Abstract
This paper presents how to design a novel Maintenance Support System (MSS) to prognostic breakdowns in production lines based on mini-terms, well known as a Mini-term 4.0. The system is based on the real time sub-cycle time (mini-term) monitorization and how the mini-term variability can be used as a fault detection indicator. Mini-terms and micro-terms were introduced in our previous work as a machine subdivision. A mini-term subdivision can be selected by the expert team for several reasons, the replacement of a machine part or simply to analyze the machine more adequately. (A micro-term is a component from a mini-term and it can be as small as the user wishes. Mini-terms are able to detect the same physical deterioration phenomenon than common sensor but with an important advantage, it is easy and cheap to install. It is cheap because do not require any additional hardware installation to measure the sub-cycle time, just use the PLC and sensors installed for the automated production process, and easy because only requires to code extra timers into the PLC. Mini-terms are nowadays implanted at Ford factories around the world where a learning process is established to enrich the knowledge of the system. The system detects change points and sends an e-mail to the maintenance workers. They repair the machine and report the pathology detected to the system. Real cases are shown at the end of the paper.
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References
Battaia O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142(2):259–277. https://doi.org/10.1016/j.ijpe.2012.10.020
Li L, Djurdjanovic D, Ni J (2007) Maintenance task priorization using data driven bottleneck detection and maintenance opportunity windows. In: ASME 2007 international conference on manufacturing science and engineering, pp 517–523. https://doi.org/10.1115/MSEC2007-31150
Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signals Process 20(7):1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012
Son KL, Fouladirad M, Barros A, Levrat E, Lung B (2013) Remaining useful life estimation based on stochastic deterioration models: a comparative study. Reliab Eng Syst Safe 112:165–175. https://doi.org/10.1016/j.ress.2012.11.022
Chang Q (2005) Supervisory factory control based on real-time production feedback. PhD
Leal F, da Silva R, Montevechi J, de Almeida D, Silva F (2011) A practical guide for operational validation of discrete simulation models. Pesquisa Oper 31:57–77. https://doi.org/10.1590/S0101-74382011000100005
Li L, Chang Q, Ni J, Biller S (2009) Real time production improvement through bottleneck control. Int J Prod Res 47(21):6145–6158. https://doi.org/10.1080/00207540802244240
López CE (2014) Unbalanced workload allocation in large assembly lines. PhD
Garcia E (2016) Análisis de los sub-tiempos de ciclo técnico para la mejora del rendimiento de las líneas de fabricación. PhD
Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems. Pearson Education
Nemati HR, Steiger DM, Lyer LS, Herschel RT (2002) Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis Support Syst 33(2):143–161. https://doi.org/10.1016/S0167-9236(01)00141-5
Chakir A, Chergui M, Elhasnaou S, Medromi H, Sayouti A (2016) A decision approach to select the best framework to treat an it problem by using multi-agent systems and expert systems. In: Advances in ubiquitous networking, pp 499–511. https://doi.org/10.1007/978-981-287-990-540
Felsberger A, Bernhard O, Gerald R (2016) A review of decision support systems for manufacturing systems. In: The international conference on knowledge technologies and data-driven business 2016 - i-KNOW 2016, pp 499–511
Murthadha M, Banaz A (2013) Knowledge-driven decision support system based on knowledge warehouse and data mining for market management. J Manag Bus Res 13(10):2249–2288
Deshpande RR, Demarco J, Sayre JW, Liu BJ (2016) Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer. Int J Comput Assistant Radiol Surg 11(11):2071–2083. https://doi.org/10.1007/s11548-016-1403-6
Muhammad SH, Ebrahim Z, Mahmood WHW, Rahman MN (2017) Decision support system classification and its application in manufactoring sector: a review. Int Jurnal Teknologi 79(1):153–163. https://doi.org/10.11113/jt.v79.7689
Garcia E, Montes N (2017) A Tensor Model for Automated Production Lines based on Probabilistic Sub-Cycle Times. Nova Science Publishers 18(1):221–234
Zhao X, Cai K, Wang X, Song Y (2018) Optimal replacement policies for a shock model with a change point. Comput Ind Eng 118:383–393. https://doi.org/10.1016/j.cie.2018.03.005
Nigro MB, Pakzad SN, Dorvash S (2014) Localized structural damage detection: a change point analysis. Comput Aided Civ Infrastruct Eng 29:416–432. https://doi.org/10.1111/mice.12059
Chakir A, Chergui M, Elhasnaou S, Medromi H, Sayouti A (2016) A decision approach to select the best framework to treat an it problem by using multi-agent system and expert systems. In: Advances in ubiquitous networking, pp 499–511 .https://doi.org/10.1007/978-981-287-990-5_40
Ahmad R, Kamaruddini S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63(1):135–149. https://doi.org/10.1016/j.cie.2012.02.002
Garcia E, Montes N, Alacreu M (2018) Towards a knowledge-driven maintenance support system for manufacturing lines. In Proceedings of the 15th international conference on informatics in control, automation and robotics (ICINCO 2018), vol 1, pp 43–54.https://doi.org/10.5220/0006834800430054
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The authors wish to thank Ford España S.L and in particular Almussafes Factory for the support in the present research.
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García, E., Montes, N., Alacreu, M. (2020). Towards a Novel Maintenance Support System Based On mini-terms: Mini-term 4.0. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics. ICINCO 2018. Lecture Notes in Electrical Engineering, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-030-31993-9_5
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