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access icon free Acoustic diagnostics of electrical origin fault modes with readily available consumer-grade sensors

© The Institution of Engineering and Technology
Received 20/03/2019, Accepted 27/06/2019, Revised 30/05/2019, Published 02/07/2019

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This study presents low-cost real-time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer-grade sensors for low-cost real-time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the pulse-width modulation controller of a BrushLess DC motor. Experimental trials positively detect 99.9% of the 1160 resultant high-bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components.

Inspec keywords: brushless DC motors; power electronics; fault diagnosis; signal denoising; wavelet transforms; control engineering computing

Other keywords: optimal wavelet selection methods; acoustic diagnostics; system operation; monitoring applications; example electrical origin fault; real-time fault diagnostics; electrical origin fault modes; suitability study; acoustic techniques; audible faults; unreported acute electrical origin fault; acoustic measurements; physical component damage; readily available consumer-grade sensors; mechanical fault modes; wider range; electrical origin faults; empirical mode decomposition processing algorithms; real-time diagnostics

Subjects: Optical, image and video signal processing; Control engineering computing; Control of electric power systems; Computer vision and image processing techniques; d.c. machines; Signal processing and detection; Integral transforms

References

    1. 1)
      • 34. Immovilli, F., Bianchini, C., Lorenzani, E., et al: ‘Evaluation of combined reference frame transformation for interturn fault detection in permanent-magnet multiphase machines’, IEEE Trans. Ind. Electron., 2015, 62, (3), pp. 1912–1920.
    2. 2)
      • 40. Pimentel, M.A.F., Clifton, D.A., Clifton, L., et al: ‘A review of novelty detection’, Signal Process., 2014, 99, pp. 215–249.
    3. 3)
      • 12. Mba, D., Rao, R.B.K.N.: ‘Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines; bearings, pumps, gearboxes, engines and rotating structures’, Shock Vib. Digest, 2006, 38, (1), pp. 3–16.
    4. 4)
      • 19. Siddique, A., Yadava, G.S., Singh, B.: ‘A review of stator fault monitoring techniques of induction motors’, IEEE Trans. Energy Convers., 2005, 20, (1), pp. 106–114.
    5. 5)
      • 22. Hassan, O.E., Amer, M., Abdelsalam, A.K., et al: ‘Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review’, IET Electr. Power Appl., 2018, 12, pp. 895–907.
    6. 6)
      • 4. Li, H., Xu, F., Liu, H., et al: ‘Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram’, Measurement, 2015, 65, pp. 1–10.
    7. 7)
      • 16. Van Hecke, B., Yoon, J., He, D.: ‘Low speed bearing fault diagnosis using acoustic emission sensors’, Appl. Acoust., 2016, 105, pp. 35–44.
    8. 8)
      • 37. Cekic, Y., Eren, L.: ‘Broken rotor bar detection via four-band wavelet packet decomposition of motor current’, Electr. Eng., 2018, 100, pp. 1957–1962.
    9. 9)
      • 35. Arellano-Padilla, J., Sumner, M., Gerada, C.: ‘Winding condition monitoring scheme for a permanent magnet machine using high-frequency injection’, IET Electr. Power Appl., 2011, 5, (1), pp. 89–99.
    10. 10)
      • 10. Kankar, P.K., Sharma, S.C., Harsha, S.P.: ‘Rolling element bearing fault diagnosis using wavelet transform’, Neurocomputing, 2011, 74, (10), pp. 1638–1645.
    11. 11)
      • 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. 4203–4211.
    12. 12)
