TAN, Wei ;CHEN, Xiao-an ;DONG, Shao-jiang . A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.12, p. 719-724, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/>. Date accessed: 22 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2013.955.
Tan, W., Chen, X., & Dong, S. (2013). A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter. Strojniški vestnik - Journal of Mechanical Engineering, 59(12), 719-724. doi:http://dx.doi.org/10.5545/sv-jme.2013.955
@article{sv-jmesv-jme.2013.955,
author = {Wei Tan and Xiao-an Chen and Shao-jiang Dong},
title = {A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {59},
number = {12},
year = {2013},
keywords = {Multiscale morphological filter; Structure element; Particle swarm; Optimization algorithm; Noise reduction},
abstract = {In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters’ structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method.},
issn = {0039-2480}, pages = {719-724}, doi = {10.5545/sv-jme.2013.955},
url = {https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/}
}
Tan, W.,Chen, X.,Dong, S. 2013 June 59. A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:12
%A Tan, Wei %A Chen, Xiao-an %A Dong, Shao-jiang %D 2013 %T A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter %B 2013 %9 Multiscale morphological filter; Structure element; Particle swarm; Optimization algorithm; Noise reduction %! A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter %K Multiscale morphological filter; Structure element; Particle swarm; Optimization algorithm; Noise reduction %X In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters’ structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method. %U https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/ %0 Journal Article %R 10.5545/sv-jme.2013.955 %& 719 %P 6 %J Strojniški vestnik - Journal of Mechanical Engineering %V 59 %N 12 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Tan, Wei, Xiao-an Chen, & Shao-jiang Dong. "A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.12 (2013): 719-724. Web. 22 Nov. 2024
TY - JOUR AU - Tan, Wei AU - Chen, Xiao-an AU - Dong, Shao-jiang PY - 2013 TI - A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.955 KW - Multiscale morphological filter; Structure element; Particle swarm; Optimization algorithm; Noise reduction N2 - In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters’ structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method. UR - https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/
@article{{sv-jme}{sv-jme.2013.955},
author = {Tan, W., Chen, X., Dong, S.},
title = {A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {59},
number = {12},
year = {2013},
doi = {10.5545/sv-jme.2013.955},
url = {https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/}
}
TY - JOUR AU - Tan, Wei AU - Chen, Xiao-an AU - Dong, Shao-jiang PY - 2018/06/28 TI - A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 12 (2013): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.955 KW - Multiscale morphological filter, Structure element, Particle swarm, Optimization algorithm, Noise reduction N2 - In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters’ structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method. UR - https://www.sv-jme.eu/article/a-new-method-for-machinery-fault-diagnoses-based-on-an-optimal-multiscale-morphological-filter/
Tan, Wei, Chen, Xiao-an, AND Dong, Shao-jiang. "A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 12 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 59(2013)12, 719-724
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters’ structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method.