Low Speed Rolling Bearing Fault Diagnosis Based on Frequency Bands Energy Features
Received date: 2016-03-06
Revised date: 2016-04-09
Online published: 2017-05-30
When the rolling bearing of a helicopter swash plate works in a low speed environment, the characteristic frequency of faults completely submerged in various interferences, making the traditional diagnosis method depending on spectral peaks of characteristic frequencies ineffective. To solve the problem, a low speed rolling bearing fault diagnosis method based on frequency bands energy features is proposed. FFT and power spectrum of the fault signal are calculated. Energy distribution characteristics in the signals of four kinds of vibrations, i.e., normal, inner ring, outer ring and ball fault, are used to construct a fault feature vector. Finally, support vector machine (SVM), which can use small scale samples, is used to construct a classifer used for determining the type of fault. On an experimental platform, simulation test was carried out in a low speed working environment of rolling bearing of a helicopter swash plate. Analysis of the collected vibration signals shows that, compared with the traditional LMD and envelope spectrum characteristics method, the adaptability and superiority of the proposed method in low speed rolling bearing fault diagnoses are verifed.
XIONG Bang-shu, WU Qiang-qiang, LI Xin-min, MO Yan, HUANG Jian-ping . Low Speed Rolling Bearing Fault Diagnosis Based on Frequency Bands Energy Features[J]. Journal of Applied Sciences, 2017 , 35(3) : 366 -372 . DOI: 10.3969/j.issn.0255-8297.2017.03.010
[1] 倪先平. 直升机手册[M]. 北京:航空工业出版社,2003.
[2] 朱可恒. 滚动轴承振动信号特征提取及诊断方法研究[D]. 大连:大连理工大学,2013.
[3] 马新娜,杨绍普. 滚动轴承复合故障诊断的自适应方法研究[J]. 振动与冲击,2016, 35(10):145-150. Ma X N, Yang S P. Study of adaptive compound fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2016, 35(10):145-150. (in Chinese)
[4] Feng Z P, Liang M, Chu F L. Recent advances in time-frequency analysis methods for machinery fault diagnosis:a review with application examples[J]. Mechanical Systems and Signal Processing, 2013, 38(1):165-205.
[5] 程军圣,于德介,杨宇. 基于SVM和EMD包络谱的滚动轴承故障诊断方法[J]. 系统工程理论与实践,2005, 9(6):131-136. Cheng J S, Yu D J, Yang Y. A fault diagnosis approach for roller bearing based on SVM and EMD envelope spectrum[J]. Systems Engineering-Theory and Practice, 2005, 9(6):131-136. (in Chinese)
[6] 杨宇,王欢欢,程军圣. 基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J]. 航空动力学报,2012, 27(5):1153-1158. Yang Y, Wang H H, Cheng J S. Application of envelope spectrum characteristics based on LMD to roller bearing fault diagnosis[J]. Journal of Aerospace Power, 2012, 27(5):1153-1158. (in Chinese)
[7] Zhu K H, Song X G, Xue D X. A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm[J]. Measurement, 2014, 47(1):669-675.
[8] Poursaeidi M H, Kundakcioglu O E. Robust support vector machines for multiple instance learning[J]. Annals of Operations Research, 2014, 216(1):205-227.
[9] 向阳辉,张干清,庞佑霞. 结合SVM和改进证据理论的多信息融合故障诊断[J]. 振动与冲击,2015, 34(13):71-77. Xiang Y H, Zhang G Q, Pang Y X. Fault diagnosis of multi-information fusion by SVM & improved evidence theory[J]. Journal of Vibration and Shock, 2015, 34(13):71-77. (in Chinese)
[10] 姜久亮,刘文艺,侯玉洁. 基于内积延拓LMD及SVM的轴承故障诊断方法研究[J]. 振动与冲击,2016, 35(6):104-108. Jiang J L, Liu W Y, Hou Y J. Research on bearing fault diagnosis method based on integral waveform extension LMD and SVM[J]. Journal of Vibration and Shock, 2016, 35(6):104-108. (in Chinese)
[11] Wang Y S, Ma Q H, Zhu Q. An intelligent approach for engine fault diagnosis based on HilbertHuang transform and support vector machine[J]. Applied Acoustics, 2014, 75(1):1-9.
/
| 〈 |
|
〉 |
