Knowledge-Intensive Decision Support System for Manufacturing Equipment Maintenance

To ensure continuous production in industrial plants, the high valued manufacturing eqipments should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and reparation must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and maintenance. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Manufacturing Equipment Maintenance in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of casebased Reasoning and ontology, the KiDSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our ap proach and the benefit of the ontology support.

[1]  Jie Hu,et al.  A CBR system for injection mould design based on ontology: A case study , 2012, Comput. Aided Des..

[2]  B. Gallupe,et al.  Knowledge management systems: surveying the landscape , 2001 .

[3]  Christine Halverson,et al.  Organizational Memory as Objects, Processes, and Trajectories: An Examination of Organizational Memory in Use , 2004, Computer Supported Cooperative Work (CSCW).

[4]  A. Adla,et al.  A Proposal of Toolkit for GDSS Facilitators , 2011 .

[5]  Bo Zhang,et al.  Research on Ontology-Based Case Indexing in CBR , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[6]  Zhiliang Ma,et al.  Data-driven decision-making for equipment maintenance , 2020 .

[7]  Frédéric Adam,et al.  20 years of decision making and decision support research published by the Journal of Decision Systems , 2012, J. Decis. Syst..

[8]  Farrokh Mistree,et al.  An Ontology for Representing Knowledge of Decision Interactions in Decision-Based Design , 2020, Comput. Ind..

[9]  Jing Li,et al.  A knowledge based machine tool maintenance planning system using case-based reasoning techniques , 2019, Robotics Comput. Integr. Manuf..

[10]  Jose Manuel Zurita,et al.  Using a CBR Approach Based on Ontologies for Recommendation and Reuse of Knowledge Sharing in Decision Making , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[11]  Intelligent Semantic Case Based Reasoning System for Fault Diagnosis , 2018 .

[12]  Jack C. Wileden,et al.  A Decision Support Ontology for collaborative decision making in engineering design , 2009, 2009 International Symposium on Collaborative Technologies and Systems.

[13]  Chang-Shing Lee,et al.  Ontology-Based Fuzzy-CBR Support System for Ship's Collision Avoidance , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[14]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[15]  Douglas D. Gemmill,et al.  Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics , 2017, Expert Syst. Appl..

[16]  Kathryn Cormican,et al.  Ontology-based systems engineering: A state-of-the-art review , 2019, Comput. Ind..

[17]  Felix B. Tan,et al.  Global Information Management Research: Current Status and Future Directions , 2004 .

[18]  Agnar Aamodt,et al.  Case-Based Reasoning Research and Development , 1995, Lecture Notes in Computer Science.

[19]  Ziad Kobti,et al.  A Domain Ontology Model for Mould Design Automation , 2010, Canadian Conference on AI.

[20]  Steffen Staab,et al.  Ontology Engineering Methodology , 2009, Handbook on Ontologies.

[21]  Herbert Schildt.  Java: The Complete Reference , 1996 .

[22]  Marina Kultsova,et al.  Intelligent Support of Decision Making in Human Resource Management Using Case-Based Reasoning and Ontology , 2014, JCKBSE.

[23]  Jeff Z. Pan,et al.  Resource Description Framework , 2020, Definitions.

[24]  Michael M. Richter,et al.  Case-Based Reasoning: A Textbook , 2013 .

[25]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[26]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[27]  Abdelkader Adla,et al.  A Co-operative Intelligent Decision Support System for Boilers Combustion Management based on a Distributed Architecture , 2007 .

[28]  Emmanuel Nauer,et al.  Tuuurbine: A Generic CBR Engine over RDFS , 2014, ICCBR.