轴承在线早期故障检测的无监督张量深度迁移学习方法
CSTR:
作者:
作者单位:

1. 河南师范大学 计算机与信息工程学院,河南 新乡 453007;2. 河南科技大学 机电工程学院, 河南 洛阳 471000;3. 智慧商务与物联网技术河南省工程实验室,河南 新乡 453007

作者简介:

通讯作者:

E-mail: maowt@htu.edu.cn.

中图分类号:

TP277

基金项目:

国家重点研发计划重点专项基金项目(2018YFB1701400);河南省重大科技专项项目(221100220100); 河南省科技研发计划联合基金项目(222103810030).


Unsupervised tensor-based deep transfer learning for online early fault detection of bearing
Author:
Affiliation:

1. School of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China;2. School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471000,China;3. Engineering Lab of Intellgence Business & Internet of Things,Henan Province, Xinxiang 453007,China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    基于张量分解可有效挖掘信号高维本质信息的优点,提出一种无监督张量深度迁移学习方法.首先,构建基于张量表示的深度多任务异常检测模型,利用核心张量构建单分类异常检测规则表示,并建立超球规则适配机制,交替优化张量分解和域无关特征提取,以实现异常检测规则在离线轴承和在线目标轴承间的有效传递,完成在线无标记数据的异常检测;其次,提出一个基于异常概率贯序累积的非参数报警阈值设定方法,可在仅设定误报警率置信度的条件下自适应选择在线阈值,并给出该阈值合理性的理论分析.在IEEE PHM Challenge 2012轴承数据集上进行实验,结果表明,所提出方法可获得更好的检测实时性和更低的误报警数,为早期故障检测提供一种具有易部署性和鲁棒性的解决方案.

    Abstract:

    With the merit that tensor decomposition can represent high-dimensional essential information of signal effectively, this paper proposes an unsupervised tensor-based deep transfer learning approach. First, a tensor representation-based deep multi-task anomaly detection model is built. This model utilizes core tensor to construct the representation of one-class anomaly detection rule and establishes hypersphere rule adaptation mechanism. By running with an alternative optimization of tensor decomposition and common feature extraction, this model can conduct effective transition of anomaly detection rule from offline bearings to online target bearing and realize anomaly detection of online unlabeled data. Second, a nonparametric alarm threshold setting method is designed based on sequential accumulation of anomalous probability. This method can adaptively determine online threshold only requiring the confidence level of false alarm. Moreover, a theoretical analysis about the threshold's rationality is provided. Experimental results on the IEEE PHM Challenge 2012 dataset show that the proposed approach can obtain real-time detection performance and lower number of false alarm. The proposed approach is believed to supply a solution with better deployment and robustness.

    参考文献
    相似文献
    引证文献
引用本文

毛文涛,施华东,张艳娜,等.轴承在线早期故障检测的无监督张量深度迁移学习方法[J].控制与决策,2024,39(3):867-876

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-02-25
  • 出版日期: 2024-03-20
文章二维码