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Diagnosing of Risk State in Subsystems of CNC Turning Center using Interval Type-2 Fuzzy Logic System with Semi Elliptic Membership Functions

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Abstract

Precise monitoring, diagnosis, and control of subtractive machines like turning centers are challenging tasks in automated manufacturing industries. To identify the failures in a subsystem of a turning center, all the required parameters need to be recorded, stored, and retrieved systematically for effective monitoring and control. In this work, the experimental data of a CNC turning center available in a repository are classified with interval type 2 fuzzy logic system (IT2FLS) using semi-elliptic membership function (SEMF) to predict the risk state of each subsystem. The geomantic property of SEMF has shown faster convergence and less computation load. The simulated results of each subsystem governed by the SEMF are compared with Gaussian membership function (GMF) to evaluate its potential. The research out comes confirms that the computation load of IT2FLS is considerably reduced based on the implementation of Wu–Mendel uncertainty bounds.

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References

  1. Wainer, H.: Eelworms, Bullet Holes, and Geraldine Ferraro some problems in statistically adjusting for survey nonresponse. ETS Res. Rep. Ser. 1(1), i–19 (1987). https://doi.org/10.1002/j.2330-8516.1987.tb00216.x

    Article  Google Scholar 

  2. Mittal, K., Jain, A., Vaisla, K.S., Castillo, O., Kacprzyk, J.: A comprehensive review on type 2 fuzzy logic applications: past, present and future. Eng. Appl. Artif. Intell. 95(September), 103916 (2020). https://doi.org/10.1016/j.engappai.2020.103916

    Article  Google Scholar 

  3. Shukla, A.K., Banshal, S.K., Seth, T., Basu, A., John, R., Muhuri, P.K.: A bibliometric overview of the field of type-2 fuzzy sets and systems [discussion forum]. IEEE Comput. Intell. Mag. 15(1), 89–98 (2020). https://doi.org/10.1109/MCI.2019.2954669

    Article  Google Scholar 

  4. Castillo, O., Muhuri, P.K., Melin, P., Pulkkinen, P.: Emerging issues and applications of type-2 fuzzy sets and systems. Eng. Appl. Artif. Intell. 90, 2–4 (2020). https://doi.org/10.1016/j.engappai.2020.103596

    Article  Google Scholar 

  5. Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Comput. Intell. Mag. 2(1), 20–29 (2007). https://doi.org/10.1109/MCI.2007.380672

    Article  Google Scholar 

  6. Wu, H., Mendel, J.M.: Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 10(5), 622–639 (2002). https://doi.org/10.1109/TFUZZ.2002.803496

    Article  Google Scholar 

  7. Liu, F.: An efficient centroid type-reduction strategy for general type-2 fuzzy logic system. Inf. Sci. (Ny) 178(9), 2224–2236 (2008). https://doi.org/10.1016/j.ins.2007.11.014

    Article  MathSciNet  Google Scholar 

  8. Mendel, J.M.: Computing derivatives in interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 12(1), 84–98 (2004). https://doi.org/10.1109/TFUZZ.2003.822681

    Article  Google Scholar 

  9. Ontiveros, E., Melin, P., Castillo, O.: Comparative study of interval type-2 and general type-2 fuzzy systems in medical diagnosis. Inf. Sci. (Ny) 525, 37–53 (2020). https://doi.org/10.1016/j.ins.2020.03.059

    Article  MathSciNet  Google Scholar 

  10. Castillo, O., Amador-Angulo, L.: A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design. Inf. Sci. (Ny) 460–461, 476–496 (2018). https://doi.org/10.1016/j.ins.2017.10.032

    Article  Google Scholar 

  11. Fazel Zarandi, M.H., Soltanzadeh, S., Mohammadi, A., Castillo, O.: Designing a general type-2 fuzzy expert system for diagnosis of depression. Appl. Soft Comput. J. 80(2019), 329–341 (2019). https://doi.org/10.1016/j.asoc.2019.03.027

    Article  Google Scholar 

  12. Castillo, O., Cervantes, L., Soria, J., Sanchez, M., Castro, J.R.: A generalized type-2 fuzzy granular approach with applications to aerospace. Inf. Sci. (Ny) 354, 165–177 (2016)

    Article  Google Scholar 

  13. Muhuri, P.K., Shukla, K.K.: Real-time task scheduling with fuzzy uncertainty in processing times and deadlines. Appl. Soft Comput. J. 8(1), 1–13 (2008). https://doi.org/10.1016/j.asoc.2006.06.006

