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Fault Detection of a Rotating Shaft by Using the Electromechanical Impedance Method and a Temperature Compensation Approach

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Abstract

Condition-based maintenance systems that use sensor networks for damage detection in rotating machinery have been evolving constantly. Such strategies aim at detecting the presence and severity of damage on a statistical basis. The aim of this contribution relies on the correct detection of incipient faults in rotating shafts by using a real-time impedance-based structural health monitoring method, with a low-cost portable device. This technique monitors changes in the electric impedance of piezoelectric transducers, acting simultaneously as actuators and sensors, which are bonded to the host structure. With the use of damage metrics, these changes can be quantified so that the presence and severity of damage are detected. This is possible since the electrical impedance of the sensor is directly related to the mechanical impedance of the structure. However, the frequency response functions resulting from this method are susceptible to environmental and operational conditions that should be accounted for to avoid false diagnostics. Consequently, a temperature compensation technique is proposed based on a hybrid optimization method associated with different damage metrics. Additionally, a statistical model is used for threshold determination based on the statistical process control method. Experimental results show that an incipient fatigue crack associated with bearing wear was successfully detected with a probability of detection above 95% confidence for the majority of sensors used.

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References

  1. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, West Sussex (2013)

    Google Scholar 

  2. Saavedra, P.N., Cuitiño, L.A.: Vibration analysis of rotor crack identification. J. Vib. Control 8, 51–67 (2002). doi:10.1177/107754602023526

    Article  MATH  Google Scholar 

  3. Doebling, S.W., Farrar, C.R., Prime, M.R.: A summary review of vibration-based damage identification methods. Shock Vib. Digest 30(2), 91–105 (1998)

  4. Darpe, A.K., Gupta, K., Chawla, A.: Coupled bending, longitudinal and torsional vibrations of a cracked rotor. J. Sound. Vib. 269(1), 33–60 (2004). doi:10.1016/S0022-460X(03)00003-8

    Article  Google Scholar 

  5. Cavalini Jr., A.A., Finzi Neto, R.M., Steffen Jr., V.: Impedance-based fault detection methodology for rotating machines. Struct. Health Monit. 14(3), 228–240 (2014). doi:10.1177/1475921714564639

    Article  Google Scholar 

  6. Cavalini Jr., A.A., Oliveira, D.D., Rabelo, D.S., Finzi Neto, R.M., Steffen Jr., V.: Fault detection in a rotating shaft by using the electromechanical impedance method and a temperature compensation approach. In: Proceedings of the XXXVI Ibero-Latin American Congress on Computational Methods in Engineering, 22–25 November 2015. Rio de Janeiro (2015)

  7. Baydar, N., Ball, A.: Detection of gear failure via vibration and acoustic signals using wavelet transform. Mech. Syst. Signal Process. 17, 787–804 (2003). doi:10.1006/mssp.2001.1435

    Article  Google Scholar 

  8. Bently, D.E., Hatch, C.T.: Fundamentals of Rotating Machinery Diagnostics (Design and Manufacturing), 1st edn. ASME Press, New York (2003)

    Google Scholar 

  9. Bachsmid, N., Pennacchi, P., Tanzi, E.: Cracked Rotors: A Survey on Static and Dynamic Behavior Including Modeling and Diagnosis. Springer, Heidelberg (2010)

    Book  Google Scholar 

  10. Rabelo, D.S., Steffen Jr., V., Finzi Neto, R.M., Lacerda, H.B.: Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024–T3 aluminum panels under varying temperature. Struct. Health Monit. 1(1), 1–17 (2016). doi:10.1177/1475921716671038

    Google Scholar 

  11. Randall, R.B.: Vibration-Based Condition Monitoring—Industrial, Aerospace and Automotive Applications. Wiley, Chichester (2011)

    Book  Google Scholar 

  12. Ma, J., Li, C.J.: Detection of localized defects in rolling element bearings via composite hypothesis test. In: Proceedings of the Symposium on Mechatronics\(,\) American Society of Mechanical Engineers (1993)

  13. Tang, H., Chand, J., Wang, Y.: The principle of cepstrum and its application in quantitative fault diagnostics of gears. ASME Des. Tech. Conf. 38, 141–144 (1991)

    Google Scholar 

  14. Kemerait, R,C,: A new cepstral approach for prognostic maintenance of cyclic machinery. In: Proceedings of IEEE Southeastcon, pp: 256–262 (1987)

  15. Lin, C.C., Wang, H.P.: Classification of autoregressive spectral estimated signal patterns using an Adaptative resonance theory neural network. Comput. Ind. 22, 143–157 (1993). doi:10.1016/0166-3615(93)90061-5

