Abstract
Nowadays the data collected in the process in maintenance systems comprise a big portion of the related databases. Analyzing these maintenance data provides the firms, enterprises and organizations with a tremendous competitive edge both in manufacturing and service sectors. As maintenance management is a costly and inevitable part of the organization, ensuring that the maintenance activities are performed in an effective manner, is of outmost importance. In other words, organizations can precede with the cost reduction operations, for instance, if and only if the unproductive maintenance activities and processes can be identified. Subsequently, rectifying or removing these kinds of activities or taking other means of modification can help enterprises and organizations to reduce their costs. Data mining is known to be an excellent tool which helps the decision makers to discover the hidden knowledge and patterns when dealing with a large amount of data. Seeing a gap in the related literature reviewed and in order to fill it, this study proposes a data mining based model to identify the unproductive maintenance activities in a maintenance system. By identifying specific inefficient maintenance activities, this model supports the maintenance decision makers to set goals to make amendments in the maintenance systems under their supervisions. Consequently, the organizations can focus on rectifying these fruitless activities and therefore reducing the costs associated with performing them. Finally, the model was used to identify the unproductive activities in a maintenance system comprising of independent components (an urban bus network).
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References
Al-Naimal F, Al-Timen A (2010) A neural network based algorithm for assessing risk priority of medical equipment. 7th International IEEE Multi-Conference on Systems, signals and devices, Amman, 27–30 June 2010
Azevedo A, Santos MF (2008) KDD, SEMMA and CRISP-DM: a parallel overview. IADIS European Conference Data Mining, pp 182–185, Amsterdam, 2008
Batanov D, Nagarur N, Nitikhunkasem P (1993) EXPERT-MM: acknowledge-based system for maintenance management. J Artifi Intell Eng 8:283–291
Boahui J, Yuxin W, Zheng-qing Y (2011) The RESEARCH OF DATA MINING in AHM technology based on association rules. In: Proceedings of IEEE prognostics & system health management, Denver, June 21, 2011
Bota M, Boulicant J-F, Masson C, Meo R (2004) Query languages supporting descriptive rule mining: a comparative study. Database Support for Data Mining Applications. Springer, Heidelberg, p 24
Cai Z, Sun S, Si S, Yannou B (2008) Maintenance management system base on bayesian networks. In: Proceedings of international seminar on information management, 19 Dec 2008
Choudhary AK, Harding JA, Lin HK (2007) Engineering moderator to universal knowledge moderator for moderating lcollaborative. GLOGIFT 07. UP Technical University, Noida, pp 529–537
Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20:501–521
Ding F, He Z, Zi Y, Chen X, Tan J, Cao H, et al. (2008). Application of support vector machine for equipment reliability forcasting. International conference on industrial informatics, July 2008
Dzeroski S, Struyf J (2006) Towards a general framework for data mining. Knowl Discov Inductive Databases 4747:259–300
Fei Y, Zhang B, Zhu W, Hu J (2010) Methods of pattern extraction and interval prediction for equipment maintenance. In: Proceeding of international conference on computer and information technology, 9 July 2010
Garcı′a MN, Roma′n IR, Pen˜alvo FI, Bonilla MT (2008) An association rule mining method for estimating the impact of project management policies on software quality, development time and effort. Expert Syst Appl 34:522–529
Han J, Kamber M (2006) Data mining, concepts and techniques. Elsevier, New York
Han J, Fu Y, Wang W, Koperski K, Zaiane O (1996) DMQL: a data mining query language for relational databases. DMKD-96 (SIGMOD-96 Workshop on KDD), 2. Montreal, June 1996
Harding JA, Neaga EI (2001) Data mining techniques for supporting manufacturing enterprise design. International conference on industrial and production management, Quebec, 20–23 August 2001
Imielinski T, Virmani A (1999) Query language for database mining: MSQL. Data Min Knowl 3:373–408
Larose DT (2005) Discovering knowledge in data, an introduction to data mining. Wiley, New York
Letourneau S, Famili F, Matwin S (1999) Data mining to predict aircraft component replacement. J Intell Syst Appl 6:59–66
Li J, Shen H, Topor R (2001) Mining the Smallest Association Rule Set for Predictions. IEEE international conference on data mining (ICDM’01), Silicon Valley, 29 Nov–2 Dec 2001
Lindley R, Higgins PE, Mobley RK (2002) Maintenance engineering handbook, 6th edn. McGraw-Hill, New York
Macgarry K (2005) A survey of interestingness measures for knowledge discovery. Knowl Eng Rev 20:39–61
Makouee A, Shojaee AA, Mosaddar D (2012) Clustering and association rules in analyzing the efficiency of maintenance system of an urban bus network. Int J Syst Assur Eng Manage 3:175–183
Meo R (1998) An extension to SQL for mining association rules. Data Min Knowl Disc II:195–224
Meseroll RJ, Kirkos CJ, Shanon RA (2007) Data mining navy flight and maintenance data to affect repair. In: Proceeding of IEEE AUTOTESCON, 17–20 Sept 2007
Michaud P (1997) Clustering techniques. Future Gener Comput Syst 13(2):135–147
Mitra S, Pal SK, Mitra P (2002) Data mining in soft computing framework: a survey. IEEE Trans Neural Netw 13(1):3–14
Mokfi T, Almaeenejad M, Sedighi MM (2011) A data mining based algorithm to enhance maintenance management: a medical equipment case study. First international conference on informatics and computational intelligence, pp 74–80. IEEE Computer Society
Sarawagi S (2000) Integrating association rule mining with relational database systems: alternatives and implications. Data Min Knowl Discov IV:89–125
SPSS I (2007) Clementine data mining guide, version 12.0
Witten IH, Frank E, Hall MA (2011) Data mining, practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington
Wright RG, Kirkland LV, Cicchain J, Deng Y, Dowd N, Hartmuller T, et al. (2001). Maintenance data mining and visualization for fault trend analysis. In: AUTOTESTCON IEEE Proceeding of systems readiness technology conference, 20–23 Aug 2011
Mobely RK (2004) Maintenance fundamentals, 2nd edn. Elsevier, Burlington
Young T, Fehskens M, Pujara P, Burger M, Edwards G. (2010) Utilizing data mining to influence maintenance. In: Proceeding of IEEE AUTOESTCON2010, Orlando, 13–16 Sept 2010
Zhang R, Yang J (2006) Association rules based research on man-made mistakes in aviation maintenance: a case study. In: Proceedings of the sixth international conference on intelligent systems design and applications, Jinan, 2006
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Mosaddar, D., Shojaie, A.A. A data mining model to identify inefficient maintenance activities. Int J Syst Assur Eng Manag 4, 182–192 (2013). https://doi.org/10.1007/s13198-013-0148-7
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DOI: https://doi.org/10.1007/s13198-013-0148-7