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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A Novel Feature Extraction Method using Deep Neural Network for Rolling Bearing Fault Diagnosis
基于深度神经网络的滚动轴承故障诊断新特征提取方法
作 者： Weining Lu
Xueqian Wang
Chunchun Yang
Tao Zhang
关键词： Fault Diagnosis  [故障诊断]
Feature Extraction  [ 特征提取]
Deep Neural Network  [ 深度神经网络]
出版年： 2015
会议名： 27th Chinese Control and Decision Conference (CCDC 2015)
第27届中国控制与决策会议（CCDC 2015）
届 次： 27th
摘 要： Rolling bearing fault diagnosis has received much attention because of its importance for the rotatory machinery. Feature extraction is the crucial part of rolling bearing fault diagnosis, which determines the diagnosis performance greatly. However, features extracted by many available methods cannot guarantee the sensitiveness to every interested fault category, which leads to incomplete diagnosis results and ability absence of handling with the situation that unknown-category fault appears. To solve this issue, the feature extraction method based on deep neural network (DNN) is proposed to extract a meaningful representation for bearing signal in this article. DNN is a new kind of machine learning tool with strong power of representation, which has been utilized as the feature extractors in lots of practical applications successfully. Afterwards, the effectiveness of this proposed approach is presented by using the actual rolling bearing data.

由于对旋转机械具有重要意义，滚动轴承故障诊断一直受到广泛关注。特征提取是滚动轴承故障诊断的关键环节，其性能在很大程度上决定了整体诊断效果。然而，许多现有方法提取的特征无法保证对所有目标故障类型的敏感性，导致诊断结果不完整，并且难以应对出现未知类别的故障情况。为解决这一问题，本文提出一种基于深度神经网络（DNN）的特征提取方法，用于从轴承信号中提取有意义的表示。DNN是一种具有强大表征能力的新颖机器学习工具，在众多实际应用中已成功被用作特征提取器。随后，通过实际滚动轴承数据验证了该方法的有效性。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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