David Hästbacka

Model-driven development of industrial process control applications

Abstract: This article presents model-driven development and domain-specific modeling applied to the development of industrial process control applications. The approach is based on established practices of the industrial automation and control domain, a compatible UML profile, and an integrated and standards based tool environment for modeling and transformation execution. The methods provide means for developing applications using domain-specific modeling concepts to increase productivity and enhance platform independent solution reuse. The approach has been implemented to support industrial practices and to be able to utilize existing control system platforms. During demonstrations and an assessment event with industry professionals the methods have been successfully applied to the development of small-scale process control applications. In this paper, attention is also paid on discussion of the practical applicability and benefits of the approach for engineering and development of industrial process control applications.

Tool Support for the UML Automation Profile - For Domain-Specific Software Development in Manufacturing

The development of modern distributed automation applications is challenging and present development practices contain manual transferring of informal information from one phase to another. Our research aims to overcome some of these challenges by integrating concepts from modern object-oriented design, model-driven development and high-level modeling potential of the UML automation profile into a seamless development path from PI-diagrams to control software. This paper presents a prototype of a control engineering tool that supports the UML automation profile and is intended to cover part of the development chain. The tool was implemented on the Eclipse platform and it utilizes various open source tools and frameworks to enable also usage of UML and SysML in modeling work. The implemented tool can be extended by transformation tools capable of processing requirements of the control system and PIM-model of the designed control software.

Device status information service architecture for condition monitoring using OPC UA

Condition monitoring and maintenance of devices and equipment is an important aspect of operating a production facility affecting the availability of production systems. Modern production environments can consist of thousands of devices that each need to be monitored so that maintenance can be performed when necessary to sustain a cost-effective state of production. Today operation and maintenance (O&M) is typically outsourced, and equipment and device manufacturers have also entered the service business. This brings challenges in managing a multitude of different devices using different protocols as well as in the varying needs for utilizing this information in enterprise functions and services. Based on OPC Unified Architecture (UA) a scalable architecture is developed for providing device status information of heterogeneous field devices and sensors to enterprise level applications and services. A proof of concept implementation of this architecture is presented and its envisioned adoption in a mine environment is discussed.

Semantics enhanced engineering and model reasoning for control application development

Development of advanced systems requires new methods to improve quality and efficiency of engineering processes, and to assist management of complex models encompassing different engineering disciplines. Methods such as model-driven development and domain-specific modeling facilitate development from this perspective but reduce interoperability and other prospects of rationalizing processes, on the other hand. An approach applying Web Ontology Language (OWL) semantics and reasoning to models is presented with examples to support industrial control application engineering. Using the methods, generalized classifications are inferred from instance models and combined with generic engineering knowledge maintained in ontologies. Reasoning allows identifying assemblies and structures outside the scope of traditional modeling to detect specific structures, flaws and error-prone designs. The semantic descriptions of models allow linking of other engineering related information to support engineering tasks during design. The results indicate that OWL semantics and reasoning can be used as a supplement enhancing typical development practices.

Unifying Process Design with Automation and Control Application Development - an Approach Based on Information Integration and Model-Driven Methods

Abstract The transfer of design information from process design to automation design requires attention as requirements on process control are increasing resulting in more complex systems to be designed. This paper presents current process and automation design practices and highlights related problems and information exchange challenges. A development method based on a common consistent information model, utilizing standards and existing data models, is discussed and model-driven methods for improved quality and productivity are introduced to the development of industrial process control applications.

System architecture for mastering machine parameter optimisation

Abstract 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.

Context modeling with situation rules for industrial maintenance

Industrial maintenance requires not only experienced service personnel to carry out the tasks but also up-to-date information about the target equipment and its environment. Accessing information required to execute the tasks is a common challenge for maintenance personnel. This paper presents a knowledge modeling approach and a technical architecture of a gateway system developed to support maintenance personnel with information combined from legacy data sources as well as from context ontology augmented with situational knowledge. The novelty of the approach is its unified object oriented style of knowledge representation encapsulating predefined queries and rules into ontology classes. The approach utilizes standard Semantic Web technologies, especially SPARQL query language and SPARQL Inferencing Notation SPIN. Feasibility of the approach is demonstrated with a simple maintenance use case example executed in an experimental knowledge gateway system.

Service-based condition monitoring for cloud-enabled maintenance operations

Condition based maintenance is regarded as a maintenance strategy that through measured component wear balances availability and accurate operation with necessary maintenance operations. In industrial settings there can be hundreds or thousands of objects to monitor, and systems that are distributed into different networks are seldom compatible. This paper proposes a condition monitoring system based on standardized services operating as parts of a service framework. The solution builds on dynamic service composition as well as standard information models for measurement related data, and supports functionality from sensors to cloud applications. The approach has been applied to industrial processing in mining and it can be claimed to improve interoperability as well as reduce engineering effort when composing functionality supporting maintenance operations.

Empowering Industrial Maintenance Personnel with Situationally Relevant Information using Semantics and Context Reasoning

Industrial maintenance is a complex discipline requiring experience and know-how. Information such as maintenance work orders are usually provided through mobile devices to field personnel. There are also other information sources with manuals, documented history, contact information etc. that is of value supporting the tasks at hand but typically this needs to be retrieved manually. The challenge is how to utilize information originating from heterogeneous information sources that, in addition, may change e.g. for outsourced maintenance service providers taking care of different sites. To facilitate the use of supporting materials an ontology knowledge management approach is developed that integrates data and documents, and provides relevant information for the task at hand using context and semantics based reasoning. Results from early prototyping show that the approach can improve utilization of information in existing systems through adapter layers and complement existing mobile as well as upcoming augmented reality applications by automatically providing situationally relevant information.

Semantics and reasoning for control application engineering models

Development of advanced systems requires new methods to improve quality and efficiency of engineering processes, and to assist management of complex models encompassing different engineering disciplines. Methods such as model-driven development and domain-specific modeling facilitate development from this perspective but reduce interoperability and other prospects of rationalizing processes, on the other hand. An approach applying OWL semantics and reasoning to models is presented with examples to support industrial control application engineering. Using the methods, generalized classifications are inferred from instance models and combined with generic engineering knowledge maintained in ontologies. Reasoning allows identifying assemblies and structures outside the scope of traditional modeling to detect flaws and error-prone designs. The results indicate that OWL semantics and reasoning can be used as a supplement furthering typical development practices.