      • 45. Delgado-Arredondo, P.A., Garcia-Perez, A., Morinigo- Sotelo, D., et al: ‘Comparative study of time–frequency decomposition techniques for fault detection in induction motors using vibration analysis during startup transient’, Shock Vib., 2015, 2015, pp. 1–14.
    13. 13)
      • 26. Glowacz, A., Glowacz, W., Glowacz, Z., et al: ‘Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals’, Measurement, 2018, 113, pp. 1–9.
    14. 14)
      • 42. 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. 696–711.
    15. 15)
      • 49. Yan, R.: ‘Wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis’, University of Massachusetts, 2007.
    16. 16)
      • 39. Farrar, K.: ‘Soundfield microphone – design and development of microphone and control unit’, Wirel. World, 1979, pp. 48–50.
    17. 17)
      • 38. Grebenik, J., Bingham, C., Srivastava, S.: ‘Continuous acoustic monitoring of electrical machines; processing signals from usb microphone & mobile smartphone sensors detecting dc motor controller fault’. Proc. of the 5th Int. Conf. on Control, Decision & Information Technologies (CoDIT), IEEE, Thessaloniki, Greece, 2018.
    18. 18)
      • 36. Sen, B., Wang, J.: ‘Stator interturn fault detection in permanent-magnet machines using pwm ripple current measurement’, IEEE Trans. Ind. Electron., 2016, 63, (5), pp. 3148–3157.
    19. 19)
      • 14. Al-Ghamd, A.M., Mba, D.: ‘A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size’, Mech. Syst. Signal Process., 2006, 20, (7), pp. 1537–1571.
    20. 20)
      • 21. Liu, Y., Bazzi, A.M.: ‘A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors - state of the art’, ISA Trans., 2017, 70, pp. 400–409.
    21. 21)
      • 30. Contreras-Hernandez, J.L., Almanza-Ojeda, D.L., Ledesma-Orozco, S., et al: ‘Quaternion signal analysis algorithm for induction motor fault detection’, IEEE Trans. Ind. Electron., 2019, 66, pp. 8843–8850.
    22. 22)
      • 43. 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, pp. 108–126.
    23. 23)
      • 44. Da Costa C., , Kashiwagi, M.: ‘Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition’, Case Stud. Mech. Syst. Signal Process., 2015, 1, pp. 15–26.
    24. 24)
      • 46. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis’. The Royal Society A - Mathematical, Physical and Engineering Sciences, The Royal Society, 1998, vol. 454, pp. 903–995.
    25. 25)
      • 3. Hemmati, F., Orfali, W., Gadala, M. S.: ‘Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation’, Appl. Acoust., 2016, 104, pp. 101–118.
    26. 26)
      • 47. Wu, Z., Huang, N.E.: ‘Ensemble empirical mode decomposition - a noise assisted data analysis method’, Adv. Adapt. Data. Anal., 2009, 1, (1), pp. 1–41.
    27. 27)
      • 28. Li, W., Mechefske, C.K.: ‘Detection of induction motor faults - a comparison of stator current, vibration and acoustic methods’, J. Vib. Control, 2006, 12, (2), pp. 165–188.
    28. 28)
      • 18. Grebenik, J., Zhang, Y., Bingham, C., et al: ‘Roller element bearing acoustic fault detection using smartphone and consumer microphones - comparing with vibration techniques’. Proc. of the 17th Int. Conf. on Mechatronics - Mechatronika (ME), Prague, Czech Republic, 2016.
    29. 29)
      • 27. Glowacz, A.: ‘Fault diagnosis of single-phase induction motor based on acoustic signals’, Mech. Syst. Signal Process., 2019, 117, pp. 65–80.
    30. 30)
      • 32. Park, J.-K., Hur, J.: ‘Detection of inter-turn and dynamic eccentricity faults using stator current frequency pattern in ipm-type bldc motors’, IEEE Trans. Ind. Electron., 2016, 63, (3), pp. 1771–1780.
    31. 31)
      • 15. Mo, Z., Wang, J., Zhang, H., et al: ‘Vibration and acoustics emission based methods in low-speed bearing condition monitoring’. Proc. of the Prognostics and System Health Management Conf., Chongqing, People's Republic of China, 2018.
    32. 32)
