• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >A disassembly-free method for evaluation of spiral bevel gear assembly
【24h】

A disassembly-free method for evaluation of spiral bevel gear assembly

机译：螺旋伞齿轮总成的免拆卸评估方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The paper presents a novel method for evaluation of assembly of spiral bevel gears. The examination of the approaches to the problem of gear control diagnostics without disassembly has revealed that residual processes in the form of vibrations (or noise) are currently the most suitable to this end. According to the literature, contact pattern is a complex parameter for describing gear position. Therefore, the task is to determine the correlation between contact pattern and gear vibrations. Although the vibration signal contains a great deal of information, it also has a complex spectral structure and contains interferences. For this reason, the proposed method has three variants which determine the effect of preliminary processing of the signal on the results. In Variant 2, stage 1, the vibration signal is subjected to multichannel denoising using a wavelet transform (WT), and in Variant 3 - to a combination of WT and principal component analysis (PCA). This denoising procedure does not occur in Variant 1. Next, we determine the features of the vibration signal in order to focus on information which is crucial regarding the objective of the study. Given the lack of unequivocal premises enabling selection of optimum features, we calculate twenty features, rank them and finally select the appropriate ones using an algorithm. Diagnostic rules were created using artificial neural networks. We investigated the suitability of three network types: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM).
机译：本文提出了一种评估螺旋锥齿轮装配的新颖方法。在不拆卸的情况下对齿轮控制诊断问题的研究方法表明，目前以振动（或噪声）形式存在的剩余过程最适合此目的。根据文献，接触模式是用于描述档位的复杂参数。因此，任务是确定接触模式与齿轮振动之间的相关性。尽管振动信号包含大量信息，但它也具有复杂的频谱结构并包含干扰。由于这个原因，所提出的方法具有三个变体，它们确定了信号的预处理对结果的影响。在变体2，阶段1中，振动信号使用小波变换（WT）进行多通道降噪，在变体3中-进行WT和主成分分析（PCA）的组合。该降噪过程不会在变体1中发生。接下来，我们确定振动信号的特征，以便专注于对研究目标至关重要的信息。鉴于缺乏能够选择最佳特征的明确前提，我们计算了20个特征，对其进行了排序，最后使用算法选择了合适的特征。诊断规则是使用人工神经网络创建的。我们研究了三种网络类型的适用性：多层感知器（MLP），径向基函数（RBF）和支持向量机（SVM）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号