Accelerated Bearing Life-time Test Rig Development for Low Speed Data Acquisition

“Accelerated Bearing Life-time Test Rig Development for Low Speed Data Acquisition”

Authors: Andreas Klausen, Roy Werner Folgerø, Kjell G. Robbersmyr and Hamid Reza Karimi,
Affiliation: University of Agder
Reference: 2017, Vol 38, No 3, pp. 143-156.

Keywords: Condition Monitoring, Bearing, Low-speed Machinery, Fault Diagnosis, Test Rig

Abstract: Condition monitoring plays an important role in rotating machinery to ensure reliability of the equipment, and to detect fault conditions at an early stage. Although health monitoring methodologies have been thoroughly developed for rotating machinery, low-speed conditions often pose a challenge due to the low signal-to-noise ratio. To this aim, sophisticated algorithms that reduce noise and highlight the bearing faults are necessary to accurately diagnose machines undergoing this condition. In the development phase, sensor data from a healthy and damaged bearing rotating at low-speed is required to verify the performance of such algorithms. A test rig for performing accelerated life-time testing of small rolling element bearings is designed to collect necessary sensor data. Heavy loads at high-speed conditions are applied to the test bearing to wear it out fast. Sensor data is collected in intervals during the test to capture the degeneration features. The main objective of this paper is to provide a detailed overview for the development and analysis of this test rig. A case study with experimental vibration data is also presented to illustrate the efficacy of the developed test rig.

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PDF (2228 Kb) DOI: 10.4173/mic.2017.3.4

DOI forward links to this article:

[1] Andreas Klausen, Huynh V. Khang and Kjell G. Robbersmyr (2019), doi:10.1016/j.ymssp.2019.106422

[2] Arild Bergesen Husebo, Andreas Klausen, Khang Huynh and Kjell G. Robbersmyr (2021), doi:10.23919/ICEMS52562.2021.9634452

[3] Andreas Klausen, Huynh Van Khang and Kjell G. Robbersmyr (2018), doi:10.1109/ICELMACH.2018.8507056

[4] Andreas Klausen, Hyunh van Khang and Kjell G. Robbersmyr (2022), doi:10.4173/mic.2022.1.3

[5] Neeraj Kumar and RK Satapathy (2023), doi:10.1007/s11668-023-01658-z

[6] Andrea Bondin, Joseph Paul Zammit, Y. Qin, X. Zhou, S. Yang, J. Zhao, B. Chen and M. Al-Ahmad (2024), doi:10.1051/matecconf/202440113001

