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国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习

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标 题： A Cross Domain Feature Extraction Method for Bearing Fault diagnosis based on Balanced Distribution Adaptation
基于均衡分布自适应的轴承故障诊断跨域特征提取方法
作 者： Jiawei Gu
Yanxue Wang
关键词： Fault diagnosis  [故障诊断]
Feature extraction  [ 特征提取]
Cross domain  [ 跨域]
Balanced distribution adaptation  [ 均衡分布适应]
Transfer learning  [ 迁移学习]
出版年： 2019
会议名： 10th Prognostics and System Health Management Conference (PHM-Qingdao 2019)
第十届预测与系统健康管理会议（PHM-青岛2019）
届 次： 10th
摘 要： Traditional intelligent fault diagnosis techniques for rotating machines have two limitations: 1) Big data with fault information is not available in some cases; 2) The training and testing data are often drawn under discrepant distribution. Thus, transfer component analysis (TCA) has been designed to reduce the distance of marginal distribution between domains. The joint distribution adaptation (JDA) was proposed to simultaneously reduced the difference between the conditional distribution and marginal distribution in source or target domains. However, these two distributions are often treated equally in these existing methods, which will lead to poor performance in practical applications. Therefore, a cross-domain feature extraction method based on balanced distribution adaptation algorithm (BDA) has been proposed, which can adaptively utilize the importance of difference between marginal distribution and conditional distribution. It should be noted that several existing cross domain feature extraction methods can be treated as special cases of BDA. As a new method in the field of transfer learning, BDA is an effective cross-domain feature extraction method. The validity of the BDA algorithm has been successfully evaluated in the actual data set in this paper.

传统的旋转机械智能故障诊断技术存在两个局限性：1）在某些情况下，缺乏故障信息的大数据； 2)) 训练数据和测试数据往往分布不一致。因此，迁移成分分析（TCA）被设计用于减小不同域之间的边缘分布距离。联合分布自适应（JDA）则被提出，以同时减少源域或目标域中条件分布与边缘分布之间的差异。然而，现有方法通常将这两种分布同等对待，这会导致在实际应用中性能不佳。因此，本文提出了一种基于平衡分布自适应算法（BDA）的跨域特征提取方法，该方法能够自适应地利用边缘分布与条件分布之间差异的重要性。值得注意的是，现有的多种跨域特征提取方法可视为BDA的特例。作为迁移学习领域的一种新方法，BDA是一种有效的跨域特征提取方法。本文成功地在真实数据集上验证了BDA算法的有效性。
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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