DO, Van Tuan ;NGUYEN, Le Cuong . Adaptive Empirical Mode Decomposition for Bearing Fault Detection. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 62, n.5, p. 281-290, may 2016. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2015.3079.
Do, V., & Nguyen, L. (2016). Adaptive Empirical Mode Decomposition for Bearing Fault Detection. Strojniški vestnik - Journal of Mechanical Engineering, 62(5), 281-290. doi:http://dx.doi.org/10.5545/sv-jme.2015.3079
@article{sv-jmesv-jme.2015.3079,
author = {Van Tuan Do and Le Cuong Nguyen},
title = {Adaptive Empirical Mode Decomposition for Bearing Fault Detection},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {62},
number = {5},
year = {2016},
keywords = {bearing fault detection, Hilbert-Huang transforms, empirical mode decomposition, intrinsic mode function, envelope analysis, nominal frequency},
abstract = {Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried out.},
issn = {0039-2480}, pages = {281-290}, doi = {10.5545/sv-jme.2015.3079},
url = {https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/}
}
Do, V.,Nguyen, L. 2016 May 62. Adaptive Empirical Mode Decomposition for Bearing Fault Detection. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 62:5
%A Do, Van Tuan %A Nguyen, Le Cuong %D 2016 %T Adaptive Empirical Mode Decomposition for Bearing Fault Detection %B 2016 %9 bearing fault detection, Hilbert-Huang transforms, empirical mode decomposition, intrinsic mode function, envelope analysis, nominal frequency %! Adaptive Empirical Mode Decomposition for Bearing Fault Detection %K bearing fault detection, Hilbert-Huang transforms, empirical mode decomposition, intrinsic mode function, envelope analysis, nominal frequency %X Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried out. %U https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/ %0 Journal Article %R 10.5545/sv-jme.2015.3079 %& 281 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 62 %N 5 %@ 0039-2480 %8 2016-05-16 %7 2016-05-16
Do, Van Tuan, & Le Cuong Nguyen. "Adaptive Empirical Mode Decomposition for Bearing Fault Detection." Strojniški vestnik - Journal of Mechanical Engineering [Online], 62.5 (2016): 281-290. Web. 19 Nov. 2024
TY - JOUR AU - Do, Van Tuan AU - Nguyen, Le Cuong PY - 2016 TI - Adaptive Empirical Mode Decomposition for Bearing Fault Detection JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2015.3079 KW - bearing fault detection, Hilbert-Huang transforms, empirical mode decomposition, intrinsic mode function, envelope analysis, nominal frequency N2 - Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried out. UR - https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/
@article{{sv-jme}{sv-jme.2015.3079},
author = {Do, V., Nguyen, L.},
title = {Adaptive Empirical Mode Decomposition for Bearing Fault Detection},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {62},
number = {5},
year = {2016},
doi = {10.5545/sv-jme.2015.3079},
url = {https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/}
}
TY - JOUR AU - Do, Van Tuan AU - Nguyen, Le Cuong PY - 2016/05/16 TI - Adaptive Empirical Mode Decomposition for Bearing Fault Detection JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 62, No 5 (2016): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2015.3079 KW - bearing fault detection, Hilbert-Huang transforms, empirical mode decomposition, intrinsic mode function, envelope analysis, nominal frequency N2 - Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried out. UR - https://www.sv-jme.eu/article/adaptive-empirical-mode-decomposition-for-bearing-fault-detection/
Do, Van Tuan, AND Nguyen, Le Cuong. "Adaptive Empirical Mode Decomposition for Bearing Fault Detection" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 62 Number 5 (16 May 2016)
Strojniški vestnik - Journal of Mechanical Engineering 62(2016)5, 281-290
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Many techniques for bearing fault detection have been proposed. Two of the most effective approaches are using envelope analysis and the empirical mode decomposition method (EMD), also known as Hilbert-Huang transform (HHT), for vibration signals. Both approaches can detect the bearing fault when the vibration data are not strongly disturbed by noise. In the approach using EMD method, the EMD algorithm is used to decompose the vibration data into components with a well-defined instantaneous frequency called intrinsic mode functions (IMFs). Then a spectral analysis is used for selected IMFs to indicate the appearance of nominal bearing defect frequencies (nominal frequencies), which are caused by bearing faults. However, when the data are strongly disturbed by noise and other sources, the approach can be failed. The EMD algorithm generates IMFs itself; hence, the IMFs will also contain both a fault signal part and other components. It becomes more severe when the other components are dominant and have significant amplitudes near the same frequencies as the fault signal part. Moreover, in the IMF extracting process, the EMD methods keeps removing the low-frequency components until the residual is an IMF; therefore, until the IMF is found, some of the fault signal parts can be removed and will appear in the next IMFs. Therefore, it must be emphasized that the energy of the fault signal part can spread in some IMFs that will lead the detecting faulty features in any of those IMFs to be weak. In this paper, we address the weakness of the EMD method for bearing fault detection by introducing an adaptive EMD (AEMD). The AEMD algorithm is intended to generate IMFs so that one of them contains most of the energy of the fault signal part; thus, it assists our model to detect the bearing fault better. Moreover, the bearing fault detection model using the AEMD method with simulation data is compared with those of using envelope analysis and the latest version of the EMD, called an ensemble EMD algorithm. An application study of bearing fault detection with AEMD method is also carried out.