References:
[1] Ahmadzadeh, F. and Lundberg, J. (2014). Ahmadzadeh, F, and Lundberg, J. Remaining useful life estimation: review. Int. J. Syst. Assur. Eng. Mang.. 5(4):461--474. doi:10.1007/s13198-013-0195-0
[2] Ali, J.B., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. (2015). Ali, J, B., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust. 89:16--27. doi:10.1016/j.apacoust.2014.08.016
[3] Antoni, J. (2007). Antoni, J, Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Sig. Process.. 21(1):108--124. doi:10.1016/j.ymssp.2005.12.002
[4] Antoni, J. and Randall, R. (2004). Antoni, J, and Randall, R. Unsupervised noise cancellation for vibration signals: part I-evaluation of adaptive algorithms. Mech. Syst. Sig. Process.. 18(1):89--101. doi:10.1016/S0888-3270(03)00012-8
[5] Caesarendra, W., Kosasih, P.B., Tieu, A.K., Moodie, C. A.S., and Choi, B.-K. (2013). Caesarendra, W, , Kosasih, P.B., Tieu, A.K., Moodie, C. A.S., and Choi, B.-K. Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition. J. Mech. Sci. Technol.. 27(8):2253--2262. doi:10.1007/s12206-013-0608-7
[6] Danielsen, H.K. etal. (2017). Danielsen, H, K. etal. Multiscale characterization of White Etching Cracks (WEC) in a 100Cr6 bearing from a thrust bearing test rig. Wear. 370:73--82. doi:10.1016/j.wear.2016.11.016
[7] Elforjani, M. and Mba, D. (2010). Elforjani, M, and Mba, D. Accelerated natural fault diagnosis in slow speed bearings with acoustic emission. Eng. Fract. Mech.. 77(1):112--127. doi:10.1016/j.engfracmech.2009.09.016
[8] Fan, B.-Q., Lee, K.-M., Ouyang, X.-P., and Yang, H.-Y. (2015). Fan, B, -Q., Lee, K.-M., Ouyang, X.-P., and Yang, H.-Y. Soft-switchable dual-pi controlled axial loading system for high-speed emu axle-box bearing test rig. IEEE Trans. Ind. Electron.. 62(12):7370--7381. doi:10.1109/TIE.2015.2458303
[9] Heng, A., Zhang, S., Tan, A.C., and Mathew, J. (2009). Heng, A, , Zhang, S., Tan, A.C., and Mathew, J. Rotating machinery prognostics: State of the art, challenges and opportunities. Mech. Syst. Sig. Process.. 23(3):724--739. doi:10.1016/j.ymssp.2008.06.009
[10] Ho, D. and Randall, R. (2000). Ho, D, and Randall, R. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical systems and signal processing. 14(5):763--788. .
[11] Jamaludin, N., Mba, D., and Bannister, R. (2001). Jamaludin, N, , Mba, D., and Bannister, R. Condition monitoring of slow-speed rolling element bearings using stress waves. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng.. 215(4):245--271. doi:10.1177/095440890121500401
[12] Junsheng, C., Dejie, Y., and Yu, Y. (2006). Junsheng, C, , Dejie, Y., and Yu, Y. A fault diagnosis approach for roller bearings based on emd method and ar model. Mech. Syst. Sig. Process.. 20(2):350--362. doi:10.1016/j.ymssp.2004.11.002
[13] Kandukuri, S.T., Klausen, A., Karimi, H.R., and Robbersmyr, K.G. (2016). Kandukuri, S, T., Klausen, A., Karimi, H.R., and Robbersmyr, K.G. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable Sustainable Energy Rev.. 53:697--708. doi:10.1016/j.rser.2015.08.061
[14] Kumar, R. and Singh, M. (2013). Kumar, R, and Singh, M. Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement. 46(1):537--545. doi:10.1016/j.measurement.2012.08.012
[15] Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., and Siegel, D. (2014). Lee, J, , Wu, F., Zhao, W., Ghaffari, M., Liao, L., and Siegel, D. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mech. Syst. Sig. Process.. 42(1):314--334. doi:10.1016/j.ymssp.2013.06.004
[16] Li, R., Sopon, P., and He, D. (2012). Li, R, , Sopon, P., and He, D. Fault features extraction for bearing prognostics. J Intell Manuf. 23(2):313--321. doi:10.1007/s10845-009-0353-z
[17] Lin, T.R., Kim, E., and Tan, A.C. (2013). Lin, T, R., Kim, E., and Tan, A.C. A practical signal processing approach for condition monitoring of low speed machinery using peak-hold-down-sample algorithm. Mech. Syst. Sig. Process.. 36(2):256--270. doi:10.1016/j.ymssp.2012.11.003