    Article  Google Scholar 

  14. Khanesar, M.A., Kayacan, E., Teshnehlab, M., Kaynak, O.: Analysis of the noise reduction property of type-2 fuzzy logic systems using a novel type-2 membership function. IEEE Trans. Syst. Man Cybern. Part B Cybern. 41(5), 1395–1406 (2011). https://doi.org/10.1109/TSMCB.2011.2148173

    Article  Google Scholar 

  15. Masumpoor, S., Yaghobi, H., Ahmadieh Khanesar, M.: Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor. Expert Syst. Appl. 42(19), 6635–6647 (2015). https://doi.org/10.1016/j.eswa.2015.04.046

    Article  Google Scholar 

  16. Muhuri, P.K., Shukla, A.K.: Semi-elliptic membership function: representation, generation, operations, defuzzification, ranking and its application to the real-time task scheduling problem. Eng. Appl. Artif. Intell. 60(May 2016), 71–82 (2017). https://doi.org/10.1016/j.engappai.2016.12.020

    Article  Google Scholar 

  17. Kayacan, E., Sarabakha, A., Coupland, S., John, R.: “Type-2 fuzzy elliptic membership functions for modeling uncertainty. Eng. Appl. Artif. Intell. 70(2017), 170–183 (2018). https://doi.org/10.1016/j.engappai.2018.02.004

    Article  Google Scholar 

  18. Dutta, P., Saikia, B.: Arithmetic operations on normal semi elliptic intuitionistic fuzzy numbers and their application in decision-making. Granul. Comput. (2019). https://doi.org/10.1007/s41066-019-00175-5

    Article  Google Scholar 

  19. Shukla, A.K., Muhuri, P.K.: Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets. Eng. Appl. Artif. Intell. 77(July 2018), 268–282 (2019). https://doi.org/10.1016/j.engappai.2018.09.002

    Article  Google Scholar 

  20. Dutta, P.: Mathematics of uncertainty: an exploration on semi-elliptic fuzzy variable and its properties. SN Appl. Sci. (2020). https://doi.org/10.1007/s42452-019-1871-8

    Article  Google Scholar 

  21. Vashisth, P., Khurana, P., Bedi, P.: A fuzzy hybrid recommender system. J. Intell. Fuzzy Syst. 32(6), 3945–3960 (2017). https://doi.org/10.3233/JIFS-14538

    Article  Google Scholar 

  22. Ding, S., Huang, X., Ban, X., Lu, H., Zhang, H.: Type-2 fuzzy logic control for underactuated truss-like robotic finger with comparison of a type-1 case 1. J. Intell. Fuzzy Syst. 33(4), 2047–2057 (2017). https://doi.org/10.3233/JIFS-161538

    Article  Google Scholar 

  23. Kececioglu, O.F.: Robust control of high gain DC-DC converter using Type-2 fuzzy neural network controller for MPPT. J. Intell. Fuzzy Syst. 37(1), 941–951 (2019). https://doi.org/10.3233/JIFS-181770

    Article  Google Scholar 

  24. Eyoh, I.J., Umoh, U.A., Inyang, U.G., Eyoh, J.E.: Derivative-based learning of interval type-2 intuitionistic fuzzy logic systems for noisy regression problems. Int. J. Fuzzy Syst. 22(3), 1007–1019 (2020). https://doi.org/10.1007/s40815-020-00806-z

    Article  Google Scholar 

  25. Xu, X., Su, P., Wang, F., Chen, L., Xie, J., Atindana, V.A.: Coordinated control of dual-motor using the interval type-2 fuzzy logic in autonomous steering system of AGV. Int. J. Fuzzy Syst. (2020). https://doi.org/10.1007/s40815-020-00886-x

    Article  Google Scholar 

  26. Zhao, T., Yu, Q., Dian, S., Guo, R., Li, S.: Non-singleton general type-2 fuzzy control for a two-wheeled self-balancing robot. Int. J. Fuzzy Syst. 21(6), 1724–1737 (2019). https://doi.org/10.1007/s40815-019-00664-4

    Article  MathSciNet  Google Scholar 

  27. Huynh, T.T., et al.: A new self-organizing fuzzy cerebellar model articulation controller for uncertain nonlinear systems using overlapped Gaussian membership functions. IEEE Trans. Ind. Electron. 67(11), 9671–9682 (2020). https://doi.org/10.1109/TIE.2019.2952790

