Decision Making for Predictive Maintenance in Asset Information Management

Asset management is a process of identification, design, construction, operation, and maintenance of physical assets (Wenzler, 2005). An asset-centric approach is vital for the success of an asset intensive organisation as the effective management of assets is a major determinant of organisational success. One key issue in asset information management is the availability of information at the right time, in the right format, before the right person, against the right query, and at the right level. This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved. The critical literature review included also highlights the need for an expert system which integrates reliable information with effective decision-support, under the umbrella of Asset Management. Various elements of asset management were critically reviewed, highlighting the need for more robust Predictive maintenance management for assets. We argue that this is best achieved by a system that, in particular, incorporates Expert System to enhance the quality of predictive maintenance through accurate decision analysis. In addition, it should have fuzzy logic reasoning ability that assists in the decision-making process. Our analysis leads us to propose that Expert System when combined with fuzzy logic provides a better way of decision making in predictive maintenance management of assets.

[1]  Richard C.M. Yam,et al.  Intelligent Predictive Decision Support System for Condition-Based Maintenance , 2001 .

[2]  Dana J. Vanier,et al.  Information needs towards service life asset management , 2000 .

[3]  David Sherwin,et al.  A review of overall models for maintenance management , 2000 .

[4]  Jae Kwang Lee,et al.  A case‐based reasoning approach for building a decision model , 2002, Expert Syst. J. Knowl. Eng..

[5]  Alexander Fay,et al.  A fuzzy knowledge-based system for railway traffic control , 2000 .

[6]  Jan Juretzka,et al.  Decision Support System , 2001 .

[7]  Se-Hak Chun,et al.  New knowledge extraction technique using probability for case‐based reasoning: application to medical diagnosis , 2006, Expert Syst. J. Knowl. Eng..

[8]  Ivo Wenzler,et al.  Development of an asset management strategy for a network utility company: Lessons from a dynamic business simulation approach , 2005 .

[9]  Lakshmi S. Iyer,et al.  Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing , 2002, Decis. Support Syst..

[10]  Arjen Zoeteman,et al.  Life cycle cost analysis for managing rail infrastructure Concept of a decision support system for railway design and maintenance , 2001 .

[11]  Albert H. C. Tsang,et al.  Condition-based maintenance: tools and decision making , 1995 .

[12]  Alexander B. Sideridis,et al.  Reasoning under uncertainty for plant disease diagnosis , 2002, Expert Syst. J. Knowl. Eng..

[13]  B H Kang,et al.  Development of an intelligent decision support system for medication review , 2007, Journal of clinical pharmacy and therapeutics.

[14]  Robert W. Blanning,et al.  A logic‐based approach to rule induction in expert systems , 2003, Expert Syst. J. Knowl. Eng..

[15]  Erik Dahlquist,et al.  Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions , 2002, HIS.

[16]  Daniel J. Fonseca,et al.  A knowledge‐based system for preventive maintenance , 2000, Expert Syst. J. Knowl. Eng..

[17]  Sean B. Eom,et al.  A survey of decision support system applications (1988–1994) , 1998, J. Oper. Res. Soc..

[18]  Nathalie Cassaigne,et al.  Predictive and reactive approaches to the train-scheduling problem: a knowledge management perspective , 2001, IEEE Trans. Syst. Man Cybern. Part C.