Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.

[1]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[2]  Abraham Bernstein,et al.  Towards cooperative planning of data mining workflows , 2009 .

[3]  Kazuo Nishimura,et al.  A Complete Characterization of Optimal Growth Paths in an Aggregated Model with a Non-Concave Production Function , 1983 .

[4]  Sze-jung Wu,et al.  A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Harriet Black Nembhard,et al.  A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization , 2005 .

[6]  Amik Garg,et al.  Maintenance management: literature review and directions , 2006 .

[7]  Nagi Gebraeel,et al.  Predictive Maintenance Management Using Sensor-Based Degradation Models , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Opher Etzion,et al.  A basic model for proactive event-driven computing , 2012, DEBS.

[9]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[10]  Benoît Iung,et al.  On the concept of e-maintenance: Review and current research , 2008, Reliab. Eng. Syst. Saf..

[11]  Damien Trentesaux,et al.  Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach , 2009, Eng. Appl. Artif. Intell..

[12]  Liliane Pintelon,et al.  CIBOCOF: A framework for industrial maintenance concept development , 2009 .

[13]  Christophe Bérenguer,et al.  Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles , 2011, Reliab. Eng. Syst. Saf..

[14]  Zhi Gang Zhang,et al.  An Improved ID3 Decision Tree Algorithm , 2014 .

[15]  L. Pintelon,et al.  A framework for maintenance concept development , 2002 .

[16]  Richard C.M. Yam,et al.  Intelligent Predictive Decision Support System for Condition-Based Maintenance , 2001 .

[17]  Shiju Sathyadevan,et al.  Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest , 2015, CI 2015.

[18]  Opher Etzion,et al.  Towards proactive event-driven computing , 2011, DEBS '11.

[19]  Vir V. Phoha,et al.  K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods , 2007, IEEE Transactions on Knowledge and Data Engineering.

[20]  Rohit Jha,et al.  Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms , 2013, ArXiv.

[21]  Benoît Iung,et al.  Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed , 2010, J. Intell. Manuf..

[22]  K. L. Bansal,et al.  Comparative Study of Data Mining Tools , 2014 .

[23]  Alaa Elwany,et al.  Sensor-driven prognostic models for equipment replacement and spare parts inventory , 2008 .

[24]  Gregoris Mentzas,et al.  Anticipation-driven Architecture for Proactive Enterprise Decision Making , 2014, CAiSE.

[25]  Benoît Iung,et al.  Maintenance alternative integration to prognosis process engineering , 2007 .

[26]  Inma T. Castro,et al.  A predictive maintenance strategy based on mean residual life for systems subject to competing failures due to degradation and shocks , 2012 .

[27]  G. Battese,et al.  A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies , 2004 .

[28]  Sean D Dessureault,et al.  Understanding big data , 2016 .

[29]  Gregoris Mentzas,et al.  A proactive decision making framework for condition-based maintenance , 2015, Ind. Manag. Data Syst..

[30]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[31]  Gene M. Grossman,et al.  Managerial Incentives and the International Organization of Production , 2004 .

[32]  Lina Bertling,et al.  An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades , 2010, IEEE Transactions on Sustainable Energy.

[33]  Elhanan Helpman,et al.  Managerial Incentives and the International Organization of Production , 2002 .

[34]  Khac Tuan Huynh,et al.  Maintenance Decision-Making for Systems Operating Under Indirect Condition Monitoring: Value of Online Information and Impact of Measurement Uncertainty , 2012, IEEE Transactions on Reliability.

[35]  Michael Patriksson,et al.  A stochastic model for opportunistic maintenance planning of offshore wind farms , 2011, 2011 IEEE Trondheim PowerTech.

[36]  Muhammad Ilyas Mazhar,et al.  Condition Based Maintenance (CBM) in the Oil and Gas Industry: An Overview of Methods and Techniques , 2011 .

[37]  Benoît Iung,et al.  Conceptual framework for e-Maintenance: Illustration by e-Maintenance technologies and platforms , 2009, Annu. Rev. Control..

[38]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[39]  Andrew Y. C. Nee,et al.  An agent-based platform for Web-enabled equipment predictive maintenance , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[40]  Chen Jin,et al.  An improved ID3 decision tree algorithm , 2009, 2009 4th International Conference on Computer Science & Education.