Abstract
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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Alkali, B. M., Bedford, T., Quigley, J., & Gaw, J. (2009). Failure and maintenance data extraction from power plant maintenance management databases. Journal of Statistical Planning and Inference, 139, 1766–1776.
Bastos, P., Lopes, I., & Pires, L. (2014). Application of data mining in a maintenance system for failure prediction. In R. D. J. M. Steenbergen et al. (Eds.), Safety, reliability and risk analysis: Beyond the horizon (pp. 933–940).
Jeon, J., & Sohn, S. Y. (2015). Product failure pattern analysis from warranty data using association rule and Weibull regression analysis: A case study. Reliability Engineering & System Safety, 133, 176–183.
Lantz, B. (2013). Machine learning with R. Birmingham, UK: Packt Publishing.
Louit, D. M., Pascual, R., & Jardine, A. K. S. (2009). A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliability Engineering & System Safety, 94(10), 1618–1628.
Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42, 4348–4360.
Prytz, R., Nowaczyk, S., Rognvaldsson, T., & Byttner, S. (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Expert Systems with Applications, 42, 4348–4360.
Wang, W., & Pham, H. (2006). Reliability and optimal maintenance. London, UK: Springer.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 15(8), 796–801.
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Arif-Uz-Zaman, K., Cholette, M.E., Li, F., Ma, L., Karim, A. (2016). A Data Fusion Approach of Multiple Maintenance Data Sources for Real-World Reliability Modelling. In: Koskinen, K., et al. Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-27064-7_7
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DOI: https://doi.org/10.1007/978-3-319-27064-7_7
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