access icon free Induction machine fault detection using smartphone recorded audible noise

This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

Inspec keywords: frequency measurement; smart phones; probability; velocity measurement; fault diagnosis; computerised instrumentation; acoustic noise measurement; neural nets; rotors; squirrel cage motors

Other keywords: handheld smartphone; neural network; broken rotor bar; dynamic rotor eccentricity; acoustic noise analysis; rotational speed frequency diagnostic indicator; mechanical vibration measurement; three-phase squirrel cage induction machine; fault diagnosis; induction machine fault detection; audible noise recording analysis; vibration sensor; twice-line frequency diagnostic indicator

Subjects: Other topics in statistics; Velocity, acceleration and rotation measurement; Computerised instrumentation; Measurement of acoustic variables; Acoustic variables measurement; Computerised instrumentation; Velocity, acceleration and rotation measurement; Neural computing techniques; Asynchronous machines; Probability theory, stochastic processes, and statistics; Mobile radio systems; Frequency measurement; Time and frequency measurement; Other topics in statistics

References

    1. 1)
      • 27. Sobra, J., Kindl, V., Skala, B.: ‘Determination of the force caused by broken rotor bar and static eccentricity in an induction machine’. Proc. ELEKTRO 2014, Rajecke Teplice, Slovakia, May 2014, pp. 375378.
    2. 2)
      • 20. Rzeszucinski, P., Orman, M., Pinto, C., et al: ‘A signal processing approach to bearing fault detection with the use of a mobile phone’. Proc. IEEE Tenth Int. Symp. Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Guarda, Portugal, September 2015, pp. 310315.
    3. 3)
      • 24. Loutasa, T.H., Rouliasa, D., Paulyb, E., et al: ‘The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery’, Mech. Syst. Signal Process., 2011, 25, (4), pp. 13391352.
    4. 4)
      • 13. Yan, J., Lu, L.: ‘Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis’, Signal Process., 2014, 98, pp. 7487.
    5. 5)
      • 5. Kankar, P.K., Sharma, S.C., Harsha, S.P.: ‘Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform’, Neurocomputing, 2013, 110, pp. 917.
    6. 6)
      • 19. Vaimann, T., Belahcen, A., Kallaste, A.: ‘Necessity for implementation of inverse problem theory in electric machine fault diagnosis’. Proc. IEEE Tenth Int. Symp. Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Guarda, Portugal, September 2015, pp. 380385.
    7. 7)
      • 12. Amarnath, M., Krishna, I.R.P.: ‘Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings’, IET Sci. Meas. Technol., 2012, 6, (4), pp. 279287.
    8. 8)
      • 11. Bin, G.F., Gao, J.J., Li, X.J., et al: ‘Early fault diagnosis of rotating machinery based on wavelet packets – empirical mode decomposition feature extraction and neural network’, Mech. Syst. Signal Process., 2012, 27, pp. 696711.
    9. 9)
      • 10. Zhang, X., Zhou, J.: ‘Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines’, Mech. Syst. Signal Process., 2013, 41, (1–2), pp. 127140.
    10. 10)
      • 7. Rafiee, J., Rafiee, M.A., Tse, P.W.: ‘Application of mother wavelet functions for automatic gear and bearing fault diagnosis’, Expert Syst. Appl., 2010, 37, (6), pp. 45684579.
    11. 11)
      • 15. Osman, S., Wang, W.: ‘An enhanced Hilbert–Huang transform technique for bearing condition monitoring’, IET Meas. Sci. Technol., 2013, 24, (8), pp. 113.
    12. 12)
      • 17. Belahcen, A., Gyftakis, K., Martinez, J., et al: ‘Condition monitoring of electrical machines and its relation to industrial internet’. Proc. IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD), Torino, Italy, March 2015, pp. 233241.
