Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: a case study
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  • 作者:Michael Kanisuru Adeyeri ; Khumbulani Mpofu…
  • 关键词:Maintenance model ; Agent hardware system ; Conventional machines ; Machine conditions monitoring
  • 刊名:Journal of Industrial Engineering International
  • 出版年:2016
  • 出版时间:March 2016
  • 年:2016
  • 卷:12
  • 期:1
  • 页码:93-109
  • 全文大小:1,574 KB
  • 参考文献:Adeyeri MK, Kareem B, Aderoba AA, Adewale OS (2012) Temperature based embedded programming algorithm for conventional machines condition monitoring proceedings of the fourth international conference on future computing technology, international academy, research and industrial association (IARIA), Nice-France, pp 51–57
    Ahmad R, Kamaruddin S, Azid I, Almanar I (2011) Maintenance management decision model for preventive maintenance strategy on production equipment. J Ind Eng Int 7(13):22–34
    Albino V, Carella G, Okogbaa O (1992) Maintenance polices in just-in-time manufacturing lines. Int J Prod Res 30:369–382CrossRef MATH
    Alsyouf I (2004) Cost effective maintenance for competitive advantages. Thesis for the degree of doctor of philosophy (terotechnology), School of Industrial Engineering, Växjö University, Sweden
    Andijani A, Duffuaa S (2002) Critical evaluation of simulation studies in maintenance systems. Prod Plan Control 13(4):336–341CrossRef
    Ashraf WL (2004) A decision analysis model for maintenance policy selection using a CMMS. J Qual Maint Eng 10(3):191–202CrossRef
    Atmel (2010) 8-bit Microcontroller with 16 K bytes in-system programmable flash ATmega16. Atmel Incorporation, San Jose
    Babaei H, Shahanaghi K, Bakhsha A (2011) A new approach for constraining failure probability of a critical deteriorating system Yard crane scheduling in port container terminals using genetic algorithm. J Ind Eng Int 7(12):52–59
    Derigent W, Thomas E, Levrat E, Iung B (2009) Opportunistic maintenance based on fuzzy modelling of component proximity. CIRP Ann Manuf Technol 58:29–32CrossRef
    Duffuaa SO, Ben-Daya M, Al-Sultan KS, Andijani AA (2001) A generic conceptual simulation model for maintenance systems. J Qual Maint Eng 7(3):34–40CrossRef
    Greasly A (2005) Using system dynamics in a discrete-event simulation study of a manufacturing plant. Int J Oper Prod Manag 25(6):534–548CrossRef
    Grieb B (1992) Temperature measurement in process control. Advances in instrumentation and control, proceedings of the annual meeting of the instrument society of America, vol 47, Instrument Society of America, Research Triangle Park, North Carolina
    Hausladen I, Bechheim C (2004) E-maintenance platform as a basis for business process integration. In: Proceedings of INDIN04, international conference on industrial informatics, pp 46–51
    Iung B, Marquez CA (2006) Special issue on e-Maintenance. Comput Ind 57(6):473–606CrossRef
    Iung B, Levrat E, Marquez AC, Erbe H (2009) Conceptual framework for e-Maintenance: illustration by e-Maintenance technologies and platforms. Annu Rev Control 33(2009):220–229CrossRef
    Jardine AKS, Joseph T, Benjevic D (1999) Optimising condition-based maintenance decisions for equipment subject to vibration monitoring. J Qual Maint Eng 5(3):192–202. (http://​www.​emerald-library.​com ). Retrieved: June, 2012
    Jasper V, Warse K, Hans W (2011) Managing condition-based maintenance technology, a multiple case study in the process industry. J Qual Maint Engi 17 (1): 40–62. (http://​www.​emerald-library.​com . June, 2012)
    Joe M (2011) Application note on thermocouple signal conditioning using the AD594/AD595. One Technology Way Norwood, USA. (http://​www.​analog.​com . Accessed 26 Nov 2011)
    Koc M, Lee J (2001) A system framework for next-generation e-Maintenance system. 2nd International symposium on environmentally conscious design and inverse manufacturing
    Lee J, Scott LW (2006) Zero
    eakdown machines and systems: productivity needs for next-generation maintenance. Eng Asset Manag 2006:31–43CrossRef
    Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostic tools and e-maintenance. Comput Ind 57:476–489CrossRef
    Levrat E, Iung B, Marquez CA (2008) e-Maintenance: review and conceptual framework. Prod Plan Control 19(4):408–429CrossRef
