Authors:
Henri Helanterä
;
Mikko Salmenperä
and
Hannu Koivisto
Affiliation:
Institute of Automation and Control, Tampere University of Technology, Finland
Keyword(s):
Proactive maintenance, condition monitoring, fault diagnostics, distributed automation, Java, MATLAB®
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Decision Support Systems
;
Distributed Control Systems
;
Enterprise Information Systems
;
Expert Systems
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Knowledge-Based Systems Applications
;
Software Agents for Intelligent Control Systems
;
Symbolic Systems
Abstract:
Proactive maintenance is a solution to increase the availability of the production equipment in the process industry. It involves online condition monitoring of field devices and reliably diagnosing the reason behind any abnormal behaviour, thus helping to rationalise maintenance operations. Making the information of different industrial sites available for analysis, significant improvements could be made to the predicting capabilities of condition monitoring and to the accuracy of fault diagnostics. The global condition monitoring system described in this paper is based on distributed agent-architecture and employs data communication networks to connect the industrial sites to one or more service centres. Many successful methods used in condition monitoring and fault diagnostics often require advanced tools. MATLAB® software is the de facto standard in numerical computing but integrating MATLAB® as a computing server to the J2EE-based condition monitoring system is a laborious task
as no all-purpose and easy-to-use methods exist. However, this paper introduces some strategies to overcome the integration problem. The most important solution presented here is so called inverted calling scheme. Also two other approaches are discussed: using MATLAB® engine functions via C-language native methods and deployment of standlone MATLAB® COM components. All the above strategies have their strengths and weaknesses. Implementing the inverted call requires more effort from the programmer but is standard-compliant. Exploiting engine functions or COM components is easier as some ready-made software can be employed but the emerging solutions are not pure-Java.
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