    13. 13)
      • 16. Kang, M., Kim, J., Kim, J.M.: ‘High-performance and energy-efficient fault diagnosis using effective envelope analysis and denoising on a general-purpose graphics processing unit’, IEEE Trans. Power Electron., 2015, 30, (5), pp. 27632776.
    14. 14)
      • 1. Cabal-Yepez, E., Garcia-Ramirez, A.G., Romero-Troncoso, R.J., et al: ‘Reconfigurable monitoring system for time–frequency analysis on industrial equipment through STFT and DWT’, IEEE Trans. Ind. Inf., 2013, 9, (2), pp. 760771.
    15. 15)
      • 14. Cheng, G., Cheng, Y.L., Shen, L.H., et al: ‘Gear fault identification based on Hilbert–Huang transform and SOM neural network’, Measurement, 2013, 46, (3), pp. 11371146.
    16. 16)
      • 9. Zheng, J., Cheng, J., Yang, Y.: ‘Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis’, Mech. Syst. Signal Process., 2013, 40, (1), pp. 136153.
    17. 17)
      • 22. Stief, A., Ottewill, J.R., Orkisz, M., et al: ‘Two stage data fusion of acoustic, electric and vibration signals for diagnosing faults in induction motors’, Elektron. Elektrotech., 2017, 23, (6), pp. 1924.
    18. 18)
      • 18. Belahcen, A., Martinez, J., Vaimann, T.: ‘Comprehensive computations of the response of faulty cage induction machines’. Proc. Int. Conf. Electrical Machines (ICEM), Berlin, Germany, September 2014, pp. 15101515.
    19. 19)
      • 8. Lei, Y., Lin, J., He, Z., et al: ‘A review on empirical mode decomposition in fault diagnosis of rotating machinery’, Mech. Syst. Signal Process., 2013, 35, (1–2), pp. 108126.
    20. 20)
      • 25. Finley, J.W.R., Hodowanec, M.M., Holter, W.G.: ‘An analytical approach to solving motor vibration problems’, IEEE Trans. Ind. Appl., 2000, 36, (5), pp. 14671480.
    21. 21)
      • 3. Yan, R., Gao, R.X., Chen, X.: ‘Wavelets for fault diagnosis of rotary machines: a review with applications’, Signal Process., 2014, 96, pp. 115.
    22. 22)
      • 6. Konar, P., Chattopadhyay, P.: ‘Bearing fault detection of induction motor using wavelet and support vector machines (SVMS)’, Appl. Soft Comput., 2011, 11, (6), pp. 42034211.
    23. 23)
      • 26. Sobra, J., Vaimann, T., Belahcen, A.: ‘Mechanical vibration analysis of induction machine under dynamic rotor eccentricity’. Proc. 17th Int. Scientific Conf. Electric Power Engineering, Prague, Czech Republic, May 2016, pp. 413416.
    24. 24)
      • 21. Orman, M., Rzeszucinski, P., Tkaczyk, A., et al: ‘Bearing fault detection with the use of acoustic signals recorded by a hand-held mobile phone’. Proc. 2015 Int. Conf. Condition Assessment Techniques in Electrical Systems (CATCON), Bangalore, India, December 2015, pp. 15.
    25. 25)
      • 4. Seshadrinath, J., Singh, B., Panigrahi, B.K.: ‘Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets’, IEEE Trans. Power Electron., 2014, 29, (2), pp. 936945.
    26. 26)
      • 23. Khazaee, M., Ahmadi, H., Omid, M., et al: ‘Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory’, Proc. Inst. Mech. Eng. E, J. Process Mech. Eng., 2014, 228, (1), pp. 2132.
    27. 27)
      • 28. Application notes, ‘Vibration diagnostics for industrial electric motor drives’, Brüel & Kjaer Sound & Vibration Measurement A/S, Naerum, Denmark, 1988.
    28. 28)
      • 2. Nandi, S., Ilamparithi, T.C., Lee, S.B., et al: ‘Detection of eccentricity faults in induction machines based on nameplate parameters’, IEEE Trans. Ind. Electron., 2011, 58, (5), pp. 16731683.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2017.0104
Loading

Related content

content/journals/10.1049/iet-smt.2017.0104
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading