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Period estimation of deconvolution (MCKD,CYCBD,SMHD)

version 1.2 (464 KB) by Yonghao Miao
Matlab implementation of the period estimation of BDMs based on the iterative algorithm (MCKD,CYCBD,SMHD)

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Updated 13 Jul 2021

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Blind deconvolution methods, such as minimum entropy deconvolution (MED), Maximum correlated Kurtosis deconvolution (MCKD) and Maximum second-order cyclostationarity blind deconvolution (CYCBD), which can counteract the effect of the transmission path, have been widely applied in machinery fault diagnosis. Taking the periodicity and the impulsiveness into consideration simultaneously, MCKD, CYCBD can solve the problem of MED which prefers to focus on the random impulse rather than the periodic fault impulses. Yet, the superiority of MCKD and CYCBD highly depends on the prior fault period. In industrial applications, it is difficult to accurately obtain the fault period due to the rotating speed fluctuation and the measurement problem.
Therefore, we firstly proposed to estimate the iterative period by using the iterative algorithm to solve the problem of the prior period in blind deconvolution methods. According to the principle of autocorrelation, that is it will show a higher value when the time delay meets the period or its multiple, the location with local maximum value is selected as the iterative period in MCKD. And envelope harmonic product spectrum (EHPS), is initially tailored to estimate the characteristic frequency in CYCBD. The period estimation based on the iterative algorithm in BDMs can help MCKD and CYCBD apply in the machinery fault diagnosis without prior knowledge.
The matlab codes of period estimation using autocorrelation permit to reproduce some results in the papers:
[1] Y. Miao, M. Zhao, J. Lin, Y. Lei, Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings, Mechanical Systems and Signal Processing, 92 (2017) 173-195.
[2] Y. Miao, M. Zhao, K. Liang, J. Lin, Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal, Renewable Energy, 151 (2020) 192-203.
The matlab codes of period estimation using EHPS permit to reproduce some results in the papers:
[1] B. Zhang, Y. Miao, J. Lin, Y. Yi, Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis, Mechanical Systems and Signal Processing, 158 (2021) 107736.
[2] Y. Miao, B. Zhang, J. Lin, M. Zhao, H. Liu, Z. Liu, H. Li, A review on the application of blind deconvolution in machinery fault diagnosis, Mechanical Systems and Signal Processing, 163 (2022) 108202.
In addition, the matlab codes of the deconvolution method, Sparse maximum harmonics-to-noise-ratio deconvolution (SMHD), permit to reproduce some results in the papers:
[1] Y. Miao, M. Zhao, J. Lin, X. Xu, Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings, Measurement Science and Technology, 27 (2016) 105004.
[2] Y. Miao, B. Zhang, J. Lin, M. Zhao, H. Liu, Z. Liu, H. Li, A review on the application of blind deconvolution in machinery fault diagnosis, Mechanical Systems and Signal Processing, 163 (2022) 108202.
Copyright (c) belongs to the authors of the papers. An acknowledgment for the codes and the citations about all the papers above must be included in the publications as long as the codes are used.
Our works and full texts can refer
https://www.researchgate.net/profile/Yonghao-Miao
https://scholar.google.com.hk/citations?user=gRZ_iZsAAAAJ&hl=zh-CN&oi=ao

Cite As

Yonghao Miao (2021). Period estimation of deconvolution (MCKD,CYCBD,SMHD) (https://www.mathworks.com/matlabcentral/fileexchange/95313-period-estimation-of-deconvolution-mckd-cycbd-smhd), MATLAB Central File Exchange. Retrieved .

Miao, Yonghao, et al. “Application of an Improved Maximum Correlated Kurtosis Deconvolution Method for Fault Diagnosis of Rolling Element Bearings.” Mechanical Systems and Signal Processing, vol. 92, Elsevier BV, Aug. 2017, pp. 173–95, doi:10.1016/j.ymssp.2017.01.033.

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Zhang, Boyao, et al. “Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and Its Application for Locomotive Bearing Fault Diagnosis.” Mechanical Systems and Signal Processing, vol. 158, Elsevier BV, Sept. 2021, p. 107736, doi:10.1016/j.ymssp.2021.107736.

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Miao, Yonghao, et al. “Sparse Maximum Harmonics-to-Noise-Ratio Deconvolution for Weak Fault Signature Detection in Bearings.” Measurement Science and Technology, vol. 27, no. 10, IOP Publishing, Aug. 2016, p. 105004, doi:10.1088/0957-0233/27/10/105004.

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Miao, Yonghao, et al. “A Review on the Application of Blind Deconvolution in Machinery Fault Diagnosis.” Mechanical Systems and Signal Processing, vol. 163, Elsevier BV, Jan. 2022, p. 108202, doi:10.1016/j.ymssp.2021.108202.

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MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
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00 subfunction

01 IMCKD

02 ACYCBD

03 SMHD