DYNAMIC THRESHOLD CALCULATION METHOD FOR BEARING OF HELICOPTER SWASHPLATE BASED ON BAYESIAN INFERENCE

CONG Lei; XIONG BangShu; LI Xin Min; SUN Wei; JIN XiaoQiang

doi:10.16579/j.issn.1001.9669.2020.04.005

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DYNAMIC THRESHOLD CALCULATION METHOD FOR BEARING OF HELICOPTER SWASHPLATE BASED ON BAYESIAN INFERENCE

更新时间：2022-09-22

- DYNAMIC THRESHOLD CALCULATION METHOD FOR BEARING OF HELICOPTER SWASHPLATE BASED ON BAYESIAN INFERENCE
- Journal of Mechanical Strength Vol. 42, Issue 4, Pages: 793-798(2020)
- 作者机构：
  
  1. 南昌航空大学图像处理与模式识别江西省重点实验室
  2. 中国直升机设计研究所直升机旋翼动力学重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2020.04.005
  CLC：
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CONG Lei, XIONG BangShu, LI Xin Min, et al. DYNAMIC THRESHOLD CALCULATION METHOD FOR BEARING OF HELICOPTER SWASHPLATE BASED ON BAYESIAN INFERENCE. [J]. 42(4):793-798(2020)
DOI：

CONG Lei, XIONG BangShu, LI Xin Min, et al. DYNAMIC THRESHOLD CALCULATION METHOD FOR BEARING OF HELICOPTER SWASHPLATE BASED ON BAYESIAN INFERENCE. [J]. 42(4):793-798(2020) DOI： 10.16579/j.issn.1001.9669.2020.04.005.

摘要

针对加权平均方法计算得到动态阈值准确度不高的问题,利用正常数据概率密度和不同故障类型数据概率密度的特点,提出一种基于贝叶斯推理的直升机自动倾斜器轴承动态阈值计算方法。首先,采用奇异值分解对振动信号进行预处理,滤除噪声对动态阈值计算结果的影响;其次,采用故障频率能量特征提取方法,分别提取正常数据和内圈、外圈、滚珠故障数据的特征;最后,采用贝叶斯推理方法分别计算正常数据与内圈、外圈、滚珠故障数据的动态阈值。利用故障植入的方法获取数据,开展动态阈值计算试验,结果表明,方法能够正确找到正常数据和不同故障类型数据概率密度曲线的分界点,具有较高的正确率,较低的漏报率,方法提高了动态阈值的计算准确度。

Abstract

The accuracy of dynamic threshold is low by using the weighted average method. In order to solve this problem,a dynamics threshold calculation method for swashplate bearing based on Bayesian inference is proposed by using the characteristics of the probability density of normal data and different fault type data. Firstly,the singular value decomposition was used to preprocess the vibration signal,and the influence of noise on the dynamic threshold calculation result was filtered out.Secondly,the fault frequency energy feature extracting method was used to extract the features of normal data and inner ring,outer ring and ball fault data. Finally,Bayesian inference method was used to calculate the dynamic threshold of normal data and inner ring,outer ring and ball fault data respectively. The data were obtained by using the fault implantation method,and the dynamic threshold experiments were carried out. The experimental results show that the proposed method which has higher true positive rate and lower false positive rate can correctly find the dividing point between the probability density curve of normal data and different fault type data. The proposed method improves the calculation accuracy of dynamic threshold.

关键词

贝叶斯推理自动倾斜器轴承动态阈值计算概率密度特征提取

Keywords

Bayesian inferenceBearing of swashplateDynamic threshold calculationProbability densityFeature extraction

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