Abstract
It is still a challenge for condition monitoring and fault diagnosis for rolling element bearings working under variable speed, while some conventional diagnostic methods are useless. Order tracking is commonly used as an effective tool of the non-stationary vibration analysis for rotating machinery and tacho-less order tracking may be more applicable for practical situations, while the key point is to obtain more accurate instantaneous rotating speed. What’s more, the collected bearing fault vibration signals always contain strong background noise that greatly affects the result of fault feature extraction. To solve these problems, a fault feature extraction method is proposed in this study. The Chirplet-based approach was used to estimate some obvious harmonics of instantaneous rotating speed and the average value of these components was regarded as the final instantaneous rotating speed to reduce the estimation error. Since the higher order energy operator (HOEO) can not only improve the signal-to-noise ratio and signal-to-interference ratio, but is also easily applied, an adaptive combined HOEO method based on hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC) was constructed to enhance the impulse components, and then, the fault features were extracted by the order spectrum analysis. Simulation and experimental results indicate that the proposed algorithm is effective for rolling element bearings’ fault diagnosis under variable speed condition.
Similar content being viewed by others
References
H. Xiao, X. Zhou, J. Liu and Y. Shao, Vibration transmission and energy dissipation through the gear-shaft-bearinghousing system subjected to impulse force on gear, Measurement, 102 (2017) 64–79.
H. Jiang and C. Duan, An adaptive lifting scheme and its application in rolling bearing fault diagnosis, J. of Vibroengineering, 14 (2) (2012) 759–770.
N. Upadhyay and P. K. Kankar, Diagnosis of bearing defects using tunable Q-wavelet transform, J. of Mechanical Science and Technology, 32 (2) (2018) 549–558.
M. Saxena, O. O. Bannet, M. Gupta and R. P. Rajoriaet, Bearing fault monitoring using CWT based vibration signature, Procedia Engineering, 144 (2016) 234–241.
Q. Xing, Y. Xu, Y. Peng, W. Zhang, Y. Li and L Tang, Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution, J. of Mechanical Science and Technology, 31 (4) (2017) 1587–1601.
H. Zhao, M. Sun, W. Deng and X. Yang, A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing, Entropy, 19 (1) (2016) 14.
D. H. Lee, J. H. Ahn and B. H. Koh, Fault detection of bearing systems through EEMD and optimization algorithm, Sensors, 17 (11) (2017) 2477.
H. Endo and R. B. Randall, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mechanical Systems and Signal Processing, 21 (2) (2007) 906–919.
G. L. Mcdonald, Q. Zhao and M. J. Zuo, Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection, Mechanical Systems and Signal Processing, 33 (2012) 237–255.
R. B. Randall and W. A. Smith, Application of the Teager Kaiser energy operator to machine diagnostics, Tenth Dst Group International Conference on Health and Usage Monitoring Systems (2017).
H. Zhao and L. Li, Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator, Iet Renewable Power Generation, 11 (4) (2017) 453–460.
D. Han, N. Zhao and P. Shi, A new fault diagnosis method based on deep belief network and support vector machine with Teager–Kaiser energy operator for bearings, Advances in Mechanical Engineering, 9 (12) (2017) 1–11.
J. M. O'Toole, A. Temko and N. Stevenson, Assessing instantaneous energy in the EEG: a non-negative, frequencyweighted energy operator, Proceedings of IEEE Conference (2014) 3288–3291.
Y. Imaouchen, M. Kedadouche, R. Alkama and M. Thomas, A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection, Mechanical Systems and Signal Processing, 82 (2017) 103–116.
P. Maragos and A. Potamianos, Higher order differential energy operators, IEEE Signal Processing Letters, 2 (8) (1995) 152–154.
H. Faghidi and M. Liang, Bearing fault identification by higher order energy operator fusion: A non-resonance based approach, J. of Sound and Vibration, 381 (2016) 83–100.
Y. Wang, G. Xu, Q. Zhang, D. Liu and K. Jiang, Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions, J. of Sound and Vibration, 348 (2015) 381–396.
H. Vold and H. Leuridan, High resolution order tracking at extreme slew rates, using Kalman tracking filters, Shock and Vibration, 2 (6) (1995) 507–515.
M. Zhao, J. Lin, X. Wang, Y. Lei and J. Cao, A tacho-less order tracking technique for large speed variations, Mechanical Systems and Signal Processing, 40 (1) (2013) 76–90.
S. Schmidt, P. S. Heyns and J. P. D. Villiers, A tacholess order tracking methodology based on a probabilistic approach to incorporate angular acceleration information into the maxima tracking process, Mechanical Systems and Signal Processing, 100 (2018) 630–646.
R. B. Randall, Vibration-based diagnostics of gearboxes under variable speed and load conditions, Meccanica, 51 (12) (2016) 1–13.
H. Huang, N. Baddour and M. Liang, Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction, J. of Sound and Vibration, 414 (2018) 43–60.
J. Urbanek, T. Barszcz and J. Antoni, A two-step procedure for estimation of instantaneous rotational speed with large fluctuations, Mechanical Systems and Signal Processing, 38 (1) 2013 96–102.
K. Chen, F. Zhou, L. Yin, S. Wang, Y. Wang and F. Wan, A hybrid particle swarm optimizer with sine cosine acceleration coefficients, Information Sciences, 422 (2017).
Y. Ai, J. Guan, C. Fei, J. Tian and F. Zhang, Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance, Mechanical Systems and Signal Processing, 88 (2017) 123–136.
D. Zhao, J. Li and W. Cheng, Feature extraction of faulty rolling element bearing under variable rotational speed and gear interferences conditions, Shock and Vibration, 3 (2015) 1–9.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Sungsoo Na
Danchen Zhu received his Master’s from Naval University of Engineering, China, in 2016. He is now pursuing a doctorate at Naval University of Engineering. His research interests include signal processing and bearing fault diagnosis.
Yongxiang Zhang received the B.E. in Huazhong University of Science and Technology and the M.E. in Naval university of engineering. He is a Professor in Naval University of Engineering at present.
Rights and permissions
About this article
Cite this article
Zhu, D., Zhang, Y., Liu, S. et al. Adaptive combined HOEO based fault feature extraction method for rolling element bearing under variable speed condition. J Mech Sci Technol 32, 4589–4599 (2018). https://doi.org/10.1007/s12206-018-0905-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-018-0905-2