基于1D-CNN联合特征提取的轴承健康监测与故障诊断
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作者简介:

刘立(1992-),男,博士,讲师,CCF专业会员,主要研究领域为工业物联网,无线传感网,机器学习.
韩光洁(1972-),男,博士,教授,博士生导师,CCF杰出会员,主要研究领域为工业互联网,智慧海洋,智能计算.
朱健成(1997-),男,硕士,主要研究领域为工业物联网.
毕远国(1980-),男,博士,教授,博士生导师,CCF专业会员,主要研究领域为车联网,软件定义网络,无线传感器网络,嵌入式系统.

通讯作者:

韩光洁,E-mail:hanguangjie@gmail.com

中图分类号:

TP181

基金项目:

国家重点研发计划(2017YFE0125300);江苏省重点研发计划(BE2019648)


Bearing Health Monitoring and Fault Diagnosis Based on Joint Feature Extraction in 1D-CNN
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Fund Project:

National Key Research and Development Program of China (2017YFE0125300); Key Research and Development Program of Jiangsu Province (BE2019648)

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    摘要:

    针对特定机械设备构建数据驱动的故障诊断模型缺乏泛化能力,而轴承作为各型机械的共有核心部件,对其健康状态的判定对不同机械的衍生故障分析具有普适性意义.提出了一种基于1D-CNN(one-dimensional convolution neural network)联合特征提取的轴承健康监测与故障诊断算法.算法首先对轴承原始振动信号进行分区裁剪,裁剪获得的信号分区作为特征学习空间并行输入1D-CNN中,以提取各工况下的代表性特征域.为了避免对故障重叠信息的处理,优先使用对健康状态敏感的特征域构建轴承健康状态判别模型,若健康状态判别模型识别轴承未处于健康状态,特征域将与原始信号联合重构,通过耦合自动编码器开展故障模式判定.使用凯斯西储大学(Case Western Reserve University)的轴承数据开展实验,结果表明,该算法继承了深层学习模型的准确性和鲁棒性,具有较高的故障诊断精度和较低的诊断时延.

    Abstract:

    Data-driven fault diagnosis models for specific mechanical equipment lack generalization capabilities. As a core component of various types of machinery, the health status of bearings makes sense in analyzing derivative failures of different machinery. This study proposes a bearing health monitoring and fault diagnosis algorithm based on 1D-CNN (one-dimensional convolution neural network) joint feature extraction. The algorithm first partitions the original vibration signal of the bearing in segmentations. The signal segmentations are used as feature learning spaces and input into the 1D-CNN in parallel to extract the representative feature domain under each working condition. To avoid processing overlapping information generated by faults, a bearing health status discriminant model is built in advance based on the feature domain sensitive to health status. If the health model recognizes that the bearing is not in a healthy state, the feature domain will be reconstructed jointly with the original signal and coupled with an automatic encoder for failure mode classification. Bearing data provided by Case Western Reserve University are used to carry out experiments. Experimental results demonstrate that the proposed algorithm inherits the accuracy and robustness of the deep learning model, and has higher diagnosis accuracy and lower time delay.

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刘立,朱健成,韩光洁,毕远国.基于1D-CNN联合特征提取的轴承健康监测与故障诊断.软件学报,2021,32(8):2379-2390

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历史
  • 收稿日期:2020-07-20
  • 最后修改日期:2020-09-07
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  • 在线发布日期: 2021-02-07
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