      • 20. Nandi, S., Toliyat, H.A., Li, X.: ‘Condition monitoring and fault diagnosis of electrical motors - a review’, IEEE Trans. Energy Convers., 2005, 20, (4), pp. 719–729.
    33. 33)
      • 8. Feng, K., Jiang, Z., He, W., et al: ‘Rolling element bearing fault detection based on optimal antisymmetric real laplace wavelet’, Measurement, 2011, 44, (9), pp. 1582–1591.
    34. 34)
      • 24. Tian, Y., Guo, D., Zhang, K., et al: ‘A review of fault diagnosis for traction induction motor’. Proc. of the 37th Chinese Control Conf. (CCC), Wuhan, People's Republic of China, 2018, pp. 5763–5768.
    35. 35)
      • 11. Al-Balushi, K.R., Addali, A., Charnley, B., et al: ‘Energy index technique for detection of acoustic emissions associated with incipient bearing failures’, Appl. Acoust., 2010, 71, (9), pp. 812–821.
    36. 36)
      • 23. Pons-Llinares, J., Climente-Alarcón, V., Puche-Panadero, R., et al: ‘Bar breakage detection on squirrel cage induction motors via transient motor current signal analysis based on the wavelet transform, a review’, Departamento de Ingeniería Eléctrica, Universidad Politécnica de Valencia, Spain, 2017.
    37. 37)
      • 41. Rai, A., Upadhyay, S.H.: ‘A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings’, Tribol. Int., 2016, 96, pp. 289–306.
    38. 38)
      • 17. Verma, N.K., Singh, J.V., Gupta, M., et al: ‘Windows mobile and tablet app for acoustic signature based machine health monitoring’. Proc. of the 9th Int. Conf. on Industrial and Information Systems (ICIIS), Gwalior, India, 2014, pp. 1–6.
    39. 39)
      • 7. Nikolaou, N.G., Antoniadis, I.A.: ‘Rolling element bearing fault diagnosis using wavelet packets’, NDT&E Int., 2002, 35, (3), pp. 197–205.
    40. 40)
      • 9. He, W., Jiang, Z.-N., Feng, K.: ‘Bearing fault detection based on optimal wavelet filter and sparse code shrinkage’, Measurement, 2009, 42, (7), pp. 1092–1102.
    41. 41)
      • 31. Ordaz-Moreno, A., Romero-Troncoso, R.D. J., Vite-Frias, J.A., et al: ‘Automatic online diagnosis algorithm for brokenbar detection on induction motors based on discrete wavelet transform for fpga implementation’, IEEE Trans. Ind. Electron., 2008, 55, (5), pp. 2193–2202.
    42. 42)
      • 33. Riera-Guasp, M., Antonino-Daviu, J.A., Capolino, G.-A.: ‘Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art’, IEEE Trans. Ind. Electron., 2015, 62, (3), pp. 1746–1759.
    43. 43)
      • 29. Sangeetha, B.P., Hemamalini, S.: ‘Rational-dilation wavelet transform based torque estimation from acoustic signals for fault diagnosis in a three phase induction motor’, IEEE Trans. Ind. Inf., 2018, 15, pp. 3492–3501.
    44. 44)
      • 5. 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., 2011, 6, (4), pp. 279–287.
    45. 45)
      • 25. Glowacz, A.: ‘Diagnostics of synchronous motor based on analysis of acoustic signals with the use of line spectral frequencies and k-nearest neighbor classifier’, Arch. Acoust., 2014, 39, (2), pp. 189–194.
    46. 46)
      • 1. Loutas, T.H., Sotiriades, G., Kalaitzoglou, I., et al: ‘Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements’, Appl. Acoust., 2009, 70, (9), pp. 1148–1159.
    47. 47)
      • 48. Kulkarni, P.G., Sahasrabudhe, A.D.: ‘Investigations on mother wavelet selection for health assessment of lathe bearings’, Int. J. Adv. Manuf. Technol., 2017, 90, pp. 9–12.
    48. 48)
      • 2. Eftekharnejad, B., Carrasco, M.R., Charnley, B., et al: ‘The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing’, Mech. Syst. Signal Process., 2011, 25, (1), pp. 266–284.
    49. 49)
      • 13. Holroyd, T.J.: ‘The application of ae in condition monitoring’, Insight - Non-Destr. Test. Cond. Monit., 2005, 47, (8), pp. 481–485.
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