Special Session on Industry 4.0

No abstract available

Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Standardized Classification and Interfaces of Complex Behaviour Models in Virtual Commissioning

AbstractToday's increasing use of Virtual Commissioning during the development process of automated manufacturing plants paired with the increasing request towards better control quality leads to the need of improved virtual plants with more effortless set ups. The common techniques of simulating the plant within Virtual Commissioning do no longer fulfil these needs, new approaches have to be developed. This paper examines ways to standardize Functional Mock-Up Unit based behaviour models of mechatronic components of such automated manufacturing plants. It is argued how such components can be classified to reach a distinction between different types to be able to develop standardized interfaces for every type. Therefore a standardized framework of how these interfaces can look like is proposed. Based on this framework as well as the classification of the components two examples, a pneumatic valve cylinder combination and an industrial robot are exemplarily implemented. Besides the standard interfaces to the control program and the visualisation of the simulation a special effort to implement energetically considerations were made. Therefore the presented work shows a way of how to standardize the interfaces of behaviour models of different classes of mechatronic components while increasing the quality of these behaviour models for more complex and accurate behaviour simulation of production plants for Virtual Commissioning as well as related applications

Special Session on Industry 4.0

No abstract available

Towards narrowing the reality gap in electromechanical systems: error modeling in virtual commissioning

Digital factories and smart manufacturing systems have been increasingly researched and multiple concepts were developed to cope with prevailing ever-shortening life-cycles. The ubiquitous digital twin, despite many definitions, is often praised for accurate virtual models. One key idea to improve manufacturing through such virtual models is $\textit{virtual commissioning}$ (VC), aiming at early machine code validation. VC and its virtual models are still lacking behind their real counterparts. This gap between reality and its virtual model, commonly termed $\textit{reality gap}$, increases the complexity of creating cyber-physical systems. An especially stark contrast is visible between the idealized virtual model and a real machine encountering errors. While error simulations exist in other fields of research, a thorough investigation in VC is missing. Thus, this paper addresses the task of narrowing the reality gap in VC based on two steps. First, a comprehensive body of research of possible errors encountered in virtual commissioning is analyzed. Secondly, the feasibility of error implementation is discussed. This paper lays the foundation for narrowing the reality gap and enabling test automation and digital twin-based control

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science