Received:
2018-08-15 | Accepted:
2018-10-20 | Published:
2018-12-30
Title
Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks
Abstract
One of the most important problems of creating new and also modernizing and operating the existing industrial equipment is to provide it with technical diagnostic tools. In modern systems, most diagnostic problems are solved by vibration monitoring methods, and they form the basis of this process. For several years already, when creating new responsible equipment, many manufacturers have completed it with monitoring and diagnostic systems, often integrating them functionally with automatic control systems. This paper discusses the methods of servicing industrial equipment, focusing on predictive maintenance, also known as actual maintenance (maintenance according to the actual technical condition).The rationale for the use of wireless systems for data collection and processing is presented. The principles of constructing wireless sensor networks and the data transmission protocols used to collect statistical information on the state of the elements of industrial equipment, depending on the field of application, are analyzed. The purpose of the study is to substantiate the feasibility of using wireless sensor networks as technical diagnostic tools from both economic and technical points of view. The result is the proposed concept of the predictive maintenance system. The paper substantiates the model of optimization of predic-tive repair using wireless sensor networks. This approach is based on minimizing the costs of maintenance of equipment. The presented concept of a system of predictive maintenance on the basis of sensor networks allows real-time analysis of the state of equipment. The approach allows implementing smart management of technologies in companies for ensuring stability of functioning.
Keywords
management of technologies, monitoring of technological processes, industrial equipment, predictive repair, smart management concept
JEL classifications
D24
, C14
, C80
URI
http://jssidoi.org/jesi/article/233
DOI
Pages
489-502
Funding
The research was conducted with the support of the Ministry of Education and Science of Russia within the framework of the project under the Agreement No.14.579.21.0142 UID RFMEFI57917X0142.
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
References
Amruthnath, N.; & Gupta, T. 2018. A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance, In: 2018 5th International Conference on Industrial Engineering and Applications, IEEE, 355-361, . https://doi.org/10.13140/RG.2.2.28822.24648
Search via ReFindit
Andreev, K.A.: Vlasov, A.I.; & Shakhnov, V.A. 2016. Silicon pressure transmitters with overload protection, Automation and Remote Control 77(7): 1281-1285, . https://doi.org/10.1134/S0005117916070146
Search via ReFindit
Baptista, M.; Sankararaman, S.; de Medeiros, I.P.; Nascimento, C.; Prendinger, H.; & Henriques, E.M.P. 2018. Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling, Computers & Industrial Engineering 115: 41-53, . https://doi.org/10.1016/j.cie.2017.10.033
Search via ReFindit
Bogdanov, S.P.; & Basov, O.O. 2012. Prospects and problems of using wireless sensors with autonomous power supply, TUSUR Reports 2/1(26): 20-23.
Search via ReFindit
Brüel; & Kjær. 1991. Monitoring the state of machinery. DKBR 0660-11. Denmark: Nerum. 47 p.
Search via ReFindit
Burduk, A.; & Chlebus, E. 2009. Evaluation of the risk in production systems with a parallel reliability structure, EksploatacjaiNiezawodność– Maintenance and Reliability 2(42): 84-95.
Search via ReFindit
Cook, D.; Das, S. 2005. Smart Environments. Technologies, protocols and applications. Hoboken NJ: Wiley-Interscience.
Search via ReFindit
Crowder, M.; & Lawless, J. 2007. On a scheme for predictive maintenance, European Journal of Operational Research 176: 1713-1722, . https://doi.org/10.1016/j.ejor.2005.10.051
Search via ReFindit
Curcuru, G.; Cocconcelli, M.; Rubini, R.; & Galante, G.M. 2017. System monitoring and maintenance policies: a review. Available at: https://iris.unimore.it/handle/11380/1144821
Search via ReFindit
Freyden, J. 2005. Modern sensors. Reference book (Transl. from English by Yu.A. Zabalotnaya under the editorship of. E.L. Svintsova), Tekhnosfera, Moscow.
Search via ReFindit
Gotra, Z.Yu.; & Tchaikovsky, O.I. (Eds.). 1995. Sensors. Reference book, Kamenyar, Lviv.
Search via ReFindit
Kozlova, E.I. 2009. Metrological support of information processing systems: A summary of lectures, BSU, Minsk.
Search via ReFindit
Lu, C. J., & Meeker, W. O. (1993). Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics, 35(2), 161–174. https://doi.org/10.1080/00401706.1993.10485038
Search via ReFindit
Mobley, R.K. 2002. An introduction to predictive maintenance, Elsevier Science.
Search via ReFindit
Prause, G.; Atari, S. 2017. On sustainable production networks for Industry 4.0, Entrepreneurship and Sustainability Issues 4(4): 421-431, . https://doi.org/10.9770/jesi.2017.4.4(2)
Search via ReFindit
Rausand, M.; & Hoyland, A. 2004. System reliability theory: Models, statistical methods and applications, Wiley.
Search via ReFindit
Rawi, Z. 2010. Machinery Predictive Analytics, In: SPE Intelligent Energy Conference and Exhibition, 23-25 March, Utrecht, the Netherlands, Society of Petroleum Engineers. . https://doi.org/10.2118/128559-MS
Search via ReFindit
Shakhnov, V.A.; Vlasov, A.I.; Rezchikova, E.V.; Tokarev, S.V.; Smurygin, I.M.; Denisenko, N.A.; & Muravev, K.A. 2013. Method of functioning of the wireless sensor network. Patent for the invention of the Russian Federation No. 2556423.
Search via ReFindit
Sharapov, V.M.; & Polischuk, E.S. (Eds.). 2012. Sensors: Reference guide, Tekhnosfera, Moscow.
Search via ReFindit
Stone, P. 2007. Introducing Predictive Analytics: Opportunities, . https://doi.org/10.2118/106865-MS
Search via ReFindit
Vlasov, A.I.; Grigoriev, P.V.; & Zhalnin, V.P. 2017b. Application of the methods and means of radio frequency identification in the corporate information production systems, In: Proceedings of the International Symposium "Reliability and Quality" 1: 272-277.
Search via ReFindit
Vlasov, A.I.; Ivanov, V.V.; & Kosolapov, I.A. 2011. Methods of proactive prediction of broadband network status, Software Products and Systems 1: 3-6.
Search via ReFindit
Vlasov, A.I.; Yudin, A.V.; Salmina, M.A.; Shakhnov, V.A.; & Usov, K.A. 2017a. Design methods of teaching the development of internet of things components with considering predictive maintenance on the basis of mechatronic devices, International Journal of Applied Engineering Research 12(20): 9390-9396.
Search via ReFindit
Whitaker, D.A.; Egan, D.; O’Brien, E.; & Kinnear, D. 2018. Application of multivariate data analysis to machine power measurements as a means of tool life predictive maintenance for reducing product waste. Available at: https://arxiv.org/abs/1802.08338
Search via ReFindit
Yudin, A.: Kolesnikov, M.; Vlasov, A.; & Salmina, M. 2017. Project oriented approach in educational robotics: From robotic competition to practical appliance, Advances in Intelligent Systems and Computing 457: 83-94, . https://doi.org/10.1007/978-3-319-42975-5_8
Search via ReFindit