    Article  Google Scholar 

  28. Cuevas, F., Castillo, O., Cortes-Antonio, P.: Optimal Design of Interval Type-2 Fuzzy Tracking Controllers of Mobile Robots Using a Metaheuristic Algorithm, pp. 315–341. Springer, Cham (2021)

    MATH  Google Scholar 

  29. Seth, T., Muhuri, P. K.: Type-2 fuzzy set based hesitant fuzzy linguistic term sets for linguistic decision making. (2020). arXiv preprint arXiv:2002.11714

  30. Tolga, A.C., Parlak, I.B., Castillo, O.: Finite-interval-valued type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem. Eng. Appl. Artif. Intell. 87(2019), 103352 (2020). https://doi.org/10.1016/j.engappai.2019.103352

    Article  Google Scholar 

  31. Sreekumar, M.: A robot manipulator with adaptive fuzzy controller in obstacle avoidance. J. Inst. Eng. Ser. C 97(3), 469–478 (2016). https://doi.org/10.1007/s40032-015-0215-8

    Article  Google Scholar 

  32. Sreekumar, M., Nagarajan, T., Singaperumal, M., Zoppi, M., Molfino, R.: Training of a fuzzy logic controller using table lookup scheme for the control of SMA actuators”. Int. J. Math. Sci. 5(2), 335–353 (2006)

    Google Scholar 

  33. Chen, J., Rine, D.C.: Training fuzzy logic controller software components by combining adaptation algorithms. Adv. Eng. Softw. 34(3), 125–137 (2003). https://doi.org/10.1016/S0965-9978(02)00140-0

    Article  MATH  Google Scholar 

  34. Khater, A.A., El-Nagar, A.M., El-Bardini, M., El-Rabaie, N.M.: Online learning of an interval type-2 TSK fuzzy logic controller for nonlinear systems. J. Franklin Inst. 356(16), 9254–9285 (2019). https://doi.org/10.1016/j.jfranklin.2019.08.031

    Article  MathSciNet  MATH  Google Scholar 

  35. Mendel, J.M.: A quantitative comparison of interval type-2 and type-1 fuzzy logic systems: First results. 2010 IEEE World Congr. Comput. Intell. WCCI 2010 (2010). https://doi.org/10.1109/FUZZY.2010.5584727

    Article  Google Scholar 

  36. Wu, D.: Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. IEEE Int. Conf. Fuzzy Syst. (2012). https://doi.org/10.1109/FUZZ-IEEE.2012.6251210

    Article  Google Scholar 

  37. Prasad, B.S., Rao, J.U., Krishna, A.G.: Analysis of vibration signals to quantify displacement amplitude in the monitoring of vibration-assisted turning. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 233(1), 35–47 (2019). https://doi.org/10.1177/0954408917742196

    Article  Google Scholar 

  38. El-Bouri, W., Deiab, I., Khanafer, K., Wahba, E.: Numerical and experimental analysis of turbulent flow and heat transfer of minimum quantity lubrication in a turning process using discrete phase model. Int. Commun. Heat Mass Transf. 104, 23–32 (2019). https://doi.org/10.1016/j.icheatmasstransfer.2019.02.012

    Article  Google Scholar 

  39. Angadi, S.V., et al.: Reliability and life study of hydraulic solenoid valve. Part 2: experimental study. Eng. Fail. Anal. 16(3), 944–963 (2009). https://doi.org/10.1016/j.engfailanal.2008.08.012

    Article  Google Scholar 

  40. Penman, J., Sedding, H.G., Lloyd, B.A., Fink, W.T.: Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Trans. Energy Convers. 9(4), 652–658 (1994). https://doi.org/10.1109/60.368345

    Article  Google Scholar 

  41. Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems. Springer, Cham (2017)

    Book  Google Scholar 

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Acknowledgements

The authors sincerely thank the Ministry of Electronics and Information Technology (MeitY), Government of India, for providing financial assistance under Visvesvaraya PhD Scheme (MEITY-PHD-1558) to carry out this research work at IIITDM Kancheepuram, Chennai, India.

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  1. Center for AI, IoT, and Robotics, Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, India

    K. B. Badri Narayanan & M. Sreekumar

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  1. K. B. Badri Narayanan
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  2. M. Sreekumar
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Correspondence to M. Sreekumar.

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Badri Narayanan, K.B., Sreekumar, M. Diagnosing of Risk State in Subsystems of CNC Turning Center using Interval Type-2 Fuzzy Logic System with Semi Elliptic Membership Functions. Int. J. Fuzzy Syst. 24, 823–840 (2022). https://doi.org/10.1007/s40815-021-01172-0

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