Catálogo de publicaciones - libros

Industrial plants usually consist of different process units which are strongly cross-linked to each other. This leads to the point that a voluntary or involuntary change in one unit (e.g. changing some process control parameter or having a malfunctioning value) can lead to unexpected results in another process unit. Hence, knowing which are the causing and which are the effecting process variables is of great interest. Still, depending on the underlying process and the characteristics of the excitation signal, directed connectivities can or can not be detected. Therefore, in this paper several types of dynamic SISO systems and excitation signals are defined for which a directed connectivity from input to output signal should be detected and from output to input should not be detected. As a method for the detection of directed influences Spectral Granger Causality is used, which has been extended with a surrogatebased significance test. This test is used to define if a directed influence exists from one process variable to another.

This paper shows how automation components can be enhanced with self-monitoring capabilities, which are more effective than traditional rule-based methods, by using Industrial Analytics approaches. Two application examples are presented to show how this approach allows the realization of a predictive maintenance strategy, while drastically reducing the realization effort. Furthermore, the benefits of a flexible architecture combining edge- and cloud-computing for the realization of such monitoring system are discussed.

In this contribution, we give an insight in our experiences in the technical and organizational realization of industrial analytics. We address challenges in implementing industrial analytics in real-world applications and discuss aspects to consider when designing a machine learning solution for production. We focus on technical and organizational aspects to make industrial analytics work for real-world applications in factory automation. As an example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture.

Cyber Physical Production Systems (CPPS) provide a huge amount and variety of process and production data. Simultaneously, operational decisions are getting ever more complex due to smaller batch sizes (down to batch size one), a larger product variety and complex processes in production systems. Production engineers struggle to utilize the recorded data to optimize production processes effectively.

In contrast, CPPS promote decentralized decision-making, so-called intelligent agents that are able to gather data (via sensors), process these data, possibly in combination with other information via a connection to and exchange with others, and finally take decisions into action (via actors). Modular and decentralized decision-making systems are thereby able to handle far more complex systems than rigid and static architectures.

This paper discusses possible applications of Machine Learning (ML) algorithms, in particular Reinforcement Learning (RL), and the potentials towards an production planning and control aiming for operational excellence.

Water distribution systems (WDSs) are large complex infrastructures made from pipes, valves, pumps, tanks and other elements designed and erected to transport water of sufficient quality from water sources to consumers. The amount of the above elements, which can reach up to tens of thousands of links and junctions, their frequently wide spatial dispersion and the WDS characteristic of being very dynamic structures make the management of real WDSs a complex problem [1–4]. However, although the main objective is to supply water in the quantity and quality required, other requirements are essential, namely maintaining conditions far from failure scenarios [5,6], ability to quickly detect sources of contamination intrusion [7,8], minimization of leaks [9,10], etc.