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The ease of software development, maintenance, commissioning and reconfiguration is paramount in industrial systems, because these must be adaptable to changes in customer demand and the evolution of production assets. Such adaptations are a core motivator in the fourth industrial revolution. Additionally, software applications should be able to communicate over the logical and geographical borders of production plants, regardless of software platforms and vendors. However, these issues remains unaddressed in current software tools, although a solution is reachable with a proper software toolkit. As a solution, this article introduces such a toolkit to support the development of industrial software systems. The toolkit implements a communication stack with both a communication protocol and an information model. The long-term goal is to enable all communication in industrial plants with such a toolkit regardless of software products, vendors and platforms. Additionally, the toolkit provides auxiliary tools to help in the development of industrial software, such as services to provide historical data for simulation purposes. As an outcome, the toolkit provides insight about the development needs of industrial communications. publishedVersion

Conventional industrial communication systems suffer from rigidness, inflexibility and lack of scalability. The environment is heterogeneous as the systems exchange data with a variety communication protocols, some of which are proprietary. This makes it laborious and expensive to reconfigure or upgrade the systems. As the solution, this article proposes a message-bus-based communication architecture to enable information exchange between systems regardless of their geographical location and position within the functional hierarchy of the plant. The architecture not only enables communication to cross the conventional physical borders but also provides scalability to growing data volumes and network sizes. As proofs of concept, the article presents a prototype in three environments: a copper smelter, a steel plant and a distillation column. The results suggest that the message-bus-based approach has potential to renew industrial communications, a core part of the fourth industrial revolution. publishedVersion

In mobile machines, as well as in manufacturing, the overall productivity is essential for business competitiveness. As the operation of a modern mobile machine is affected by various parameters, they need to be tuned to reach an optimal performance – however, due to machine complexity, parameter optimisation is difficult for a typical operator. To enable parameter optimisation locally in machines, this article presents a system architecture to generate information and knowledge from machine fleet data and to utilise them in machine operations in the field. Measurement data is collected and analysed to discover the associations between machine performance and parameter values. While some results are plain statistical distributions, any resulting more sophisticated domain knowledge is stored as rules. Rule-based reasoning enables a zone of interoperation between the information system and domain experts. Once information and knowledge have been generated, they are made available to machines that run the actual parameter assessment application. Results made with forestry data indicate that the system has a considerable potential to improve machine productivity. acceptedVersion

The modern mobile machinery has advanced on-board computer systems. They may execute various types of applications observing machine operation based on sensor data (such as feedback generators for more efficient operation). Measurement data utilisation requires preprocessing before use (e.g. outlier detection or dataset categorisation). As more and more data is collected from machine operation, better data preprocessing knowledge may be generated with data analyses. To enable the repeated deployment of that knowledge to machines in operation, information management must be considered; this is particularly challenging in geographically distributed fleets. This study considers both data refinement management and the refinement workflow required for data utilisation. The role of machine learning in data refinement knowledge generation is also considered. A functional cloud-managed data refinement component prototype has been implemented, and an experiment has been made with forestry data. The results indicate that the concept has considerable business potential. acceptedVersion

Due to the networked nature of modern industrial business, repeated information exchange activities are necessary. Unfortunately, information exchange is both laborious and expensive with the current communication media, which causes errors and delays. To increase the efficiency of communication, this study introduces an architecture to exchange information in a digitally processable manner in industrial ecosystems. The architecture builds upon commonly agreed business practices and data formats, and an open consortium and information mediators enable it. Following the architecture, a functional prototype has been implemented for a real industrial scenario. This study has its focus on the technical information of equipment, but the architecture concept can also be applied in financing and logistics. Therefore, the concept has potential to completely reform industrial communication. publishedVersion

To save the environment and costs, there is a clear need to optimise and coordinate large-scale industrial processes, but the task is challenging. Complex plants may run multiple unit processes that are operated with separate control systems, although the processes are interdependent. Often, optimisation requires appropriate scheduling for both continuous processes and batch processes. These requirements of coordination, asynchronism and distributed operation are difficult to conform to with the current tools or systems. Therefore, to ease optimisation, this study suggests a communication platform concept. The platform enables the use of mathematical models to manage and utilise knowledge about production processes. To promote adaptation to actual business cases and their future evolution, the platform design emphasises asynchronism, loose coupling of optimisation modules and systems as well as distributed operation. This work has received particular motivation from copper and steel production, as they both require materials refinement in multiple separate but mutually dependent steps. Several contributions are presented for the platform: requirements, architecture design and two proofs of concept. The results suggest that the concept would bring considerable business value in industrial production.

Efficiency of industrial processes and a high quality of the products can be achieved with advanced monitoring and control solutions. In addition, industrial processes often consume large amounts of energy and through their efficiency and use of resources also have a significant environmental impact. For industrial process optimisation it is necessary to integrate distributed data and functionality into plant-wide coordinating level solutions. From an implementation point of view this is challenging due to different communication protocols and messaging structures. In this paper an integration architecture is proposed that decouples control systems using a message bus mediator approach. The mediator acts as a unified point of access that through adapters facilitates integration of existing control systems to advanced plant-wide data-driven and event-driven control. It is demonstrated with a laboratory case presenting two examples applicable to real-life industrial problems.