Adaptive Time-Varying Morphological Filtering (ATVMF)

version 1.0.0 (452 KB) by Chen Bingyan
ATVMF can adaptively determine the shape and scale of structural element (SE) according to the inherent characteristics of the signal.

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Updated 7 Apr 2022

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Morphological filtering is a typical nonlinear signal processing approach derived from the set theory. In this approach, the impulsive features in the signal can be excavated by interacting with a specified structural element (SE). The parameter (i.e., shape, height and length) selection of SE has an important influence on the result of morphological filtering. To solve this problem, an adaptive time-varying morphological filtering (ATVMF) method is proposed. ATVMF can adaptively determine the shape and scale of SE according to the inherent characteristics of the signal to be analyzed, effectively improving the transient feature extraction capability and computational efficiency. Publications using the ATVMF code are required to cite the following paper:
B. Chen, D. Song, W. Zhang, Y. Cheng, Z. Wang, A performance enhanced time-varying morphological filtering method for bearing fault diagnosis, Meas. J. Int. Meas. Confed. 176 (2021) 109163. https://doi.org/10.1016/j.measurement.2021.109163.
In addition, the definition of generalized morphological product operator (GMPO) has been proposed, which can construct new morphological product operators for feature extraction. The definition and application of GMPO are introduced in the following paper:
B. Chen, Y. Cheng, W. Zhang, G. Mei, Investigation on enhanced mathematical morphological operators for bearing fault feature extraction, ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.07.027.

Cite As

Chen Bingyan (2022). Adaptive Time-Varying Morphological Filtering (ATVMF) (https://www.mathworks.com/matlabcentral/fileexchange/109585-adaptive-time-varying-morphological-filtering-atvmf), MATLAB Central File Exchange. Retrieved .

Chen, Bingyan, et al. “A Performance Enhanced Time-Varying Morphological Filtering Method for Bearing Fault Diagnosis.” Measurement, vol. 176, Elsevier BV, May 2021, p. 109163, doi:10.1016/j.measurement.2021.109163.

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Created with R2017b
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