基于卷积神经网络的轴承故障识别与分类

M. Bhadane, K. I. Ramachandran
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引用次数: 19

摘要

状态监测(CBM)是一种广泛应用于故障诊断的方法,它为任何设备或元件的安全、正常运行提供分析。振动分析是CBM最准确、最可靠的揭示装置或元件状态的技术。该方法可以通过分析加速度计的振动数据进行故障诊断。卷积神经网络(CNN)已成为模式识别和声学数据分析中应用最广泛的方法之一。本文将CNN作为轴承故障检测的后端分类器。采集轴承正常状态、内圈故障和外圈故障三种不同状态下的振动数据。从振动数据中提取统计特征作为CNN分类器的输入。卷积滤波器是通过训练CNN来学习的,用于检测每个轴承条件的独特特征。结果表明,CNN是一种可靠有效的轴承故障诊断技术。与同类算法相比,它具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bearing fault identification and classification with convolutional neural network
Condition-based monitoring (CBM) is widely used methodology for the fault diagnosis, which provides the analysis for the safe and proper operations of any device or element. Vibration analysis is most accurate and reliable technique of CBM to reveal the condition of device or element. In this technique, fault can be diagnosed by analysing the vibration data acquired from accelerometer. Convolutional Neural Network (CNN) has emerged as one of the most widely used methodology in application of pattern recognition and acoustic data analysis. In this paper, CNN is used as back-end classifier for bearing fault detection. Vibration data is collected for three different conditions of bearings i.e. normal condition, inner race fault and outer race fault. Statistical features are extracted from vibration data and used as input to CNN classifier. Convolution filters are learned by training CNN and are used to detect the unique features for each condition of bearing. The obtained accuracy shows that CNN is very reliable and effective technique for bearing fault diagnosis. It exhibits good performance compared to peer algorithms.
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