基于多维卷积和注意力机制的轴承故障检测。

IF 2.6 4区 工程技术 Q1 Mathematics
Yingying Xu, Chunhe Song, Chu Wang
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引用次数: 0

摘要

轴承是工业设备的关键部件,对工业物理系统的安全有重大影响。轴承故障可能导致设备停机并引发事故,对生产安全构成重大风险。然而,在实践中很难获得大量的轴承故障数据,这使得样本量小的问题成为轴承故障检测的一大挑战。此外,一些方法可能会忽略轴承振动信号中的重要特征,导致检测能力不足。针对轴承故障检测面临的挑战,本文提出了几种基于多维卷积和注意力机制的样本学习方法。首先,设计了一种多通道预处理方法,以更有效地利用轴承振动信号中的信息。其次,通过多维卷积运算和关注机制提取多维特征并加强对重要特征的关注,提高了网络的特征提取能力。此外,将特征向量非线性映射到度量空间计算距离,可以更好地衡量样本之间的相似性,从而提高轴承故障检测的准确性,为工业系统的安全运行提供重要保障。大量实验表明,所提出的方法在小样本条件下具有良好的故障检测性能,有利于减少机器停机时间和经济损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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