TY - JOUR
T1 - Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine
AU - Li, Yongbo
AU - Yang, Yuantao
AU - Wang, Xianzhi
AU - Liu, Binbin
AU - Liang, Xihui
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/8/18
Y1 - 2018/8/18
N2 - Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.
AB - Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.
KW - Complexity measurement
KW - Fault diagnosis
KW - HSDE
KW - Rolling bearing
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85047245458&partnerID=8YFLogxK
U2 - 10.1016/j.jsv.2018.04.036
DO - 10.1016/j.jsv.2018.04.036
M3 - 文章
AN - SCOPUS:85047245458
SN - 0022-460X
VL - 428
SP - 72
EP - 86
JO - Journal of Sound and Vibration
JF - Journal of Sound and Vibration
ER -