Antti Pakonen

OWL based information agent services for process monitoring

To determine the operational situation of a monitored industrial process, an operator needs efficient access to a wide range of information. Measurement data alone does not encapsulate the overall situation, but pieces of information have to be searched from different plant IT systems that unfortunately often have varying interfaces and data formats. Information agent and semantic Web techniques address similar challenges in the context of the Internet by annotating heterogeneous data with formal semantics provided by ontology languages like OWL, and by providing human users with autonomous assistants for information retrieval. This paper presents an agent based concept for process automation that provides operators with easily configured information retrieval and monitoring services, releasing them from tedious data harvesting tasks.

A toolset for model checking of PLC software

Model checking is a powerful formal verification method that can also be used to evaluate PLC software. A lot of manual work and some expertise are still needed. Proposed methods for automating the process rely on standardised specification languages, but PLC software is often vendor-specific, and the source code for function blocks may not even be available. We propose a toolset for model checking of function block based software. After manually modelling the elementary function block library, the model of any block diagram can be specified with easy-to-use graphical tools. The counterexamples output by the model checker can also be visualised using a “living” function block diagram. Our toolset is based on integrating the popular model checker NuSMV with the open source modelling platform Simantics.

Indirect Process Monitoring with Constraint Handling Agents

An approach to indirect process monitoring based on a society of constraint handling agents is presented in this paper. According to this approach an operator of a process automation system can define monitoring tasks which a group of agents perform proactively. The monitoring tasks are assumed to be composites and refer to several process measurements. The purpose of the monitoring agents is to enhance the work of the operator by letting him to supervise the indirect monitoring tasks instead of following a large amount of measurement data. The monitoring agents operate as a multi-agent system consisting of agents with constraint handling capabilities. The agents can setup and execute user configured monitoring tasks cooperatively. Constraints are used as one method for modeling the monitoring logic of the agents. The approach is illustrated with a test scenario using measurement data from an industrial process.

Multi-Agent System Enhanced Supervision of Process Automation

This paper studies issues concerning the application of user configurable cooperative information agents for monitoring tasks in process automation. Within this application area the amount of information gathered from the processes has been growing vastly and the supervising personnel has been minimized in the production plants. As this trend seems to keep going further, the end users need more effective information handling tools. However, the information overflow problem has also shown up in other application domains, and it is useful to discuss the similarities and differences with solutions used in these areas. This paper proposes an agent-based architecture to support active monitoring of the changes in process related data situated in various heterogeneous information sources. This approach is based on a BDI agent model, where individual user-configurable information processing modules are flexibly linked. The approach is demonstrated with an industrially inspired test scenario

Proactive Computing in Process Monitoring: Information Agents for Operator Support

While automation systems can track thousands of measurements it is still up to human process operators to determine the operational situation of the controlled process, particularly in abnormal situations. To fully exploit the computing power of embedded processors and to release humans from simple data harvesting activities, the concept of proactive computing tries to exploit the strengths of both man and machine. Proactive features can be implemented using intelligent agent technology, enabling humans to move from simple interaction with computers into supervisory tasks. Autonomous information agents can handle massive amounts of heterogeneous data. They perform tedious tasks of information retrieving, combining and monitoring on the behalf of their users. This paper presents a multi-agent-based architecture for process automation, which aims to support process operators in their monitoring activities. The approach is tested with a scenario inspired by a real-world industrial challenge.

Information access and control operations in multi-agent system based process automation

An approach to combine information access and control operations in a process automation system extended with multi-agent system technology is presented in this paper. According to this approach a multi-agent system supervises an ordinary process automation system by performing higher-level information access and control operations. The information access operations are aimed for actively combining information from different sources depending on the monitoring tasks of the users. The control operations of the multi-agent system are supervisory control tasks performed either in sequential or iterative fashion. The expected benefit of the multi-agent system is enhanced adaptability of the automation system and increased situation awareness of its users. The architecture of the multi-agent system is based on the BDI agent model and utilization of so-called ontologies. An approach for engineering applications for this kind of a multi-agent system is also discussed. The approach is demonstrated with results from experiments performed with industrial test data and a laboratory test process.

A fuzzy ontology based approach for mobilising industrial plant knowledge

Semantic Web technologies - ontologies in particular - aim at efficient access to heterogeneous, distributed knowledge. However, current ontology languages such as OWL cannot properly address uncertainties, inconsistencies or contradictions. Fuzzy ontologies have been proposed to fix these shortcomings and further enhance information retrieval. The domain of industrial process plants faces many knowledge management challenges. Knowledge in e.g. the form of written reports is stored in different systems, but retrieval is often ineffective and reuse therefore limited. This paper presents an attempt at applying a fuzzy ontology for searching reports of past situations of interest at a process plant. The aim has been to get richer search results from a knowledge base by extending the query with fuzzy neighbour concepts.

A study on user-friendly formal specification languages for requirements formalization

Formal methods and languages are used to prove the correctness of various industrial systems, especially mission-critical ones. They can also be viewed as a means to provide safety and correctness demonstration to the stakeholders of such systems. In domains such as nuclear power plant engineering, the benefits from structured safety evidences would seem obvious. However, most stakeholders in nuclear power industry are not even familiar with formal notations. As a result, to promote the applications of formal methods in practice, the first step is to make formal specification languages (FSLs) more accessible. With user-friendly FSLs, users can focus on safety requirements rather than on their sophisticated formalization. This paper, as a preliminary work towards an integrated framework supporting transparent safety demonstration, reviews existing approaches applied to facilitate requirements formalization and formal specifications. Moreover, the common features of user-friendly languages and their tool supports are also summarized.

Information Agents Handling Semantic Data as an Extension to Process Monitoring Systems

An approach to extend process monitoring with the help of information agents (IA) handling semantic data is presented in this paper. According to this approach, an operator of a process automation system can configure monitoring tasks that a group of IAs performs proactively. The monitoring tasks are assumed to be composites which refer to several process observations and their logical relations. The purpose of these composite monitoring tasks is to enhance the work of the operator by letting him to supervise process phenomena at a higher level of abstraction instead of following a large amount of simple measurement data. The monitoring agents operate as a multi-agent system consisting of agents with capabilities to combine both numerical and symbolic information from several data sources. The agents can setup and execute user configured monitoring tasks cooperatively. The approach is illustrated with test scenarios using data from an industrial paper making process.