Skip to main content
SpringerLink
Log in

Investigation of damaged structure in altered viscous fluid medium using multiple adaptive neurofuzzy inference system (MANFIS)

  • Original Paper
  • Published:
Acta Mechanica Aims and scope Submit manuscript

Abstract

In this research, a multiple adaptive neurofuzzy inference system (MANFIS) for fault diagnosis of rotating multi-damage cantilever rotor partially immersed in the viscous fluid environment was proposed. Theoretical, experimental, and finite element methods are employed to ascertain the dynamic responses of a damaged and undamaged cantilever rotor shaft that is partially submerged in a viscous fluid medium. Influence coefficient methods are used to compute the transverse directional (i.e., x and y-axis) natural frequency and amplitude of the rotating multi- cracked cantilever rotor shaft with attached mass (i.e., disk) at the free end. To assess the impact of fluid forces, the Navier–Stokes equation was used. The overall combined framework of fuzzy logic inference system and neural networks is used in the platform of neurofuzzy model. It is very useful tool to estimate the damage identification of the rotor shaft system. Relative crack size and position are the output of the multiple ANFIS controller. In order to determine the robustness of the suggested multiple adaptive neurofuzzy inference system, the final computed results from the multiple ANFIS have been compared with the findings acquired from the experimental investigation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig.10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Abbreviations

A :

Cross-sectional area of rotor

ANFIS:

Artificial neurofuzzy interference system

D :

Rotor shaft diameter

a :

Depth of crack

ɛ :

Eccentricity (i.e., mass of disk from its center)

E :

Young’s modulus elasticity

C ij :

Element of compliance matrix

h :

Height of rectangular strip

F y :

Forces of fluid at y-axis in rotor

F x :

Forces of fluid at x-axis in rotor

I :

Moment of inertia

K ij :

Local flexibility matrix of crack element

J :

Strain energy release rate

i :

Variable

P i :

Nodal forces on the crack element

K s :

Rotor shaft stiffness

RD :

Radius of disk

L :

Length of shaft

m :

Fluid mass displaced by the shaft per unit length

M D :

Disk mass

M*:

Dimensionless parameter (m/ms)

R 1 :

Radius of rotor shaft

RMSE:

Root mean square error

R 2 :

Radius of viscous fluid container

m s :

Mass of shaft per unit length

T D :

Thickness of disk

q :

Gap ratio (R2−R1/R1)

v :

Tangential flow velocity

u :

Radial flow velocity

β :

Relative depth of crack (a/D)

α :

Relative position of crack (Lcrack/L)

ɛ :

ɛ1/R1

ɛ 1 :

Eccentricity along x-direction

ɛ 2 :

Eccentricity along y-direction

Ω:

Angular velocity of whirling

δ :

Whirling radius

ν :

Kinematics viscosity of fluid

ω n :

Natural frequency

ω :

Rotating speed of rotor

ω yy :

Critical speed at y-direction

ω xx :

Critical speed at x-direction

References

  1. Ye, Z., Sadeghian, A., Wu, B.: Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System. Electr. Power Syst. Res. 76(9), 742–752 (2006)

    Article  Google Scholar 

  2. Kuo, H.C., Chang, H.K.: A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems. Eng. Appl. Artif. Intell. 17(8), 919–930 (2004)

    Article  Google Scholar 

  3. Eslamloueyan, R.: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process. Appl. Soft Comput. 11(1), 1407–1415 (2011)

    Article  Google Scholar 

  4. Zio, E., Gola, G.: A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery. Reliab. Eng. Syst. Saf. 94(1), 78–88 (2009)

    Article  Google Scholar 

  5. Yang, B.S., Oh, M.S., Tan, A.C.C.: Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Syst. Appl. 36(2), 1840–1849 (2009)

    Article  Google Scholar 

  6. Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11(1), 1014–1020 (2011)

    Article  Google Scholar 

  7. Salahshoor, K., Khoshro, M.S., Kordestani, M.: Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems. Simul. Model. Pract. Theory 19(5), 1280–1293 (2011)

    Article  Google Scholar 

  8. Sadeghian, M., Fatehi, A.: Prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique. J. Process Control 21(2), 302–308 (2011)

    Article  CAS  Google Scholar 

  9. Zhu, F., Deng, Z., Zhang, J.: An integrated approach for structural damage identification using wavelet neuro-fuzzy model. Expert Syst. Appl. 40(18), 7415–7427 (2013)

    Article  Google Scholar 

  10. Bachi, I.O., Abdulrazzaq, N., He, Z.: Neuro fuzzy model for predicting the dynamic characteristics of beams. Acta Mech. Solida Sin. 27(1), 85–96 (2014)

