R. Accorsi, R. Manzini, P. Pascarella, M. Patella, and S. Sassi, Data mining and machine learning for condition-based maintenance Procedia Manufacturing, vol.11, pp.1153-1161, 2017.
DOI : 10.1016/j.promfg.2017.07.239

URL : https://doi.org/10.1016/j.promfg.2017.07.239

R. Agrawal, T. Imielinski, and . Swami, A (1993) Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD 93, pp.207-216

R. Agrawal and R. Srikant, Maintenance practices in Swedish industries, Fast algorithms for mining association rules in large databases Proceedings of the 20th international conference on very large data bases, vol.121, pp.212-223, 1994.

K. Arif-uz-saman, M. E. Cholette, L. Ma, and A. Karim, Extracting failure time data from industrial maintenance records using text mining Advanced Engineering Informatics, vol.33, pp.388-396, 2016.

J. Baohui, W. Yuxin, and Y. Zheng-qing, The research of data mining in AHM technology based on association rule Proceedings of prognostics and system health management conference (PHM), pp.1-8, 2011.

M. Ben-daya, S. Duffuaa, A. Raouf, J. Knezevic, and D. Ait-kadi, An improved algorithm for high speed train s maintenance data mining based on MapReduce, 25th international conference on could computing and big data, 2006.

D. Bruzzese, C. Davino, J. A. Harding, and M. Tiwari, Data mining in manufacturing: A review based on the kind of knowledge, Visual data mining, vol.4404, pp.501-521, 2008.

C. Marquez, A. Gupta, and J. , Contemporary maintenance management: Process, framework and supporting pillars Omega, International Journal of Management Science, vol.34, issue.3, pp.313-326, 2006.

T. Djatna and I. Alitu, An application of association rule mining in total productive maintenance strategy: An analysis and modelling in wooden door manufacturing industry Procedia Manufacturing, vol.4, pp.336-343, 2015.

, Maintenance terminology -European standard CEN (European Committee for Standardization), 2001.

M. Fowler, UML distilled, 2004.

E. Garcia, C. Romero, S. Ventura, and C. De-castro, An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering User Modeling and User-Adapted Interaction, vol.19, pp.99-132, 2009.

G. Gasmi, B. Yahia, S. , M. Nguifo, E. Slimani et al., Une nouvelle base générique informative des règles d'association Revue I3 (Information Interaction Intelligence), vol.6, pp.31-67, 2006.

G. Gasmi, S. Benyahia, E. M. Nguifo, and Y. Slimani, A new informative generic base of association rules In proceedings of the ninth Pacific Asia knowledge discovery and data mining conference, pp.81-90, 2005.

L. Geng and H. Hamilton, Interestingness measures for data mining: A, survey ACM Computing Surveys, vol.38, issue.3, p.9, 2006.
DOI : 10.1145/1132960.1132963

R. Glavar, Z. Kemeny, T. Nemeth, K. Matyas, and L. Monostori, A holistic approach foe quality oriented maintenance planning supported by data mining methods Procedia CIRPP, vol.57, pp.259-264, 2016.

, Fouille de données en maintenance: Exploitation des résultats, 2005.

M. Hahsler and S. Chelluboina, Visualizing association rules: Introduction to the Rextension package arules Viz Technical report, 2015.

J. Han and M. Kamber, Data mining: Concepts and techniques, 2006.

J. Harding, M. Shahbaz, and S. Kusiak, A (2006) Data mining in manufacturing: A, review Journal of Manufacturing Science and Engineering -Transactions of the ASME, vol.128, issue.4, pp.969-976

M. Karray, B. Chebel-morello, and N. (. Zerhouni, A formal ontology for industrial maintenance Terminology & ontology: Theories and applications, TOTh Conference, 2011.

G. Köksal, I. Batmaz, and M. Testik, A review of data mining applications for quality improvement in manufacturing industry Expert Systems with Applications, vol.38, pp.13448-13467, 2011.

K. Koskinen, Problem absorption as an organizational learning mechanism in project-based companies: Process thinking perspective, International Journal of Project Management, vol.30, issue.3, pp.308-316, 2012.

S. Kotsiantis and D. Kanepoulos, Association rule mining: A recent overview, GESTS International Transactions on Computer Science and Engineering, vol.32, issue.1, pp.71-82, 2006.

D. Larose, Discovering knowledge in data: An introduction to data mining, 2005.

S. H. Liao, P. H. Chu, and P. Hsiao, Data mining techniques and applications -A decade review from, Expert Systems with Applications, vol.39, issue.12, pp.11303-11311, 2000.

G. Mansingh, K. Osei-bryson, and H. Reichgelt, Using ontologies to facilitate post-processing of association rules by domain experts Information Sciences, vol.181, pp.419-434, 2011.

G. Mansingh, K. Osei-bryson, L. Rao, and M. Mcnaughton, Data preparation: art or science?, Proceedings of third IEEE international conference on data science and engineering ICDSE, pp.23-25, 2016.

A. Maquee, A. A. Shojaie, and . Mosaddar, D (2012) Clustering and association rules in analyzing the efficiency of maintenance system of an urban bus network, International Journal of System Assurance Engineering and Management, vol.3, issue.3, pp.175-183

S. Palanisamy, B. Kamsu-foguem, and B. Grabot, Generating knowledge in maintenance from experience feedback, knowledge based systems, special issue on "Enhancing experience reuse and learning, Thesis from Worcester Polytechnic Institute Potes Ruiz, vol.68, pp.4-20, 2006.

E. Ruschel, A. Santos, and E. De-freitas-rocha-loures, Mining shopfloor data for preventive maintenance management: Integrating probabilistic and predictive models Procedia Manufacturing, vol.11, pp.1127-1134, 2017.

W. Sammouri, E. Come, L. Oukhellou, P. Aknin, C. Fonlladosa et al., Temporal association rule mining for the preventive diagnosis of onboard subsystems within floating train data framework Proceedings of the 15th international IEEE conference on intelligent transportation systems (ITSC), pp.16-19, 2012.

. September, , pp.1351-1356

T. Scheffer, Finding association rules that trade support optimally against confidence Intelligent Data Analysis, vol.9, pp.381-395, 2005.

P. Tan, M. Steinbach, and . Kumar, V (2006) Introduction to data mining

Y. Y. Yao, Y. H. Chen, and X. Yang, A measurement-theoretic foundation of rule interestingness evaluation, Foundations and novel approaches in data mining, pp.41-59, 2006.

T. Young, M. Fehskens, P. Pujara, M. Burger, and G. Edwards, Utilizing data mining to influence maintenance actions Proceedings of AUTOTESTCON systems readiness technology conference, pp.13-16, 2010.