    Article  Google Scholar 

  16. Widodo, A., Yang, B.S.: Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21(6), 2560–2574 (2007). doi:10.1109/IEEM.2010.5674594

    Article  Google Scholar 

  17. Palomino, L.V., Steffen Jr., V., Finzi Neto, R.M.: Probabilistic neural network and fuzzy cluster analysis methods applied to impedance-based shm for damage classification. Shock Vib. 2014, 1–12 (2014)

    Article  Google Scholar 

  18. Singer, R.M., Gross, K.C., Walsh, M., Humenik, K.E.: Reactor coolant pump monitoring and diagnostic system. In: Proceedings of the 2nd International Machinery Monitoring and Diagnostic Conference, 22–25 October 1990. Los Angeles (1990)

  19. Rabelo, D.S., Finzi Neto, R.M., Steffen Jr., V.: Impedance-based structural health monitoring incorporating compensation of temperature variation effects. In: Proceedings of the 23rd ABCM International Congress of Mechanical Engineering, 6–11 December 2015, Rio de Janeiro (2015)

  20. Sun, F.P., Chaudhry, Z., Liang, C., Rogers, C.A.: Truss structure integrity identification using PZT sensor-actuator. J. Intell. Mater. Syst. Struct. 6(1), 134–139 (1995). doi:10.1177/1045389X9500600117

    Article  Google Scholar 

  21. Park, G., Kabeya, K., Cudney, H.H., Inman, D.J.: Impedance-based structural health monitoring for temperature varying applications. JSME Int. J. 42(2), 249–258 (1999). doi:10.1299/jsmea.42.249

    Article  Google Scholar 

  22. Koo, K.Y., Park, S., Lee, J.J., Yun, C.B.: Automated impedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects. J. Intell. Mater. Syst. Struct. 20(4), 367–377 (2009). doi:10.1177/1045389X08088664

    Article  Google Scholar 

  23. Rabelo, D.S., Guimarães, C.G., Cavalini Jr., A.A., Steffen Jr., V.: A comparative study of temperature compensation techniques for impedance-based structural health monitoring systems. In: Proceedings of the 1st Workshop on Industrial Mathematics, Modeling and Optimization, 16–18 November 2015. Catalão (2015)

  24. Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Int. Comput. Sci. Inst. 12(1), 1–16 (1995). doi:10.1023/A:1008202821328

    Google Scholar 

  25. Vanderplaats, G.N.: Multidiscipline Design Optimization. Vanderplaats Research & Development Inc, Colorado (2007)

    Google Scholar 

  26. Rocha, L.A.A., Rabelo, D.S., Steffen Jr., V.: Identification of damage in structures with rivets using impedance techniques and controls of lamb waves. In: Proceedings of the 25th International Congress of Mechanical Engineering, 10–15 August 2014, no. 0503, Uberlândia (2014)

  27. Palomino, L.V., Steffen Jr., V.: Damage metrics associated with electromechanical impedance technique for SHM applied to a riveted structure. In: Proceedings of the 20th International Congress of Mechanical Engineering, 15–20 November 2009, Gramado-RS (2009)

  28. Baptista, F.G., Budoya, D.E., Almeida, V.A.D., Ulson, J.A.C.: An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring. Sensors 14(1), 1208–1227 (2014). doi:10.3390/s140101208

    Article  Google Scholar 

  29. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution—A Practical Approach to Global Optimization. Springer, Heidelberg (2005)

  30. Bendat, J.S., Piersol, A.G.: Random Data—Analysis and Measurement Procedures, 4th edn. Wiley, New York (2000)

    MATH  Google Scholar 

  31. Finzi Neto, R.M., Steffen Jr., V., Rade, D.A., Gallo, C.A., Palomino, L.V.: A low-cost electromechanical impedance-based SHM architecture for multiplexed piezoceramic actuators. Struct. Health Monit. 10(4), 393–401 (2011). doi:10.1177/1475921710379518

    Article  Google Scholar 

  32. Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, New York (2012)

    MATH  Google Scholar 

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Acknowledgements

The authors are thankful to CNPq (574001/2008-5), FAPEMIG (TEC-APQ-02284-15), and CAPES, Brazilian research agencies, through the National Institute of Science and Technology for Smart Structures in Engineering (INCT-EIE). The first author is thankful to FAPEMIG for his Ph.D. scholarship (11302).

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Correspondence to A. A. Cavalini Jr.

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Rabelo, D.S., Tsuruta, K.M., de Oliveira, D.D. et al. Fault Detection of a Rotating Shaft by Using the Electromechanical Impedance Method and a Temperature Compensation Approach. J Nondestruct Eval 36, 25 (2017). https://doi.org/10.1007/s10921-017-0405-9

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  • DOI: https://doi.org/10.1007/s10921-017-0405-9

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