CONDITION ASSESSMENT OF THIN-WALLED ROBOT BEARING BASED ON MULITI-SENSOR DATA FUSION

LIU Yang; XIE TanCheng; XU YanWei; WANG Bing; LI BanHu

doi:10.16579/j.issn.1001.9669.2019.05.005

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CONDITION ASSESSMENT OF THIN-WALLED ROBOT BEARING BASED ON MULITI-SENSOR DATA FUSION

更新时间：2022-09-22

- CONDITION ASSESSMENT OF THIN-WALLED ROBOT BEARING BASED ON MULITI-SENSOR DATA FUSION
- Journal of Mechanical Strength Vol. 41, Issue 5, Pages: 1042-1047(2019)
- 作者机构：
  
  1. 河南科技大学机电工程学院
  2. 智能数控装备河南省工程实验室
  3. 洛阳轴研科技股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2019.05.005
  CLC：
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LIU Yang, XIE TanCheng, XU YanWei, et al. CONDITION ASSESSMENT OF THIN-WALLED ROBOT BEARING BASED ON MULITI-SENSOR DATA FUSION. [J]. 41(5):1042-1047(2019)
DOI：

LIU Yang, XIE TanCheng, XU YanWei, et al. CONDITION ASSESSMENT OF THIN-WALLED ROBOT BEARING BASED ON MULITI-SENSOR DATA FUSION. [J]. 41(5):1042-1047(2019) DOI： 10.16579/j.issn.1001.9669.2019.05.005.

摘要

针对薄壁机器人轴承的状态评估,提出了基于信息融合的评估方法。采集声发射(Acoustic Emission, AE)和振动加速度信号,并在时域和频域上分别提取两种信号的特征作为识别薄壁机器人轴承状态的特征参量,利用自组织特征映射(Self Organizing Maps, SOM)神经网络对AE和振动信号的特征信息进行融合。经过实测数据的分析与验证,结果表明,该方法能够有效地判断出薄壁机器人轴承的状态类型。

Abstract

Based on the Condition Assessment of Thin-walled Robot Bearing, developed an assessment method of multi-sensor data fusion. Firstly, acoustic emission（AE） and vibration acceleration signal were collected, and the characteristics of two kinds of signals were extracted separately in time domain and frequency domain. Secondly, the characteristics of AE and vibration signals were used as the characteristic parameters to identify the fault type of thin-wall robot bearing. Finally, self organizing maps（SOM） neural network was used to integrate the characteristics information of AE and vibration signals. The result shows that the method can effectively identify the fault type of thin-walled robot bearing after analysed of the measured data.

关键词

薄壁机器人轴承状态评估振动声发射信息融合自组织特征映射神经网络

Keywords

Thin-walled robot bearingCondition assessmentVibrationAcoustic emissionData fusionSelf organizing maps（SOM）

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