    Lu S, Tu YC, Lu H (2007) Predictive condition-based maintenance for continuously deteriorating systems. Qual Reliab Eng Int 23:71–81. doi:10.​1002/​qre.​823 CrossRef
    Mahantesh N, Ramachandra A, Satosh Kumar AN (2008) Artificial intelligence-based condition monitoring for plant maintenance. Assem Autom 28(2):143–150CrossRef
    Marquez AC, Herguedas SA (2002) Models for maintenance optimization: a study for repairable systems and finite time periods. Reliab Eng Syst Saf 75(3):367–377CrossRef
    Marquez CA, Gupta JND, Herguedas SA (2003) Maintenance policies for a production system with constrained production rate and buffer capacity. Int J Prod Res 41(9):1909–1926CrossRef MATH
    Muhammad AM, Sarmad N, Sepehr N (2011) The AVR microcontroller and embedded systems using assembly and C. Prentice Hall, London
    Muller A, Suhner MC, Iung B (2008) Proactive maintenance for industrial system operation based on a formalised prognosis process. Reliab Eng Syst Saf 93(2):234–253CrossRef
    NIC-National Instruments Corporation (2011) Proteus software, National Instruments Corporation, Mopac Expwy Austin TX 78759-3504. http://​www.​ni.​com/​academic/​circuits . Accessed 08 May 2011
    Oladokun VO, Charles-Owaba OE, Nwaouzru CS (2006) An application of artificial neural network to maintenance management. J Ind Eng Int 2(3):19–26
    OTW-One Technology Way (2009) “ADXL345 Data sheet”, analog devices, One Technology Way, USA. http://​www.​analog.​com . Accessed 08 Oct 2011
    Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance—a review. Int J Adv Manuf Technol 50:293–313CrossRef
    Tao B, Ding H, Xion YL (2003) IP sensor and its distributed networking application in e-Maintenance. In: Proceedings of the 2003 IEEE international conference on systems, man and cybernetics, vol 4, pp 3858–3863
    Tsang A (2002) Strategic dimensions of maintenance management. JQME 8(1):7–39
    Uca M, Qiu RG (2005) e-Maintenance in support of e-automated manufacturing systems. J Chin Inst Ind Eng 22(1):1–10
    Voisin A, Levrat E, Cocheteux P, Iung B (2010) Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test. J Intell Manuf 21:177–193CrossRef
    Yuan J, Chaing J (2000) Optimal maintenance policy for a production system subject to aging and shocks. J Qual Maint Eng 6(3):200–216CrossRef
    Zineb S, Chadi S (2001) Maintenance integration in manufacturing systems: from the modeling tool to evaluation. Int J Flex Manuf Syst 13(3):267–285CrossRef
  • 作者单位:Michael Kanisuru Adeyeri (1)
    Khumbulani Mpofu (1)
    Buliaminu Kareem (2)

    1. Department of Industrial Engineering, Tshwane University of Technology, Pretoria, South Africa
    2. Department of Mechanical Engineering, The Federal University of Technology, Akure, Nigeria
  • 刊物主题:Industrial and Production Engineering;
  • 出版者:Springer Berlin Heidelberg
  • ISSN:2251-712X
文摘
This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal and best functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers’ demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitoring probes programmed in microcontroller using C language. The developed hardware consists of vibration sensor of ADXL345, temperature sensor of AD594/595 of type K thermocouple, microcontroller, graphic liquid crystal display, real time clock, etc. The hardware is divided into two: one is based at the workstation (majorly meant to monitor machines behaviour) and the other at the base station (meant to receive transmission of machines information sent from the workstation), working cooperatively for effective functionalities. The resulting hardware built was calibrated, tested using model verification and validated through principles pivoted on least square and regression analysis approach using data read from the gear boxes of extruding and cutting machines used for polyethylene bag production. The results got therein confirmed related correlation existing between time, vibration and temperature, which are reflections of effective formulation of the developed concept. Keywords Maintenance model Agent hardware system Conventional machines Machine conditions monitoring

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