基于一维卷积神经网络的旋转机械故障诊断与识别

IF 0.7 Q4 ENGINEERING, MECHANICAL
Feifei Yu, Guoyan Chen, Canyi Du, Liwu Liu, Xiaoting Xing, Xiaoqing Yang
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引用次数: 0

摘要

本文以微型台钻和汽车发动机这两种旋转机械为研究对象。传统的机器学习存在需要人工提取特征,对专家诊断经验和专业知识的依赖性很强,同时也存在准确性低、时效性低、效率低等缺点。针对传统旋转机械故障诊断方法多基于传统机器学习模型的问题,本文提出了一种基于一维卷积神经网络的故障识别方法。根据微型台钻和汽车发动机不可拆卸的特点,分别设置了相应的故障,利用信号采集器获取姿态传感器的振动信号,并对采集到的数据进行预处理,然后建立 CNN 模型进行故障识别,并不断优化网络结构,得到精度高(达 100%)、鲁棒性强的最优网络模型。结果表明,当原始信号作为输入信号时,一维卷积神经网络模型与传统的机器学习 SVM 模型相比,提高了故障识别精度,降低了成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network
The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal.
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来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
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