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ISSN: 2475-4811 (print)
ISSN: 2475-482X (online)
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn

A Low Cost Device for Excessive Vibration Detection in Electric Motors

Journal of Vcibration Testing and System Dynamics 2(3) (2018) 239--247 | DOI:10.5890/JVTSD.2018.09.004

M. Sundin$^{1}$, A. Babaei$^{2}$, S. Paudyal$^{2}$, C. Yang$^{2}$, N. Kaabouch$^{1}$

$^{1}$ Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202-7165, USA

$^{2}$ Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202-8359, USA

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Abstract

Health monitoring and fault diagnosis are essential to ensuring reliable operation of machinery in industry. In this paper, we present the design of a low cost device for sensing mechanical vibrations and detecting excessive vibration. Advantages of the device nclude cost effectiveness and simplicity of the design. Piezoelectric-based sensor, light emitting diodes, resistors, and liquid crystal display, and an Arduino board are the main components of the device. The effectiveness of designed device was tested on simple lectric motors, such as fan and drill motors. This vibration measurement device can be used to monitor machinery health by detecting unwanted oscillations and subsequent potential hazards.

References

  1. [1]  Niu, G., Yang, B.S., and Pecht, M. (2010), Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance, Reliability Engineering & System Safety, 95(7), 786-796.
  2. [2]  TungTran, V. and Yang, B. (2012), An intelligent condition-based maintenance platform for rotating machinery, Journal Expert Systems with Applications, 39(3), 2977-C298.
  3. [3]  Attoui, I., Boutasseta, N., Fergani, N., Oudjani, B., and Deliou, A. (2015), Vibration-based bearing fault diagnosis by an integrated DWT-FFT approach and an adaptive neuro-fuzzy inference system, 3rd International Conference on Control, Engineering & Information Technology (CEIT).
  4. [4]  Seryasat, O.R., Honarvar, F., and Rahmani, A. (2010), Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS), In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, 4295-4299.
  5. [5]  Nejadpak, A. and Yang, C. (2016), A vibration-based diagnostic tool for analysis of superimposed failures in electric machines, Proceedings of IEEE International Conference on Electro Information Technology (EIT), Grand Forks, ND, 2016, 0324-0329, DOI: 10.1109/EIT.2016.7535260.
  6. [6]  Plante, T., Nejadpak, A., and Yang, C. (2015), Faults detection and failures prediction using vibration analysis, Proceedings of IEEE Autotestcon, 2015, National Harbor, MD, 227-231, DOI: 10.1109/AUTEST.2015.7356493.
  7. [7]  Broch, J.T. (1980), Mechanical vibration and shock measurements, Brüel & Kjær.
  8. [8]  Tsypkin, M. (2013), Induction motor condition monitoring: Vibration analysis technique-a twice line frequency component as a diagnostic tool, In Electric Machines & Drives Conference (IEMDC), IEEE International 2013 May 12, 117-124.
  9. [9]  Patil, S. and Gaikwad, J. (2013), Vibration analysis of electrical rotating machines using FFT: a method of predictive maintenance, In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, India, 4-6 July 2013, pp.1-6, New York: IEEE.
  10. [10]  Atoui, I., Meradi, H., and Boulkroune, R. (2013), Fault detection and diagnosis in rotating machinery by vibration monitoring using FFT and Wavelet techniques, In: 2013 8th international workshop on systems, signal processing and their applications (WoSSPA), Algiers, Algeria, 12-15 May 2013, 401-406, New York: IEEE.
  11. [11]  Bajric, R., Zuber, N., Skrimpas, G., and Mijatovic, N. (2016), Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox, Shock and Vibration, 2016, Article ID 6748469, 10 pages, 2016, DOI:10.1155/2016/6748469.
  12. [12]  Adhikari, S., Sinha, N., and Dorendrajit, T. (2016), Fuzzy logic based on-line fault detection and classification in transmission line, SpringerPlus, 5(1), 1002, DOI:10.1186/s40064-016-2669-4.
  13. [13]  Klomjit, J. and Ngaopitakkul, A. (2016), Selection of proper input pattern in fuzzy logic algorithm for classifying the fault type in underground distribution system, Region 10 Conference (TENCON) 2016 IEEE, 2650-2655, ISSN: 2159-3450.
  14. [14]  Plante, T., Stanley, L., Nejadpak, A., and Yang, C. (2016), Vibration Based Fault Detection using Principal Component Analysis, Proceedings of IEEE Autotestcon 2016, Anaheim, CA, USA, Sept. 12-15, 2016.
  15. [15]  Sinha, J. and Elbhbah, K. (2013), A future possibility of vibration based condition monitoring of rotating machines, Mechanical Systems and Signal Processing, 34(1), 231-40.
  16. [16]  Nejadpak, A. and Yang, C. (2017), Misalignment and unbalance faults detection and identification using KNN analysis, Proceedings of CANCAM 2017, Victoria, BC, May 28-June 1, 2017, pp. 0324-0329, DOI: 10.1109/EIT.2016.7535260.
  17. [17]  He, Q. and Wang, J. (2007), Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes, IEEE Transactions on Semiconductor Manufacturing, 20(4), 345-354, Nov. 2007, DOI: 10.1109/TSM.2007.907607.
  18. [18]  Johnson, J. and Yadav, A. (2016), Fault detection and classification technique for HVDC transmission lines using KNN, International Conference on ICT for Sustainable Development ICT4SD.
  19. [19]  Pandya, D., Upadhyay, S., and Harsha, S. (2013), Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN, Expert Systems with Applications, 40(10), 4137- 4145.
  20. [20]  Li, C., René-Vinicio, S., Grover, Z., Mariela, C., and Diego, C. (2016), Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning, Sensors, 16(6), 895.
  21. [21]  Elbhbah, K. and Sinha, J. (2013), Vibration-based condition monitoring of rotating machines using a machine composite spectrum, Journal of Sound and Vibration, 332(11), 2831-2845,
  22. [22]  Fundamentals-of-piezoelectric-shock-and-vibration-sensors, (2011).
  23. [23]  Nejadpak, A. and Yang, C. (2017), Fast Unbalancing of Rotating Machines by Combination of Computer Vision and Vibration Data Analysis, Journal of Vibration Testing and System Dynamics, 1(4), 343-352, DOI: 10.5890/JVTSD.2017.12.005.
  24. [24]  https://www.parallax.com/sites/default/files/downloads/605-00004-Piezo-Film-Datasheet.pdf.

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