Abstract
Bearings are one of the most widely used components of rotary machines. To keep these bearings running in the best condition, several techniques for the early diagnosis of faults are applied to enable continuous monitoring of their condition and avoid unexpected faults that may cause damage to humans and materials. Several works have focused on the development of such technologies, including those that apply artificial intelligence, in the classification and diagnosis of faults. This work reports on a multi-layer perceptron (MLP) to classify the conditions of faulty bearings, using the envelope analysis method to extract the faulty features of the bearings. The proposed architecture is implemented on a field programmable gate array (FPGA) board, where the Digilent Zybo Z7-20 platform with a Zynq-7000 FPGA circuit from Xilinx was selected as the target. The Case Western Reserve University (CWRU) dataset, which is considered the standard reference for testing bearing fault classifications, is used to evaluate the performances. The results of the implemented embedded system are first compared to those obtained through MATLAB simulations and then to those obtained from the literature. These practical results provide an average accuracy of 95 and 89% for the fault-type identification and fault-severity identification, respectively.
Similar content being viewed by others
References
Attoui, I.; Oudjani, B.; Boutasseta, N.; Fergani, N.; Bouakkaz, M.; Bouraiou, A.: Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis. Int. J. Adv. Manuf. Technol. 106, 3409–3435 (2020). https://doi.org/10.1007/s00170-019-04729-4
Zhang, Y.; Ren, G.; Wu, D.; Wang, H.: Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method. Measurement 181, 109614 (2021). https://doi.org/10.1016/j.measurement.2021.109614
Duan, Z.; Wu, T.; Guo, S.; Shao, T.; Malekian, R.; Zhixiong, L.: Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review. Int. J. Adv. Manuf. Technol. 96, 803–819 (2018). https://doi.org/10.1007/s00170-017-1474-8
Tingarikar, G.; Choudhury, A.: Vibration analysis—based fault diagnosis of a dynamically loaded bearing with distributed defect. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-05862-7
Niu, X.; Zhu, L.; Ding, H.: New statistical moments for the detection of defects in rolling element bearings. Int. J. Adv. Manuf. Technol. 26, 1268–1274 (2005). https://doi.org/10.1007/s00170-004-2109-4
Siegel, D.; Ly, C.; Lee, J.: Methodology and framework for predicting helicopter rolling element bearing failure. IEEE Trans. Reliab. 61, 846–857 (2012). https://doi.org/10.1109/ICPHM.2011.6024339
Gligorijevic, J.; Gajic, D.; Brkovic, A.; Savic-Gajic, I.; Georgieva, O.; Di Gennaro, S.: Online condition monitoring of bearings to support total productive maintenance in the packaging materials industry. Sensors 16, 1–16 (2016). https://doi.org/10.3390/s16030316
Lin, H.; Ye, Y.-C.: Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms. Adv. Mech. Eng. 11, 1–12 (2019). https://doi.org/10.1177/1687814018816751
Feng, G.J.; Gu, J.; Zhen, D.; Aliwan, M.; Gu, F.S.; Ball, A.D.: Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis. Int. J. Autom. Comput. 12, 14–24 (2015). https://doi.org/10.1007/s11633-014-0862-x
Ibarra-zarate, D.; Tamayo-pazos, O.; Vallejo-guevara, A.: Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission. Int. J. Adv. Manuf. Technol. 104, 4155–4168 (2019). https://doi.org/10.1007/s00170-019-04171-6
Park, C.; Choi, Y.; Kim, Y.H.: Early fault detection in automotive ball bearings using the minimum variance cepstrum. Mech. Syst. Signal Process. 38, 534–548 (2013). https://doi.org/10.1016/j.ymssp.2013.02.017
