FUZZY MODEL OF THE OPERATIONAL POTENTIAL CONSUMPTION PROCESS OF A COMPLEX TECHNICAL SYSTEM

Michał Pająk

DOI Number
10.22190/FUME200306032P
First page
453
Last page
472

Abstract


During the operation process of a system its technical state is changed. The changes take place because of the wearing factors impact. The impact depends on the flow and intensity of the operation process what is characterized by the time histories of the working parameters. Simultaneously, the changes of the technical state are correlated with the changes of the amount of the operational potential included in a system. In order to avoid the inability state occurrence the amount of this potential should be higher than the boundary value. The amount of the operational potential included in a system is determined by the values of the cardinal features of it but in the case of the real technical system the values cannot always be measured. Therefore, the amount of the operational potential and the technical state of the system cannot always be determined online. To solve this problem the model of the operational potential consumption process was created and presented in the paper. The model uses artificial intelligence techniques to calculate the change of the operational potential amount by determining the changes of the cardinal features of the system on the basis of the time histories of the working parameters. The verification of the model quality was performed using the pulverized boiler OP-650k-040 operating in the power plant. The description of the conducted research and the results of the verification were presented in the end of the paper proving the adequacy of the model implementation in the case of industrial objects.

Keywords

Fuzzy Model, Operational Potential Consumption, Complex Technical System, Operation, System Feature, Working Parameter

Full Text:

PDF

References


Muślewski, Ł., Pająk, M., Landowski, B., Żółtowski, B., 2016, A method for determining the usability potential of ship steam boilers, Polish Maritime Research, 92(4), pp. 105-112.

Mallick, A.R., 2015, Practical boiler operation engineering and power plant, PHI Learning Pvt. Ltd., Delhi, 594 p.

Pająk, M., 2017, Modelling of the operation and maintenance tasks of a complex power industry system in the fuzzy technical states space, Proc. 18th International scientific conference EPE 2017 (Electric Power Engineering), Kounty nad Desnou, Czech Republic, doi:10.1109/EPE.2017.7967234.

Pająk, M., 2017, Fuzzy modelling of cardinal features of a complex technical system, pp.62-78, ESREL 2016 European safety and reliability conference, CRC Press Taylor & Francis Group, London.

Pająk, M., 2015, Operational potential of a complex technical system, Maintenance Problems, 4, pp. 99-113.

Kostek, R., Landowski, B., Muślewski, Ł., 2015, Simulation of rolling bearing vibration in diagnostics, Journal of Vibroengineering, 17(8), pp. 4268-4278.

Landowski, B., Muślewski, Ł., 2017, Decision Model of an Operation and Maintenance Process of City Buses, Proc. 58th International conference of machine design departments ICMD 2017, Prague, Czech Republic.

Yonggang, M., Xu, J., Jin, Z., Braham, P., Yuanzhong, H., 2020, A review of recent advances in tribology, Friction, 8(2), pp. 221-300.

Hirani, H., 2016, Fundamentals of engineering tribology with applications, Cambridge University Press, Cambridge, 432 p.

Marcus, P., 2017, Corrosion mechanisms in theory and practice, CRC Press, Boca Raton, 742 p.

Ferrari, A., 2017, Fluid dynamics of acoustic and hydrodynamic cavitation in hydraulic power systems, Proceedings of the Royal Society A: Mathematical, Physical, and Engineering Sciences, 473. doi:10.1098/rspa.2016.0345.

Kirkwood, J.R., 2015, Markov processes, CRC Press, Boca Raton, 322 p.

Mandeliya, M., Vishwakarma, M., 2018, A review on boiler tube assessment in power plant using ultrasonic testing, International Research Journal of Engineering and Technology, 5(6), pp. 708-714.

Sarkar, B., Saren, S., 2016, Product inspection policy for an imperfect production system with inspection errors and warranty cost, European Journal of Operational Research, 2016; 248(1), pp. 263-271.

Schilling, E.G., Neubauer, D.V., 2017, Acceptance sampling in quality control, CRC Press, Boca Raton, 842 p.

Peña-Rodríguez, M.E., 2018, Serious about samples, Quality Progress, 51, pp. 18-23.

Zachwieja, J. 2015, Rules of storing of high-power electric motors, Logistics, 4(2), pp. 2152-2157.

Mendel, J.M., 2017, Uncertain rule-based fuzzy systems, Springer, Cham, 684 p.

Kahraman, C., Oztaysi, B., Çevik, O.S., Öner, S.C., 2018, Fuzzy sets applications in complex energy systems: a literature review, pp.15-37, Energy management collective and computational intelligence with theory and applications. Studies in systems, decision and control, Springer, Cham.

Lovato, A.V., Fontes, C.H., Embiruçu, M., Kalid, R., 2018, A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control, Computers & Industrial Engineering, 115, pp. 167-189.

