Bearing fault diagnosis method for shearer rocker arm based on blind source separation

被引：0

作者：

Taiyuan Mining Machinery Group Co., Ltd., Taiyuan ^{[1
]}

030032, China

不详 ^{[2
]}

030024, China

不详 ^{[3
]}

200011, China

机构：

[1] Taiyuan Mining Machinery Group Co., Ltd., Taiyuan

[2] College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan

[3] SKF (China) Sales Co., Ltd., Shanghai

来源：

Meitan Xuebao | / 11卷 / 2509-2513期

关键词：

Acceleration enveloping; Blind source separation; Fault diagnosis; Maximum signal to noise ratio; Shearer arm;

D O I：

10.13225/j.cnki.jccs.2015.7012

中图分类号：

学科分类号：

摘要：

Aiming at bearing failure of shearer rocker arm, this paper presents a bearing fault recognition method based on blind source separation. The method uses an optimized sliding average length to achieve blind source separation with maximum signal to noise ratio as the objective function, combining with the acceleration envelope method after separating the signal. Signal characteristics can be used to identify bearing faults. The method was verified through the shearer loading test rig. Two acceleration sensors were installed in different parts of the rocker arm. The collecting vibration signals were processed through acceleration enveloping before separation and after separation. The results show the new method could make the signal feature more obvious to recognize the defect bearing. The research results prove that the method is effective for predicting the failure of shearer arm. It can further improve the intelligent level in shearer failure field. © 2015, China Coal Society. All right reserved.

引用

页码：2509 / 2513

页数：4

共 9 条

[1]

Ge S., Key technology of intelligent coal mining equipment, Coal Science and Technology, 42, 9, pp. 7-11, (2014)

[2]

Colas M., Gelle G., Blind source separation: A tool for rotating machine monitoring by vibration analysis, Sound and Vibration, 248, 5, pp. 865-885, (2001)

[3]

Yang Y., Li Y., He Z., Et al., BSS method of gearbox multifault based on LCD, China Mechanical Engineering, 26, 8, pp. 1062-1066, (2015)

[4]

Zhang J., Gao H., Chen C., Et al., Blind source separation method and application for non-stationary vibration signal of high speed train, Journal of Mechanical Engineering, 50, 19, pp. 97-104, (2014)

[5]

Zhang Y., Li B., Wang Y., Fault diagnosis based on underdetermined blind source separation using potential energy function, Journal of Aerospace Power, 25, 1, pp. 218-223, (2010)

[6]

Li Z., Liu W., Yi X., Underdetermined blind source separation method of machine faults based on local mean decomposition, Journal of Mechanical Engineering, 47, 7, pp. 97-102, (2011)

[7]

Wang R., Zhan Y., Zhou H., Adaptive blind source separation algorithm for time-varying number of sources, Chinese Journal of Scientific Instrument, 36, 2, pp. 262-270, (2015)

[8]

Zhang J., Blind sources separation of non-stationary signals based on adaptive maximum signal-to-noise ratio method, Journal of Southwest Jiaotong University, 48, 4, pp. 769-775, (2013)

[9]

Liu Y., Research of source separation algorithm based on maximum signal noise ratio and application in vibration signal, pp. 26-32, (2011)

← 1 →