Lee, D.; Hong, C.; Jeong, W.; Ahn, S.: Applied sciences time—frequency envelope analysis for fault detection of rotating machinery signals with impulsive noise. Appl. Sci. 11, 1–16 (2021). https://doi.org/10.3390/app11125373
Fei, S.: Fault diagnosis of bearing based on wavelet packet transform-phase space reconstruction-singular value decomposition and SVM classifier. Arab. J. Sci. Eng. 42, 1967–1975 (2017). https://doi.org/10.1007/s13369-016-2406-x
Wang, C.; Gan, M.; Zhu, C.: Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory. J. Intell. Manuf. 29, 937–951 (2018). https://doi.org/10.1007/s10845-015-1153-2
Zhang, J.; Huang, D.; Yang, J.; Liu, H.; Liu, X.: Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis. J. Mech. Sci. Technol. 31, 4599–4610 (2017). https://doi.org/10.1007/s12206-017-0906-6
Wang, J.; Du, G.; Zhu, Z.; Shen, C.; He, Q.: Fault diagnosis of rotating machines based on the EMD manifold. Mech. Syst. Signal Process. 135, 1–21 (2020). https://doi.org/10.1016/j.ymssp.2019.106443
Boudiaf, A.; Moussaoui, A.; Dahane, A.; Atoui, I.: A comparative study of various methods of bearing faults diagnosis using the Case Western Reserve University Data. J. Fail. Anal. Prev. 16, 271–284 (2016). https://doi.org/10.1007/s11668-016-0080-7
Bokde, N.; Feijóo, A.; Villanueva, D.; Kulat, K.: A review on hybrid empirical mode decomposition models for wind speed and wind power prediction. Energies 12, 1–42 (2019). https://doi.org/10.3390/en12020254
Liu, C.; Cichon, A.; Królczyk, G.; Li, Z.: Technology development and commercial applications of industrial fault diagnosis system: a review. Int. J. Adv. Manuf. Technol. (2021). https://doi.org/10.1007/s00170-021-08047-6
Guo, L.; Chen, J.; Li, X.: Rolling bearing fault classification based on envelope spectrum and support vector machine. J. Vib. Control 15, 1349–1363 (2009). https://doi.org/10.1177/1077546308095224
Tyagi, S.; Panigrahi, S.K.: An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis. J. Comput. Des. Eng. 4, 305–317 (2017). https://doi.org/10.1016/j.jcde.2017.05.002
Amini, A.; Entezami, M.; Papaelias, M.: Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Case Stud. Nondestruct. Test. Eval. 6, 8–16 (2016). https://doi.org/10.1016/j.csndt.2016.06.002
Smith, W.A.; Randall, R.B.: Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015). https://doi.org/10.1016/j.ymssp.2015.04.021
Liu, R.; Yang, B.; Zio, E.; Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33–47 (2018). https://doi.org/10.1016/j.ymssp.2018.02.016
Unal, M.; Onat, M.; Demetgul, M.; Kucuk, H.: Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement 58, 187–196 (2014). https://doi.org/10.1016/j.measurement.2014.08.041
Ben Ali, J.; Fnaiech, N.; Saidi, L.; Chebel-morello, B.; 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 (2015). https://doi.org/10.1016/j.apacoust.2014.08.016
Kuncan, M.; Kaplan, K.; Recep, M.; Kaya, Y.; Ertunç, H.M.: A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA Trans. J. 100, 346–357 (2020). https://doi.org/10.1016/j.isatra.2019.11.006
Li, Y.; Wang, X.; Si, S.; Huang, S.: Entropy based fault classification using the Case Western Reserve University data: a benchmark study. IEEE Trans. Reliab. 69, 754–767 (2020). https://doi.org/10.1109/TR.2019.2896240
Welcome to the Case Western Reserve University Bearing Data Center Website | Bearing Data Center (n.d.). https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website. Accessed 22 Mar 2020
Laala, W.; Guedidi, A.; Guettaf, A.: Bearing faults classification based on wavelet transform and artificial neural network. Int. J. Syst. Assur. Eng. Manag. (2020). https://doi.org/10.1007/s13198-020-01039-x
Sikder, N.; Shamim, A.; Arif, M.; Islam, M.M.M.; Al, A.: Induction motor bearing fault classification using extreme learning machine based on power features. Arab. J. Sci. Eng. 46, 8475–8491 (2021). https://doi.org/10.1007/s13369-021-05527-5