    Article  Google Scholar 

  11. Zhang, L., Xiong, G., Liu, H., Zou, H., Guo, W.: Bearing fault diagnosis using multi- scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 37(8), 6077–6085 (2010)

    Article  Google Scholar 

  12. Nikolić, V., Petkovic, D., Yeeb, P.L., Shamshirband, S., Zamani, M., Cojbasic, Z., Motamedi, S.: Potential of of neuro-fuzzy methodology to estimate noise level of wind turbines. Mech. Syst. Signal Process. 66(67), 715–722 (2016)

    Article  ADS  Google Scholar 

  13. Chen, B., Matthews, P.C., Tavner, P.J.: Wind turbine pitch faults prognosis using a- priori knowledge-based ANFIS. Expert Syst. Appl. 40(17), 6863–6876 (2013)

    Article  Google Scholar 

  14. Saee, R.A., Galybin, A.N., Popov, V.: 3D fluid–structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mech. Syst. Signal Process. 34, 259–276 (2013)

    Article  ADS  Google Scholar 

  15. Vassilopoulos, A.P., Bedi, R.: Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates. Comput. Mater. Sci. 43, 1086–1093 (2008)

    Article  Google Scholar 

  16. Pawar, R.S., Sawant, S.H.: An overview of vibration analysis of cracked cantilever beam with non-linear parameters and harmonic excitations. Int. J. Innov. Technol. Explor. Eng. 8(3), 53–55 (2014)

    Google Scholar 

  17. Petkovic, D., Issa, M., Pavlovic, N.D., Zentner, L., Cojbasic, Z.: Adaptive neurofuzzy Controller For adaptive Compliant robotic gripper. Expert Syst. Appl. 0957–417439, 13295–13304 (2012)

    Article  Google Scholar 

  18. Matsubara, M., Rahnejat, H., Gohar, R.: Computational modelling of precision spindles supported by ball bearings. Int. J. Mach. Tools Manuf. 28(4), 429–442 (1988)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, G H Raisoni College of Engineering and Management Pune, Pune, India

    Adik R. Yadao

Authors
  1. Adik R. Yadao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adik R. Yadao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

The membership functions for P, Q, R, S and T considered in ‘layer 1’ are the bell-shaped functions (Fig. 11) and are defined as follows:

$$\mu_{Ph} \left( y \right) = \frac{1}{{1 + \left\{ {\left( {\frac{{y - c_{h} }}{{a_{h} }}} \right)^{2} } \right\}^{{b_{h} }} }};\;h = 1, \ldots ,n_{1}$$
(31)
$$\mu_{Qh} \left( y \right) = \frac{1}{{1 + \left\{ {\left( {\frac{{y - c_{h} }}{{a_{h} }}} \right)^{2} } \right\}^{{b_{h} }} }};h = n_{1} + 1, \ldots ,n_{1} + n_{2}$$
(32)
$$\mu_{Rh} \left( y \right) = \frac{1}{{1 + \left\{ {\left( {\frac{{y - c_{h} }}{{a_{h} }}} \right)^{2} } \right\}^{{b_{h} }} }};\;h = n_{1} + n_{2} + 1, \ldots ,n_{1} + n_{2} + n_{3}$$
(33)
$$\mu_{Sh} \left( y \right) = \frac{1}{{1 + \left\{ {\left( {\frac{{y - c_{h} }}{{a_{h} }}} \right)^{2} } \right\}^{{b_{h} }} }};\;h = n_{1} + n_{2} + n_{3} + 1, \ldots ,n_{1} + n_{2} + n_{3} + n_{4}$$
(34)
$$\mu_{Th} \left( y \right) = \frac{1}{{1 + \left\{ {\left( {\frac{{y - c_{h} }}{{a_{h} }}} \right)^{2} } \right\}^{{b_{h} }} }};\;h = n_{1} + n_{2} + n_{3} + 1, \ldots ,n_{1} + n_{2} + n_{3} + n_{4}$$
(35)

where \(b_{h}\), \(c_{h}\), \(a_{h}\) are the parameters for fuzzy membership function. The parameter has been altered by the bell-shaped function. The alteration of contour of bell-shaped function is required according to the dataset for the considered problem.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadao, A.R. Investigation of damaged structure in altered viscous fluid medium using multiple adaptive neurofuzzy inference system (MANFIS). Acta Mech 235, 543–563 (2024). https://doi.org/10.1007/s00707-023-03751-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00707-023-03751-8

Search

Navigation