...
  • 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 >Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment
【24h】

Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment

机译：基于隐马尔可夫模型和扰动属性投影的轴承性能退化评估

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

Hidden Markov model (HMM) has been widely applied in bearing performance degradation assessment. As a machine learning-based model, its accuracy, subsequently, is dependent on the sensitivity of the features used to estimate the degradation performance of bearings. It's a big challenge to extract effective features which are not influenced by other qualities or attributes uncorrelated with the bearing degradation condition. In this paper, a bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. NAP can filter out the effect of nuisance attributes in feature space through projection. The new feature space projected by NAP is more sensitive to bearing health changes and barely influenced by other interferences occurring in operation condition. To verify the effectiveness of the proposed method, two different experimental databases are utilized. The results show that the combination of HMM and NAP can effectively improve the accuracy and robustness of the bearing performance degradation assessment system.
机译：隐马尔可夫模型（HMM）已广泛应用于轴承性能退化评估中。作为基于机器学习的模型，其准确性随后取决于用于估计轴承退化性能的特征的敏感性。提取不受与轴承退化条件无关的其他质量或属性影响的有效特征，这是一个巨大的挑战。提出了一种基于隐马尔可夫模型和NAP的轴承性能退化评估方法。 NAP可以通过投影滤除特征空间中有害属性的影响。 NAP投影的新特征空间对轴承的健康变化更加敏感，几乎不受操作条件下发生的其他干扰的影响。为了验证所提出方法的有效性，利用了两个不同的实验数据库。结果表明，HMM和NAP的结合可以有效地提高轴承性能退化评估系统的准确性和鲁棒性。

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