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引用次数: 4

摘要

提出了一种基于小波变换和主成分分析的滚珠轴承故障诊断方法。对采集到的信号进行小波变换预处理,将信号分解为低(近似)和高(详细)频率部分,其中高频部分用于故障诊断。然后从不同轴承故障信号和健康轴承信号的高频部分提取11个潜在的统计特征。提出了四种信号类型,即外圈故障、内圈故障、球故障和无故障信号。PCA用于对统计提取的多维数据进行线性变换和降维,以便进行更直接的分析。保留95%以上显著性水平的六个主成分用于轴承故障检测和分类。该方法将小波变换、统计特征提取和主成分分析相结合,在不了解轴承故障频率和经验丰富的用户分析的情况下,成功地检测和分类了故障类型。提出了一种基于小波变换和主成分分析的滚珠轴承故障诊断方法。对采集到的信号进行小波变换预处理,将信号分解为低(近似)和高(详细)频率部分,其中高频部分用于故障诊断。然后从不同轴承故障信号和健康轴承信号的高频部分提取11个潜在的统计特征。提出了四种信号类型,即外圈故障、内圈故障、球故障和无故障信号。PCA用于对统计提取的多维数据进行线性变换和降维,以便进行更直接的分析。保留95%以上显著性水平的六个主成分用于轴承故障检测和分类。通过结合小波变换,实现了统计滤波。
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Ball bearing fault diagnosis using wavelet transform and principal component analysis
This study proposes a new method for fault diagnosis in ball bearings based on wavelet transform and principal component analysis (PCA) of the acquired vibration signals. The signals collected are pre-processed using a wavelet transform to decompose the signals into low (approximated) and high (detailed) frequency part where the high-frequency part are needed for fault diagnosis purposes. Eleven potential statistical features are then extracted from the high-frequency part coming from different bearing fault signals and those from healthy bearings as well. Four types of signals are proposed, they are outer race fault, inner race fault, ball fault and no-fault signals. The PCA is used to linearly transform and reduce multidimensional data resulted from statistical extraction down to a few dimensions for more straightforward analysis. Six principal components retaining more than 95% significance level are used for bearing fault detection and classification. By combining the wavelet transform, statistical features extraction and PCA, the proposed method successfully detected and classified fault types without knowledge of a bearing fault frequencies and analysis from experienced users.This study proposes a new method for fault diagnosis in ball bearings based on wavelet transform and principal component analysis (PCA) of the acquired vibration signals. The signals collected are pre-processed using a wavelet transform to decompose the signals into low (approximated) and high (detailed) frequency part where the high-frequency part are needed for fault diagnosis purposes. Eleven potential statistical features are then extracted from the high-frequency part coming from different bearing fault signals and those from healthy bearings as well. Four types of signals are proposed, they are outer race fault, inner race fault, ball fault and no-fault signals. The PCA is used to linearly transform and reduce multidimensional data resulted from statistical extraction down to a few dimensions for more straightforward analysis. Six principal components retaining more than 95% significance level are used for bearing fault detection and classification. By combining the wavelet transform, statistical fe...
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