OSP的方法。过程的隐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（lc）。显然，用传感器（或执行器）观察（或影响）lc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“Do-operator”，基于通过过程执行器对过程量进行低幅度调制的进料。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lc，并可以使用适当的传感器（或执行器）来观察（或影响）混杂因素。此外，将研究变分自编码器（VAE）以发现潜在变量（lv），目的是修改VAE，使lv获得可被仪器化的lc的含义。在迭代改进的高级阶段，减少最初的过度使用变得越来越重要。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，则可以减少仪器的使用。过度仪器本质上是作为一种中间措施，在最初不成熟的过程中实现快速理解、探索和进步。灵敏度分析方法（SA），通过任意多项式混沌展开（aPCE）的数据驱动方法计算，将被用作因果分析的方法补充，以评估变量及其仪器的重要性。对于冲压成形的示例过程，过度仪表的方法需要进行测试、验证和改进。

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.