Pająk, M., 2018, Fuzzy identification of a threat of the inability state occurrence, Journal of Intelligent and Fuzzy Systems, 35(3), pp. 3593-3604.

Wei, Y., Qiu, J., Karimi, H.R., 2017, Reliable output feedback control of discrete-time fuzzy affine systems with actuator faults, IEEE Transactions on Circuits and Systems I: Regular Papers, 64(1), pp. 170-181.

Pająk, M., 2015, Genetic-Fuzzy system of power units maintenance schedules generation, Journal of Intelligent and Fuzzy Systems, 28(4), pp. 1577-1589.

Kostikova, A., Tereliansky, P., Shuvaev, A., Parakhina, V., Timoshenko, P., 2016, Expert fuzzy modeling of dynamic properties of complex systems, Journal of Engineering and Applied Sciences, 11(17), pp. 10222-10230.

Wei, Y., Qiu, J., Karimi, R.H., 2018, Fuzzy-Affine-Model-Based memory filter design of nonlinear systems with time-varying delay, IEEE Transactions on Fuzzy Systems, 26(2), pp. 504-517.

Rodríguez-Fdez, I., Mucientes, M., Bugarín, A., 2016, FRULER: Fuzzy rule learning through evolution for regression, Information Sciences, 354, pp. 1-18.

Pająk, M., Muślewski, Ł., Landowski, B., 2018, Optimisation of changes of the operation quality of the transportation system in the fuzzy quality states space, IOP Conference Series: Materials Science and Engineering, 421: 032023.

Bray, S., Caggiani, L., Ottomanelli, M., 2015, Measuring transport systems efficiency under uncertainty by fuzzy sets theory based data envelopment analysis: theoretical and practical comparison with traditional DEA model, Transportation Research Procedia, 5, pp. 186-200.

Muślewski, Ł., Pająk, M., Grządziela, A., Musiał, J., 2015, Analysis of vibration time histories in the time domain for propulsion systems of minesweepers, Journal of Vibroengineering, 7(3), pp. 1309-1316.

Pająk, M., 2018, Identification of the operating parameters of a complex technical system important from the operational potential point of view, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 232(1), pp. 62-78.

Pourjavad, E., Mayorga, R.V., 2019, A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system, Journal of Intelligent Manufacturing, 30(3), pp. 1085-1097.

Zoukit, A., El Ferouali, H., Salhi, I., Doubabi, S., Abdenouri, N., 2019, Fuzzy modeling of a hybrid solar dryer: experimental validation, Journal of Energy Systems, 3(1), pp. 1-13.

Štěpnička, M., Jayaram, B., Su, Y., 2018, A short note on fuzzy relational inference systems, Fuzzy Sets and Systems, 338, pp. 90-96.

Biezma, M.V., Agudo, D., Barron, G., 2018, A fuzzy logic method: predicting pipeline external corrosion rate, International Journal of Pressure Vessels and Piping, 163, pp. 55-62.

Meena, P.K., Bhushan, B., 2017, Simulation for position control of DC motor using fuzzy logic, International Journal of Electronics, Electrical and Computational System, 6(6), pp. 188-191.

Voskoglou, M., 2020, Fuzzy Sets, Fuzzy Logic and Their Applications, MDPI, Basel, 366 p.

Stojcic, M., Stjepanovic, A., Stjepanovic, D., 2019, ANFIS model for the prediction of generated electricity of photovoltaic modules, Decision Making: Applications in Management and Engineering, 2(1), pp. 35-48.

Vilela, M., Oluyemi, G., Petrovski, A., 2019, A fuzzy inference system applied to value of information assessment for oil and gas industry, Decision Making: Applications in Management and Engineering, 2(2), pp. 1-18.

Sremac, S., Tanackov, I., Kopić, M., Radović, D., 2018, ANFIS model for determining the economic order quantity, Decision Making: Applications in Management and Engineering, 1(2), pp. 81-92.

Fernández, A., López, V., del Jesus, J.M., Herrera, F., 2015, Revisiting evolutionary fuzzy systems: taxonomy, applications, new trends and challenges, Knowledge-Based Systems, 80, pp. 109 121.

Cordon, O., del Jesus, M., Herrera, F., Evolutionary approaches to the learning of fuzzy rule based classification systems, [online], Available from [1 April 2017]

Alcala, R., Casillas, J., Cordon, O., Herrera, F., Zwir, I. Techniques for learning and tuning fuzzy rule-based system for linguistic modeling and their application, [online], Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.3817&rep=rep1&type=pdf [last access: 1 November 2017].

Polish Agency of Energy Market, 2015, Catalogue of power plants and CHP plants, Warsaw.

http://www.rafako.com.pl/products/boilers/668/pulverised-fuel-fired-boilers-with-steam-drum#type

_548 [last access: 12 February 2019].




DOI: https://doi.org/10.22190/FUME200306032P

Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4