[18] Muruganatham, B., Sanjith, M., Krishnakumar, B., and Murty, S.S. (2013). Muruganatham, B, , Sanjith, M., Krishnakumar, B., and Murty, S.S. Roller element bearing fault diagnosis using singular spectrum analysis. Mech. Syst. Sig. Process.. 35(1):150--166. doi:10.1016/j.ymssp.2012.08.019
[19] Niknam, S.A., Songmene, V., and Au, Y.J. (2013). Niknam, S, A., Songmene, V., and Au, Y.J. The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines. Int. J. Adv. Manuf. Technol.. 69(9-12):2679--2689. doi:10.1007/s00170-013-5222-4
[20] Peled, R., Braun, S., and Zacksenhouse, M. (2005). Peled, R, , Braun, S., and Zacksenhouse, M. A blind deconvolution separation of multiple sources, with application to bearing diagnostics. Mech. Syst. Sig. Process.. 19(6):1181--1195. doi:10.1016/j.ymssp.2005.08.019
[21] Peng, Y., Dong, M., and Zuo, M.J. (2010). Peng, Y, , Dong, M., and Zuo, M.J. Current status of machine prognostics in condition-based maintenance: a review. Int. J. Adv. Manuf. Technol.. 50(1):297--313. doi:10.1007/s00170-009-2482-0
[22] Qiu, H., Lee, J., Lin, J., and Yu, G. (2006). Qiu, H, , Lee, J., Lin, J., and Yu, G. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound Vibration. 289(4):1066--1090. doi:10.1016/j.jsv.2005.03.007
[23] Randall, R.B. and Antoni, J. (2011). Randall, R, B. and Antoni, J. Rolling element bearing diagnostics—a tutorial. Mech. Syst. Sig. Process.. 25(2):485--520. doi:10.1016/j.ymssp.2010.07.017
[24] Sawalhi, N. and Randall, R. (2008). Sawalhi, N, and Randall, R. Simulating gear and bearing interactions in the presence of faults: Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults. Mech. Syst. Sig. Process.. 22(8):1924--1951. doi:10.1016/j.ymssp.2007.12.001
[25] Sawalhi, N. and Randall, R. (2011). Sawalhi, N, and Randall, R. Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size. Mech. Syst. Sig. Process.. 25(3):846--870. doi:10.1016/j.ymssp.2010.09.009
[26] Shakya, P., Darpe, A.K., and Kulkarni, M.S. (2016). Shakya, P, , Darpe, A.K., and Kulkarni, M.S. Bearing diagnosis using proximity probe and accelerometer. Measurement. 80:190--200. doi:10.1016/j.measurement.2015.11.029
[27] Siegel, D., Al-Atat, H., Shauche, V., Liao, L., Snyder, J., and Lee, J. (2012). Siegel, D, , Al-Atat, H., Shauche, V., Liao, L., Snyder, J., and Lee, J. Novel method for rolling element bearing health assessment—a tachometer-less synchronously averaged envelope feature extraction technique. Mech. Syst. Sig. Process.. 29:362--376. doi:10.1016/j.ymssp.2012.01.003
[28] SKF. (0). SKF, Bearing lifetime calculator. (Link accessible October 2017). http://webtools3.skf.com/BearingCalc/, .
[29] Yu, J. (2011). Yu, J, Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mech. Syst. Sig. Process.. 25(7):2573--2588. doi:10.1016/j.ymssp.2011.02.006
[30] Zarei, J., Tajeddini, M.A., and Karimi, H.R. (2014). Zarei, J, , Tajeddini, M.A., and Karimi, H.R. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics. 24(2):151--157. doi:10.1016/j.mechatronics.2014.01.003
[31] Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M.E., and Vachtsevanos, G. (2011). Zhang, B, , Sconyers, C., Byington, C., Patrick, R., Orchard, M.E., and Vachtsevanos, G. A probabilistic fault detection approach: Application to bearing fault detection. IEEE Trans. Ind. Electron.. 58(5). doi:10.1109/TIE.2010.2058072

BibTeX:
@article{MIC-2017-3-4,
  title={{Accelerated Bearing Life-time Test Rig Development for Low Speed Data Acquisition}},
  author={Klausen, Andreas and Folgerø, Roy Werner and Robbersmyr, Kjell G. and Karimi, Hamid Reza},
  journal={Modeling, Identification and Control},
  volume={38},
  number={3},
  pages={143--156},
  year={2017},
  doi={10.4173/mic.2017.3.4},
  publisher={Norwegian Society of Automatic Control}
};

Article

“Accelerated Bearing Life-time Test Rig Development for Low Speed Data Acquisition”