Smart Data Mining System for Automatically Assessing the Performance in Engineering Asset Management

Engineering or Physical Asset Management (EAM)based on the standards of BSI PAS 55 and/or ISO 55000-2becomes a common criterion to assess the quality ofpublic/building services. Nowadays, there is no specific method toevaluate the performance of EAM according to the standardsand lack of benchmarking process to rank performance ofcompanies that have the same nature of business. To fill this gap,an intelligent Support Vector Machines (SVM) approach isproposed here. The goal is to make the assessment ofperformance smarter, more practical and efficient. The systemincludes a questionnaire in evaluating the performance ofinformation management in EAM and can be filled on-line viathe Internet. The system also includes a smart approach toautomatically assess and benchmark the practice of EAM of aparticular SME to the best practice of a good building O&M(operation and maintenance) practitioner in informationmanagement. By using SVM, it can classify a new O&Mpractitioner that has filled the questionnaire via the Internet andautomatically output a score that belongs to either the best,satisfied, average or unacceptable performance in EAM. Withthe help of this smart system, the surveyed SME can find herperformance score and status as compared to the best practice ofthe similar type of O&M practitioner. Hence, the SMEs do notneed to hire expensive experts or consultancy but still enjoy alow-cost and effective mean to find the gap in EAM practice sothat they can improve their practices and finally will be qualifiedto obtain the certificate in EAM.