Abstract
The article presents selected cases of application of fuzzy-logic techniques in technological process control. It presents the possibilities of supporting manufacturers of road machines for road drying with innovative solutions in the field of artificial intelligence. It presents an algorithmic approach to determine the quality (welfare) of the device, taking into account important process parameters, processed with the use of fuzzy-logic technique. The methodology for controlling the rotation of the turbine engine in the initial phase of its start-up is presented, using rules based on fuzzy logic. The results of the calculations are presented in a graphical form, friendly to interpretation by users and machine manufacturer. The article discusses the technical aspects of the TORGOS road machine control system, indicating the multifunctionality of the authors’ controller and its software.
The authors would like to thank PHU CEMAR Import-Export for kind permission to publish joint research results.
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Notes
- 1.
Mostly high-speed ball bearing.
References
Beg, I., Rashid, T.: Modelling uncertainties in multi-criteria decision making using distance measure and topsis for hesitant fuzzy sets. J. Artif. Intell. Soft Comput. Res. 7(2), 103–109 (2017). https://content.sciendo.com/view/journals/jaiscr/7/2/article-p103.xml
Cpalka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 3, pp. 1764–1769. IEEE (2005)
Dâ\(^{\text{TM}}\) Aniello, G., Gaeta, M., Loia, F., Reformat, M., Toti, D.: An environment for collective perception based on fuzzy and semantic approaches. J. Artif. Intell. Soft Comput. Res. 8(3), 191–210 (2018). https://content.sciendo.com/view/journals/jaiscr/8/3/article-p191.xml
Ferdaus, M.M., Anavatti, S.G., Garratt, M.A., Pratama, M.: Development of c-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. J. Artif. Intell. Soft Comput. Res. 9(2), 99–109 (2019). https://content.sciendo.com/view/journals/jaiscr/9/2/article-p99.xml
Huang, Y.C., Sun, H.C.: Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic. IEEE Trans. Dielectr. Electr. Insul. 20(3), 974–981 (2013)
Kacprzyk, J.: Multistage Fuzzy Control: A Prescriptive Approach. Wiley, New York (1997)
Liu, M., Cao, X., Shi, P.: Fault estimation and tolerant control for fuzzy stochastic systems. IEEE Trans. Fuzzy Syst. 21(2), 221–229 (2013)
PHU Cemar Import-Export: Webpage (2019). http://cemar.pro/
Prasad, M., Liu, Y.T., Li, D.L., Lin, C.T., Shah, R.R., Kaiwartya, O.P.: A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017). https://content.sciendo.com/view/journals/jaiscr/7/1/article-p33.xml
Rafajlowicz, E.: Optimal experiment design for identification of linear distributed-parameter systems: frequency domain approach. IEEE Trans. Autom. Control 28(7), 806–808 (1983)
Rafajłowicz, E., Skubalska-Rafajłowicz, E.: RBF nets for approximating an object’s boundary by image random sampling. Nonlinear Anal. Theor. Methods Appl. 71(12), e1247–e1254 (2009)
Rafajłowicz, E., Wietrzych, J., Rafajłowicz, W.: A computer vision system for evaluation of high temperature corrosion damages in steam boilers. In: Korbicz, J., Kowal, M. (eds.) Intelligent Systems in Technical and Medical Diagnostics. AISC, vol. 230, pp. 391–402. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-39881-0_33
Riid, A., Preden, J.S.: Design of fuzzy rule-based classifiers through granulation and consolidation. J. Artif. Intell. Soft Comput. Res. 7(2), 137–147 (2017). https://content.sciendo.com/view/journals/jaiscr/7/2/article-p137.xml
Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control Cybern. 31(2), 297–308 (2002)
Rutkowski, L.: Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation, vol. 771. Springer, New York (2006). https://doi.org/10.1007/b115533
Rzeszuciński, P., Orman, M., Pinto, C., Krishnamoorthi, K.: Wykrywanie uszkodzeń łożysk tocznych z wykorzystaniem sygnałów akustycznych rejestrowanych telefonem komórkowym. Maszyny Elektryczne - Zeszyty Problemowe 2(110), 163–168 (2016)
Starczyński, J., Sułowicz, M.: Wykrywanie uszkodzeń w silnikach indukcyjnych w oparciu o sygnały akustyczne. Maszyny Elektryczne - Zeszyty Problemowe 4(108), 149–156 (2015)
Zhao, Y., Liu, Q.: A continuous-time distributed algorithm for solving a class of decomposable nonconvex quadratic programming. J. Artif. Intell. Soft Comput. Res. 8(4), 283–291 (2018). https://content.sciendo.com/view/journals/jaiscr/8/4/article-p283.xml
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Rafajłowicz, W., Domski, W., Jabłoński, A., Ratajczak, A., Tarnawski, W., Zajda, Z. (2019). Fuzzy Reasoning in Control and Diagnostics of a Turbine Engine – A Case Study. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_32
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