Bicakci, S.; Coramik, M.; Gunes, H.; Citak, H.; Ege, Y.: A new artificial neural network—based failure determination system for electric motors. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-021-05594-8
Meserkhani, A.; Jafari, S.M.; Rahi, A.: Experimental comparison of acoustic emission sensors in the detection of outer race defect of angular contact ball bearings by artificial neural network. Measurement 168, 108198 (2021). https://doi.org/10.1016/j.measurement.2020.108198
Seng, K.P.; Lee, P.J.; Ang, L.M.: Embedded intelligence on FPGA: survey, applications and challenges. Electronics 10, 1–33 (2021). https://doi.org/10.3390/electronics10080895
Ghazi Blaiech, A.; Ben Khalifa, K.; Valderrama, C.; Fernandes, M.A.C.; Hedi Bedoui, M.: A survey and taxonomy of FPGA-based deep learning accelerators. J. Syst. Archit. 98, 331–345 (2019). https://doi.org/10.1016/j.sysarc.2019.01.007
Saidi, A.; Ben Othman, S.; Dhouibi, M.; Ben Saoud, S.: FPGA-based implementation of classification techniques: a survey. Integration 81, 280–299 (2021). https://doi.org/10.1016/j.vlsi.2021.08.004
Kang, M.; Kim, J.; Kim, J.M.: An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals. IEEE Trans. Ind. Electron. 62, 2319–2329 (2015). https://doi.org/10.1109/TIE.2014.2361317
Camarena-Martinez, D.; Valtierra-Rodriguez, M.; Garcia-Perez, A.; Osornio-Rios, R.A.; Romero-Troncoso, R.D.J.: Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors. Sci. World J. 2014, 1–17 (2014). https://doi.org/10.1155/2014/908140
Contreras-hernandez, J.L.; Almanza-ojeda, D.L.; Ledesma, S.; Ibarra-manzano, M.A.: Motor fault detection using Quaternion Signal Analysis on FPGA. Measurement 138, 416–424 (2019). https://doi.org/10.1016/j.measurement.2019.01.088
Cabal-yepez, E.; Valtierra-rodriguez, M.; Romero-troncoso, R.J.; Garcia-perez, A.; Osornio-Rios, R.A.; Miranda-Vidales, H.; Alvarez-Salas, R.: FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors. Mech. Syst. Signal Process. 30, 123–130 (2012). https://doi.org/10.1016/j.ymssp.2012.01.021
Bengherbia, B.; Ould Zmirli, M.; Toubal, A.; Guessoum, A.: FPGA-based wireless sensor nodes for vibration monitoring system and fault diagnosis. Measurement 101, 81–92 (2017). https://doi.org/10.1016/j.measurement.2017.01.022
Bengherbia, B.; Kara, R.; Toubal, A.; Ould Zmirli, M.; Chadli, S.; Wira, P.: FPGA implementation of a wireless sensor node with a built-in ADALINE neural network coprocessor for vibration analysis and fault diagnosis in machine condition monitoring. Measurement 163, 107960 (2020). https://doi.org/10.1016/j.measurement.2020.107960
McInerny, S.A.; Dai, Y.: Basic vibration signal processing for bearing fault detection. IEEE Trans. Educ. 46, 149–156 (2003). https://doi.org/10.1109/TE.2002.808234
Acoustics, O.N.: FFT pruning applied to time domain interpolation and peak localization. IEEE Trans. Acoust. 35, 1776–1778 (1987). https://doi.org/10.1109/TASSP.1987.1165102
Marple, S.L.: Computing the discrete-time “Analytic” signal via FFT. IEEE Trans. SIGNAL Process. 47, 2600–2603 (1999). https://doi.org/10.1109/78.782222
Acknowledgements
This research work is fully supported by the Directorate General for Scientific Research and Technological Development (DGRSDT), Algeria. The software is provided by Xilinx under Xilinx University Program (XUP).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Toumi, Y., Bengherbia, B., Lachenani, S. et al. FPGA Implementation of a Bearing Fault Classification System Based on an Envelope Analysis and Artificial Neural Network. Arab J Sci Eng 47, 13955–13977 (2022). https://doi.org/10.1007/s13369-022-06599-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-022-06599-7