Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 68, Issue -, Pages 21-28Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2016.10.005
Keywords
Engineering asset management; Clustering; Performance evaluation; Density and distance-based clustering; Outlier analysis; K-means
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Funding
- Research Grants Council of the Hong Kong Special Administrative Region, China [11201315]
- National Natural Science Foundation of China [61573332, 61601431]
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Engineering Asset Management (EAM) emphasizes on achieving sustainable business outcomes and competitive advantages by applying systematic and risk-based processes to decisions concerning an organization's physical assets. Nowadays, there is no specific method to evaluate performance of EAM and lack of benchmark to rank performance. To fill this gap, an improved density and distance-based clustering approach is proposed. The proposed approach is intelligent and efficient. It has largely simplified the current evaluating method so that the commitment in resources for manual data analyzing and performance ranking can be significantly reduced. Moreover, the proposed approach provides a basis on benchmarking for measuring and ranking the performance in Engineering Asset Management (EAM). Additionally, by using the intelligent approach, companies can avoid to pay expensive consultant fees for inviting external consultancy company to provide the necessary EAM auditing and performance benchmarking. (C) 2016 Elsevier Ltd. All rights reserved.
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