آمار

تعداد نشریات11
تعداد شماره‌ها228
تعداد مقالات2,324
تعداد مشاهده مقاله3,703,491
تعداد دریافت فایل اصل مقاله2,717,592
Omoregbee, H. O., Olanipekun, M. U., Okwu, M. O., Edward, B. A.. (1401). Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms. فناوری آموزش, 12(1), 121-132. doi: 10.22061/jcarme.2021.6760.1869
H. O. Omoregbee; M. U. Olanipekun; M. O. Okwu; B. A. Edward. "Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms". فناوری آموزش, 12, 1, 1401, 121-132. doi: 10.22061/jcarme.2021.6760.1869
Omoregbee, H. O., Olanipekun, M. U., Okwu, M. O., Edward, B. A.. (1401). 'Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms', فناوری آموزش, 12(1), pp. 121-132. doi: 10.22061/jcarme.2021.6760.1869
Omoregbee, H. O., Olanipekun, M. U., Okwu, M. O., Edward, B. A.. Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms. فناوری آموزش, 1401; 12(1): 121-132. doi: 10.22061/jcarme.2021.6760.1869

Bearing fault prognostics using Takagi-Sugeno of extended fuzzy with recursive least square algorithms

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 10، دوره 12، شماره 1 - شماره پیاپی 23، مهر 2022، صفحه 121-132    اصل مقاله (1.3 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2021.6760.1869
نویسندگان
H. O. Omoregbee* 1؛ M. U. Olanipekun2؛ M. O. Okwu3؛ B. A. Edward3
1Department of Mechanical Engineering, University of Lagos, Akoka, Lagos, Nigeria
2Electrical Engineering Department, Tshwane University of Technology, eMalahleni Campus, South Africa
3Mechanical Engineering Department, Federal University of Petroleum Resources, Effurun. Nigeria
تاریخ دریافت: 27 فروردین 1399،  تاریخ بازنگری: 24 مهر 1400،  تاریخ پذیرش: 12 آبان 1400 
چکیده
This paper presents the detection of fault prognostics in bearings with the application of extended Takagi-Sugeno fuzzy recursive least square algorithms (exTSFRLSA). The nonlinear system is decomposed into a multi-model structure, consisting of linear models that are not inherently independent, due to the fuzzy regions defined in exTSFRLSA. The exTSFRLSA was developed to tune, adjust and adapt the parameters involved in the propagation model, as it tends to update itself with the availability of new data. This method is suitable for the online identification of systems because of its unsupervised learning pattern which dwells on a mechanism cantered on rule-based evolution. Scenarios considered for the rule-based modification and upgrade are quite diverse, thereby ensuring effective comparison of measured and predicted defect size. An estimation of the remaining useful life was determined successfully with the proposed method, showing that the system performance health indicator reflects bearing degradation, and it was concluded that exTSFRLSA can be used for fault prediction of bearing where rolling element  are involved, especially while its operation is associated with fluctuating speed and load conditions.
کلیدواژه‌ها
Prognostics؛ Rolling element bearing؛ Remaining useful life؛ Extended Takagi Sugeno؛ Fuzzy؛ Recursive least squares method
مراجع
[1] L. Liao and F. Köttig, “Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction”, IEEE Trans. Reliab. Soc., Vol. 63, No. 1, pp. 191 – 207, (2014). 

[2] H. O. Omoregbee and S. Heyns, “Fault detection in roller bearing operating at low speed and varying loads using Bayesian Robust New Hidden Markov Model”, J. of Mech. Sci. Technol., Vol. 32, No. 9, pp. 4025 – 4036, (2018). 

[3] Y. Y. Lin, J-Y. Chang and C-T. Lin, “A TSK-Type-Based self-evolving compensatory interval type-2 fuzzy neural network (TSCIT2FNN) and its applications”, IEEE Trans. Ind. Electr., Vol. 61, No. 1, pp. 447 – 459, (2014). 

[4] S. S. Ge, F. Hong and T. H. Lee, “Adaptive neural network control of nonlinear systems with unknown time delays”, IEEE, Trans. Auto. Cont., Vol. 48, No. 11, pp. 204-210, (2003).  

[5] M. J. Jesus, F. Hoffmann, L. J. Navascués and L. Sánchez, “Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms”, IEEE Trans. Fuzzy Syst., Vol. 12, No. 3, pp. 296-308,  (2004).

[6] N. K. Kasabov and Q. Song, “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction”, IEEE Trans. Fuzzy Syst., Vol. 10, No. 2, pp.144 – 154, (2002). 

[7] Source: ISO 13372: 2012, 10.2

[8] D. A. Tobon-Mejia, K. Medjaher, N. Zerhouni and G. Tripot, “A data-driven failure prognostics method based on mixture of Gaussians Hidden Markov Models”, IEEE Trans. Reliab.,  Vol. 61, No. 2, pp. 491–503, (2012). 

[9] Source: ISO 13372: 2012, 1.15

[10] H. Han and J. Qiao, “A self organizing fuzzy neural network based on a growing-and-pruning algorithm”, IEEE Trans. Fuzzy Syst., Vol. 18, No. 6, pp. 1129-1143, (2010). 

[11] J. de J. Rubio, “SOFMLS: Online self-organizing fuzzy modified least-squares network”, IEEE Trans. Fuzzy Syst., Vol. 17, No. 6, pp. 1296 – 1309, (2009). 

[12] M. El-Koujok, R. Gouriveau and N. Zerhouni, “Error estimation of a neuro-fuzzy predictor for prognostic purpose”, IFAC Proc., Vol. 42, No. 8, pp. 131 – 136, (2009). 

