LI, Zhuang ;MA, Zhiyong ;LIU, Yibing ;TENG, Wei ;JIANG, Rui . Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.1, p. 63-73, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/>. Date accessed: 25 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2014.1769.
Li, Z., Ma, Z., Liu, Y., Teng, W., & Jiang, R. (2015). Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network. Strojniški vestnik - Journal of Mechanical Engineering, 61(1), 63-73. doi:http://dx.doi.org/10.5545/sv-jme.2014.1769
@article{sv-jmesv-jme.2014.1769,
author = {Zhuang Li and Zhiyong Ma and Yibing Liu and Wei Teng and Rui Jiang},
title = {Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {1},
year = {2015},
keywords = {relative wavelet energy; adaptive resonance theory; neural network; pattern recognition; gearbox; fault detection},
abstract = {In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault.},
issn = {0039-2480}, pages = {63-73}, doi = {10.5545/sv-jme.2014.1769},
url = {https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/}
}
Li, Z.,Ma, Z.,Liu, Y.,Teng, W.,Jiang, R. 2015 June 61. Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:1
%A Li, Zhuang %A Ma, Zhiyong %A Liu, Yibing %A Teng, Wei %A Jiang, Rui %D 2015 %T Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network %B 2015 %9 relative wavelet energy; adaptive resonance theory; neural network; pattern recognition; gearbox; fault detection %! Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network %K relative wavelet energy; adaptive resonance theory; neural network; pattern recognition; gearbox; fault detection %X In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault. %U https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/ %0 Journal Article %R 10.5545/sv-jme.2014.1769 %& 63 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 61 %N 1 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Li, Zhuang, Zhiyong Ma, Yibing Liu, Wei Teng, & Rui Jiang. "Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.1 (2015): 63-73. Web. 25 Apr. 2024
TY - JOUR AU - Li, Zhuang AU - Ma, Zhiyong AU - Liu, Yibing AU - Teng, Wei AU - Jiang, Rui PY - 2015 TI - Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1769 KW - relative wavelet energy; adaptive resonance theory; neural network; pattern recognition; gearbox; fault detection N2 - In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault. UR - https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/
@article{{sv-jme}{sv-jme.2014.1769},
author = {Li, Z., Ma, Z., Liu, Y., Teng, W., Jiang, R.},
title = {Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {1},
year = {2015},
doi = {10.5545/sv-jme.2014.1769},
url = {https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/}
}
TY - JOUR AU - Li, Zhuang AU - Ma, Zhiyong AU - Liu, Yibing AU - Teng, Wei AU - Jiang, Rui PY - 2018/06/27 TI - Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 1 (2015): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1769 KW - relative wavelet energy, adaptive resonance theory, neural network, pattern recognition, gearbox, fault detection N2 - In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault. UR - https://www.sv-jme.eu/article/crack-fault-detection-for-a-gearbox-using-discrete-wavelet-transform-and-an-adaptive-resonance-theory-neural-network/
Li, Zhuang, Ma, Zhiyong, Liu, Yibing, Teng, Wei, AND Jiang, Rui. "Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 1 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 61(2015)1, 63-73
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands. The changes of sub-band energy are thought to be caused by the crack fault. Therefore, the relative wavelet energy is proposed as a feature. An artificial neural network is introduced for the detection of crack faults. Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults. An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector. The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault. The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault.