YIN, Aijun ;LU, Juncheng ;DAI, Zongxian ;LI, Jiang ;OUYANG, Qi . Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 62, n.12, p. 740-750, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2016.3694.
Yin, A., Lu, J., Dai, Z., Li, J., & Ouyang, Q. (2016). Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model. Strojniški vestnik - Journal of Mechanical Engineering, 62(12), 740-750. doi:http://dx.doi.org/10.5545/sv-jme.2016.3694
@article{sv-jmesv-jme.2016.3694,
author = {Aijun Yin and Juncheng Lu and Zongxian Dai and Jiang Li and Qi Ouyang},
title = {Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {62},
number = {12},
year = {2016},
keywords = {Isomap; dimensionality reduction; deep belief network (DBN); machine health; combined assessment model (CAM)},
abstract = {This paper presents a novel combined assessment model (CAM) for machine health assessment, in which 38 original features of the vibration signal were extracted from time domain analysis, frequency domain analysis, and wavelet packet transform (WPT), following which the nonlinear global algorithm Isomap was adopted for dimensionality reduction and extraction of the more representative features. Next, the acquired low-dimensional features array is input into the well trained deep belief network (DBN) model to evaluate the performance status of the bearing. Finally,after the bearing accelerated degradation data from Cincinnati University were investigated for further research, through the comparison experiments with two other popular dimensionality reduction methods (principal component analysis (PCA) and Laplacian Eigenmaps) and two other intelligent assessment algorithms (hidden Markov model (HMM) and back-propagation neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective for the evaluation of bearing performance degradation.},
issn = {0039-2480}, pages = {740-750}, doi = {10.5545/sv-jme.2016.3694},
url = {https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/}
}
Yin, A.,Lu, J.,Dai, Z.,Li, J.,Ouyang, Q. 2016 June 62. Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 62:12
%A Yin, Aijun %A Lu, Juncheng %A Dai, Zongxian %A Li, Jiang %A Ouyang, Qi %D 2016 %T Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model %B 2016 %9 Isomap; dimensionality reduction; deep belief network (DBN); machine health; combined assessment model (CAM) %! Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model %K Isomap; dimensionality reduction; deep belief network (DBN); machine health; combined assessment model (CAM) %X This paper presents a novel combined assessment model (CAM) for machine health assessment, in which 38 original features of the vibration signal were extracted from time domain analysis, frequency domain analysis, and wavelet packet transform (WPT), following which the nonlinear global algorithm Isomap was adopted for dimensionality reduction and extraction of the more representative features. Next, the acquired low-dimensional features array is input into the well trained deep belief network (DBN) model to evaluate the performance status of the bearing. Finally,after the bearing accelerated degradation data from Cincinnati University were investigated for further research, through the comparison experiments with two other popular dimensionality reduction methods (principal component analysis (PCA) and Laplacian Eigenmaps) and two other intelligent assessment algorithms (hidden Markov model (HMM) and back-propagation neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective for the evaluation of bearing performance degradation. %U https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/ %0 Journal Article %R 10.5545/sv-jme.2016.3694 %& 740 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 62 %N 12 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Yin, Aijun, Juncheng Lu, Zongxian Dai, Jiang Li, & Qi Ouyang. "Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model." Strojniški vestnik - Journal of Mechanical Engineering [Online], 62.12 (2016): 740-750. Web. 20 Dec. 2024
TY - JOUR AU - Yin, Aijun AU - Lu, Juncheng AU - Dai, Zongxian AU - Li, Jiang AU - Ouyang, Qi PY - 2016 TI - Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.3694 KW - Isomap; dimensionality reduction; deep belief network (DBN); machine health; combined assessment model (CAM) N2 - This paper presents a novel combined assessment model (CAM) for machine health assessment, in which 38 original features of the vibration signal were extracted from time domain analysis, frequency domain analysis, and wavelet packet transform (WPT), following which the nonlinear global algorithm Isomap was adopted for dimensionality reduction and extraction of the more representative features. Next, the acquired low-dimensional features array is input into the well trained deep belief network (DBN) model to evaluate the performance status of the bearing. Finally,after the bearing accelerated degradation data from Cincinnati University were investigated for further research, through the comparison experiments with two other popular dimensionality reduction methods (principal component analysis (PCA) and Laplacian Eigenmaps) and two other intelligent assessment algorithms (hidden Markov model (HMM) and back-propagation neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective for the evaluation of bearing performance degradation. UR - https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/
@article{{sv-jme}{sv-jme.2016.3694},
author = {Yin, A., Lu, J., Dai, Z., Li, J., Ouyang, Q.},
title = {Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {62},
number = {12},
year = {2016},
doi = {10.5545/sv-jme.2016.3694},
url = {https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/}
}
TY - JOUR AU - Yin, Aijun AU - Lu, Juncheng AU - Dai, Zongxian AU - Li, Jiang AU - Ouyang, Qi PY - 2018/06/27 TI - Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 62, No 12 (2016): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.3694 KW - Isomap, dimensionality reduction, deep belief network (DBN), machine health, combined assessment model (CAM) N2 - This paper presents a novel combined assessment model (CAM) for machine health assessment, in which 38 original features of the vibration signal were extracted from time domain analysis, frequency domain analysis, and wavelet packet transform (WPT), following which the nonlinear global algorithm Isomap was adopted for dimensionality reduction and extraction of the more representative features. Next, the acquired low-dimensional features array is input into the well trained deep belief network (DBN) model to evaluate the performance status of the bearing. Finally,after the bearing accelerated degradation data from Cincinnati University were investigated for further research, through the comparison experiments with two other popular dimensionality reduction methods (principal component analysis (PCA) and Laplacian Eigenmaps) and two other intelligent assessment algorithms (hidden Markov model (HMM) and back-propagation neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective for the evaluation of bearing performance degradation. UR - https://www.sv-jme.eu/article/isomap-and-deep-belief-network-based-machine-health-combined-assessment-model/
Yin, Aijun, Lu, Juncheng, Dai, Zongxian, Li, Jiang, AND Ouyang, Qi. "Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 62 Number 12 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 62(2016)12, 740-750
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper presents a novel combined assessment model (CAM) for machine health assessment, in which 38 original features of the vibration signal were extracted from time domain analysis, frequency domain analysis, and wavelet packet transform (WPT), following which the nonlinear global algorithm Isomap was adopted for dimensionality reduction and extraction of the more representative features. Next, the acquired low-dimensional features array is input into the well trained deep belief network (DBN) model to evaluate the performance status of the bearing. Finally,after the bearing accelerated degradation data from Cincinnati University were investigated for further research, through the comparison experiments with two other popular dimensionality reduction methods (principal component analysis (PCA) and Laplacian Eigenmaps) and two other intelligent assessment algorithms (hidden Markov model (HMM) and back-propagation neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective for the evaluation of bearing performance degradation.