[13] R. Gouriveau and N. Zerhouni,  “Connexionist-systems-based long term prediction approaches for prognostics”, IEEE Trans. Reliab., Vol. 61, No. 4, pp. 909–920, (2012). 

[14] P. Angelov and X. Zhou, “Evolving fuzzy-rule-based classifiers from data streams”, IEEE Trans. Fuzzy Syst., Vol. 16, No. 6, pp. 1462-1475, (2008). 

[15] H. Du and N. Zhang, “Application of evolving takagi–sugeno fuzzy model to nonlinear system identification”, Appl. Soft Comput., Vol. 8, No. 1, pp. 676-686, (2008). 

[16]  Z. Deng, L. Cao, Y. Jiang and S. Wang, “Minimax probability TSK fuzzy system classifier: a more transparent and highly interpretable classification model”, IEEE Trans. Fuzzy Syst., Vol. 23, No. 4, pp. 813 – 826, (2015). 

[17] L. Ai, Y. Xu, L. Deng and K. L. Teo, “Least squares support vector machine-based multivariate generalized predictive control for parabolic distributed parameter systems with control constraints”, MDPI, Vol. 13, No. 453, pp. 1-14, (2021).

[18]. S. Wu and M. J. Er, “Dynamic fuzzy neural networks—a novel approach to function approximation”, IEEE Trans. Syst., Manuf., Cybern., —Part B: Vol. 30, No. 2, pp. 358–364, (2000). 

[19]  T. Tagaki and M. Sugeno, “Fuzzy identification of systems and its application to modelling and control”, IEEE Trans. Syst., Manuf. Cybern. Vol. 15, No. 1, pp. 116 – 132, (1985). 

[20]  L. Cheng, Z. Wang, F. Jiang and J. Li, “Adaptive neural network control of nonlinear systems with unknown dynamics”, Adv. Space Res. Vol. 67, No. 3, pp. 1114-1123, (2021). 

[21] H. Espitia, J. Soriano, I. Machón and H. López, “Design methodology for the implementation of fuzzy inference systems based on boolean relations”, MDPI, Vol. 8, No. 1243, pp. 1–28, (2019). 

[22] H. Ishibuchi and T. Nakashima, “Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes”, IEEE Trans. Ind. Electr., Vol. 46, No. 6, pp. 1057 – 1068, (1999). 

[23]. M. Setnes and H. Roubos, “A GA-based fuzzy modeling approach for generating TSK models”, Fuzzy Set Syst., Vol. 131, No. 2, pp. 121-152, (2002). 

[24] H. JunRong, N. Sundararajan, G. BinHuang and P. Saratchandran, “Sequential adaptive fuzzy inference system (safis) for nonlinear system identification and prediction”,  Fuzzy Sets Syst., Vol. 157, No. 9, pp. 1260-1275, (2006). 

[25] M. Okwu and O. Adetunji, “comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs”, Int. J. of Eng. Bus. Manage., Vol. 10, No. 12, pp. 1-17, (2018). 

[26]  A. El-shafie, M. Mukhlisin, A. A. Najah and M. R. Taha, "Performance of artificial neural network and regression techniques for rainfall-runoff prediction", Int. J. of Phy. Sci., Vol. 6, No. 8, pp. 1997-2003, (2011). 

[27] P. P. Angelov and D. P. Filev, “An approach to online identification of Takagi-Sugeno fuzzy models”, IEEE Explor. Vol. 34, No. 1, pp. 484–498, (2004). 

[28] P. Angelov and X. Zhou, “On line learning fuzzy rule-based system structure from data streams”, IEEE, Explor. Vol. 44, No. 1524, pp. 915–922, (2008). 

[29] T. R. Kurfess, S. Billington and S. Y. Liang, “Adaptive prognostics for rolling element bearing condition”, Mech. Syst. Signal. Process., Vol. 13, No. 1, pp. 103 – 113, (1999). 

[30] M. Okwu and L. Tartibu, “Sustainable supplier selection in the retail industry: A topsis- and anfis-based evaluating methodology”, Int. J. of Eng Bus. Manage., Vol. 10, No. 12, pp. 1-14, (2020). 

[31] P. P. Angelov and D. P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models", IEEE Trans. Syst., Manuf, Cybern., Part B (Cybernetics), Vol. 34, No. 1, pp. 484-498, (2004). 

[32] S. Sheridan, “Non deterministic processing in neural networks: an introduction to multi-threaded neural networks”, ITB J. Vol. 1, No. 2, pp. 4-20, (2000). 

[33]   L. A. Zadeh, “The role of fuzzy logic in the management of uncertainty in expert systems”, Fuzzy Sets Syst., Vol. 11, No. 1-3, pp. 199-227, (1983).

[34] C. Paleologu, S. Ciochină and A. A. Enescu, “A family of recursive least-square adaptive algorithms suitable for fixed-point implementation”, Int. J.  Adv. Telecom., Vol. 2, No. 2, pp. 88–97, (2009). 

[35] A. B. Abdessalem, R. A., M. Touzet-Cortina, A. Gégout-Petit and M. Puiggali, “Stochastic modelling and prediction of fatigue crack propagation using piecewise-deterministic Markov processes”, Annu. Conf. of Prog. Health Manage. Soc., Vol. 230, No. 4, pp. 1–13, (2009). 

آمار
تعداد مشاهده مقاله: 755
تعداد دریافت فایل اصل مقاله: 617


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب