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An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

机译：使用多元统计和模式识别来自动检测和识别滚珠轴承损坏的程序

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摘要

This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern recognition (PR) procedure used to recognise between signals coming from healthy bearings and those generated from different bearing faults. Four categories of signals are considered, namely no fault signals (from a healthy bearing), inner race fault, outer race fault and rolling element fault signals. The PR procedure uses the first six principal components extracted from the signals after a proper principal component analysis (PCA). In this work a modified PCA is suggested, which is much more appropriate for categorical data. The combination of the modified PCA and the PR method ensures that the fault is automatically detected and classified to one of the considered fault categories. The method suggested does not require the knowledge/determination of the specific fault frequencies and/or any expert analysis: once the signal filtering is done and the PC's are found the PR method automatically gives the answer if there is a fault present and its type.
机译：本文提出了一种用于滚动轴承故障检测和分类的自动方法，该方法基于模式识别和对测得的振动信号进行主成分分析。使用小波变换对记录的信号进行预处理，以提取出球轴承故障检测所需的适当的高频（详细）区域。接下来是模式识别（PR）程序，该程序用于识别来自健康轴承的信号与来自不同轴承故障的信号。考虑了四种信号，即无故障信号（来自健康轴承），内圈故障，外圈故障和滚动元件故障信号。 PR过程使用经过适当主成分分析（PCA）后从信号中提取的前六个主成分。在这项工作中，提出了修改后的PCA，它更适合于分类数据。修改后的PCA和PR方法的组合可确保自动检测故障并将其分类为考虑的故障类别之一。建议的方法不需要了解/确定特定故障频率和/或进行任何专家分析：一旦完成信号过滤并找到PC，PR方法就会自动给出答案（如果存在故障及其类型）。

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