Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

被引:6
作者
Behzad, Mehdi [1 ]
Kiakojouri, Amirmasoud [1 ]
Arghand, Hesam Addin [1 ]
Davoodabadi, Ali [1 ]
机构
[1] Sharif Univ Technol, Sch Mech Engn, Azadi Ave, Tehran 111559567, Iran
关键词
Inaccessible rolling bearing; indirect vibration measurement; continuous wavelet transform; fault detection; weak signature; DEMODULATION; TRANSFORM; DEFECT;
D O I
10.1177/1077546321989503
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale is selected to maximize the amplitude of bearing characteristic frequencies in comparison with the amplitude of the other frequencies. The results of the proposed method are compared with a popular method, energy-to-entropy ratio criterion, using two different sets of run-to-failure experimental data. Results indicate that the proposed method in this article is more effective and efficient for extracting the weak signatures and diagnosing inaccessible bearings from the recorded vibration signals.
引用
收藏
页码:1239 / 1250
页数:12
相关论文
共 27 条
[21]   Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring [J].
Singh, Sukhjeet ;
Kumar, Navin .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (03) :1341-1349
[22]   Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement [J].
Su, Wensheng ;
Wang, Fengtao ;
Zhu, Hong ;
Zhang, Zhixin ;
Guo, Zhenggang .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2010, 24 (05) :1458-1472
[23]   The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: Part 2 of the two related manuscripts that have a joint title as "Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement-Parts 1 and 2" [J].
Tse, Peter W. ;
Wang, Dong .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2013, 40 (02) :520-544
[24]   A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings [J].
Vakharia, V. ;
Gupta, V. K. ;
Kankar, P. K. .
JOURNAL OF VIBRATION AND CONTROL, 2015, 21 (16) :3123-3131
[25]   Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis [J].
Wang, Yi ;
Xu, Guanghua ;
Liang, Lin ;
Jiang, Kuosheng .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 54-55 :259-276
[26]   Wavelets for fault diagnosis of rotary machines: A review with applications [J].
Yan, Ruqiang ;
Gao, Robert X. ;
Chen, Xuefeng .
SIGNAL PROCESSING, 2014, 96 :1-15
[27]   Feature extraction method of wind turbine based on adaptive Monet wavelet and SVD [J].
Jiang, Yonghua ;
Tang, Baoping ;
Qin, Yi ;
Liu, Wenyi .
RENEWABLE ENERGY, 2011, 36 (08) :2146-2153