Assessment and Review of Maintenance Practices in the 4th Industrial Revolution using the Cognitive Analytics Framework

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Authors

  • Niraj R. Sharma
     Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad. E-mail: nirajpauwels@gmail.com, A.K. Mishra, Professor, Department of Mining Engineering, IIT(ISM), Dhanbad ,IN
  • A.K. Mishra
     Professor, Department of Mining Engineering, IIT(ISM), Dhanbad. ,IN
  • Hemant Agrawal
     Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad ,IN

Keywords:

Equipment; industrial revolution; breakdown; AI; industry 4.0; cognitive

Abstract

This paper reviews past and the prevailing maintenance concepts practiced, evolved with industrial revolutions over the centuries and briefly outlines the cognitive process, methods and framework of tools and techniques which will be used in the days to come. The maintenance practices have continuously evolved in how the equipment was earlier managed using breakdown, corrective, preventive, total productive maintenance, condition-based maintenance, failure analysis reporting, risk-based maintenance and reliability centric maintenance. The core objective of maintenance remained the same “Increase useful life of an asset with minimal costs”. The thinking now has changed from viewing maintenance as “costs” to maintenance as “investments”. In the era of Industry 4.0, the maintenance value chain - an integrated cyber-physical system plays an important role in the maintenance of the mining equipment. A cognitive/AI (Artificial Intelligence) maintenance framework can be an effective tool in optimizing the maintenance programme with minimal costs when compared to the traditional maintenance programme in the industry. The optimal replacement policy can be calculated and determined by the computer to minimize the expected downtime or maximize the expected profit. The minimum expected downtime per unit time and maximum expected profit per unit time can also be determined. This replacement policy and mathematic models can be used as reference to the failure system maintenance and replacement.The evolution from traditional data-driven algorithms to blended intelligent algorithms is helping in developing new optimization models for maintenance management systems.

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Published

2022-10-20

How to Cite

Sharma, N. R., Mishra, A., & Agrawal, H. (2022). Assessment and Review of Maintenance Practices in the 4th Industrial Revolution using the Cognitive Analytics Framework. Journal of Mines, Metals and Fuels, 67(9), 416–423. Retrieved from https://informaticsjournals.com/index.php/jmmf/article/view/31650

Issue

Volume 67, Issue 9, September 2019

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

References

Rosienkiewicz M., Chlebus E., Detyna J. (2017): A hybrid spares demand forecasting method de dicate d to mining industry, Appl. Math. Model. 49, 87–107. doi:10.1016/j.apm. 2017.04.027.

Singh D.R., Mishra A.K. (2016): Review of IT enabled technologies in Indian mining industry for improved productivity & safety, in: Recent Adv. Inf. Technol. (RAIT), 2016 3rd Int. Conf., IEEE, pp. 613–618.

McKone K.E., Schroeder R.G., Cua K.O.(1999): Total productive maintenance: a contextual view, J. Oper. Manag. 17, 123–144.

Samanta B., Sarkar B., Mukherjee S.K., (2001): Reliability centred maintenance (RCM) for heavy earth-moving machinery in an open cast coal mine, CIM Bull. 104-107.

Lu Y., (2017): A survey on technologies , applications and open research issues, J. Ind. Inf. Integr. 6, 1-10. doi:10.1016/j.jii.2017.04.005.

Sriram C., Haghani A., (2003): An optimization model for aircraft maintenance scheduling and re-assignment, Transp. Res. Part A Policy Pract. 37 29-48.

Cooper B., (1996): Maintenance strategy procedures development and implementation, Min. Technol. 78 3- 6.

Brah S.A., Chong W. K., (2004): Relationship between total productive maintenance and performance, Int. J. Prod. Res. 42, 2383–2401.

Henderson K., Pahlenkemper G., Kraska O., (2014): Integrated Asset Management – An Investment in Sustainability, Procedia Eng. 83, 448-454. doi:10.1016/ j.proeng.2014.09.077.

Campos J., Sharma P., Gorostegui U., Jantunen E., Baglee D., (2017): A Big Data Analytical Architecture for the Asset Management, Procedia CIRP. 64, 369– 374. doi:10.1016/j.procir. 2017.03.019.

Gomes F., Michaelides A., (2005): Optimal life cycle asset allocation: Understanding the empirical evidence, J. Finance. 60, 869-904.

Pan E., Liao W., Xi L., (2010): Single-machine-based production scheduling model integrated preventive maintenance planning, Int. J. Adv. Manuf. Technol. 50, 365-375.

Taylor J.C., Patankar M.S., (2001): Four generations of maintenance resource management programs in the United States: An analysis of the past, present, and future,.

