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.

Impact and interest:

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

33 since deposited on 19 Feb 2016
20 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 93080
Item Type: Conference Paper
Refereed: Yes
Keywords: Data fusion, Maintenance data, Naive Bayes, Text mining, Reliability modelling
Divisions: 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: 21 Jun 2017 18:14

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page