A data fusion approach of multiple maintenance data sources for real-world reliability modelling
Arif-Uz-Zaman, Kazi, Cholette, Michael E., Li, Fengfeng, Ma, Lin, & Karim, Azharul (2015) A data fusion approach of multiple maintenance data sources for real-world reliability modelling. In 10th World Congress on Engineering Asset Management, 28-30 September 2015, Tampere Hall, Tampere, Finland.
A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.
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|Item Type:||Conference Paper|
|Keywords:||Data fusion, Maintenance data, Naive Bayes, Text mining, Reliability modelling|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 [Please consult the author]|
|Deposited On:||19 Feb 2016 04:04|
|Last Modified:||27 Jan 2017 05:21|
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