Barberá L., Crespo A., Viveros P., Stegmaier R., (2014): A case study of GAMM (graphical analysis for maintenance management) in the mining industry, Reliab. Eng. Syst. Saf. 121, 113-120. doi:10.1016/ j.ress.2013.07.017.

Sherwin D., (2000): A review of overall models for maintenance management, J. Qual. Maint. Eng. 6, 138- 164.

Muller A., Marquez A.C., Iung B., (2008): On the concept of e-maintenance: Review and current research, Reliab. Eng. Syst. Saf. 93, 1165-1187.

Veldman J., Klingenberg W., Wortmann H., (2011): Managing condition-based maintenance technology: A multiple case study in the process industry, J. Qual. Maint. Eng. 17, 40-62.

Rajlich V., (2014): Software evolution and maintenance, in: Proc. Futur. Softw. Eng., ACM,: pp. 133-144.

O’Brien L.G., (1989): Evolution and benefits of preventive maintenance strategies.

F.S.Nowlan, Heap H.F., (1978): Reliability-centered maintenance, United Air Lines Inc San Francisco Ca.

Dal B., Tugwell P., Greatbanks R., (2000): Overall equipment effectiveness as a measure of operational improvement–a practical analysis, Int. J. Oper. Prod. Manag. 20, 1488-1502.

Ljungberg Õ., (1998): Measurement of overall equipment effectiveness as a basis for TPM activities, Int. J. Oper. Prod. Manag. 18, 495-507.

Soukhanov A.H., Soukhanov A., (2001): Microsoft Encarta College Dictionary: The First Dictionary for the Internet Age, Macmillan.

Susto G.A., Schirru A., Pampuri S., McLoone S., Beghi A., (2015): Machine learning for predictive maintenance: A multiple classifier approach, IEEE Trans. Ind. Informatics. 11 812-820.

Korbicz J., Koscielny J.M., Kowalczuk Z., Cholewa W.: (2012): Fault diagnosis: models, artificial intelligence, applications, Springer Science & Business Media.

Sinha A.N., Mukherjee P.S., De A., (2000): Assessment of useful life of lubricants using artificial neural network, Ind. Lubr. Tribol. 52, 105-109.

Saranga H., Kumar U.D., (2006): Optimization of aircraft maintenance/support infrastructure using genetic algorithms - level of repair analysis, Ann. Oper. Res. 143, 91.

Heo J.-H., Kim M.-K., Park G.-P., Yoon Y.T., Park J.K., Lee S.-S., Kim D.-H., (2011): A reliability-centered approach to an optimal maintenance strategy in transmission systems using a genetic algorithm, IEEE Trans. Power Deliv. 26, 2171-2179.

Bravo C.E., Saputelli L., Rivas F., Pérez A.G., (2014): M. Nickolaou, G. Zangl, N. De Guzmán, S.D. Mohaghegh, G. Nunez, State of the art of artificial intelligence and predictive analytics in the E&P industry: A technology survey, Spe J. 19, 547-563.

Moavenzadeh J., (2015): The 4th Industrial Revolution: Reshaping the Future of Production, DHL Glob. Eng. Manuf. Summit, Amsterdam, Netherlands. 6-8.

Filip F.G., Donciulescu D.A., Filip C.I., (2002): Towards intelligent real-time decision support systems for industrial milieu, Stud. Informatics Control. 11, 303-312.

Turing A.M., (2009): Computing machinery and intelligence, in: Parsing Turing Test, Springer, pp. 23-65.

Fitouhi M.-C., Nourelfath M., (2012): Integrating noncyclical preventive maintenance scheduling and production planning for a single machine, Int. J. Prod. Econ. 136, 344-351.

Tam A.S.B., Chan W.M., Price J.W.H., (2006): Optimal maintenance intervals for a multi-component system, Prod. Plan. Control. 17 769-779.

Bentley J.P., Bentley J.P., (1999): Introduction to reliability and quality engineering, Addison-Wesley Reading, MA.

Yam R.C.M., Tse P.W., Li L., Tu P., (2001): Intelligent predictive decision support system for condition-based maintenance, Int. J. Adv. Manuf. Technol. 17, 383-391.

Smyth B., McKenna E., (2001): Competence models and the maintenance problem, Comput. Intell. 17, 235-249.

Mosheiov G., Sarig A., (2009): Scheduling a maintenance activity to minimize total weighted completion-time, Comput. Math. with Appl. 57, 619-623.

Cassady C.R., Kutanoglu E., (2005): Integrating preventive maintenance planning and production scheduling for a single machine, IEEE Trans. Reliab. 54, 304-309.

Yulan J., Zuhua J., Wenrui H., (2008): Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine, Int. J. Adv. Manuf. Technol. 39, 954-964.

Wu S., (2012): Assessing maintenance contracts when preventive maintenance is outsourced, Reliab. Eng. Syst. Saf. 98, 66-72.