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Application of vibration analysis and data mining techniques for bearing fault diagnosis in two stroke IC engine gearbox

机译：振动分析和数据挖掘技术在两个中风IC发动机齿轮箱中轴承故障诊断的应用

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This paper is about monitoring of ball bearing used in the IC engine gearbox using condition monitoring techniques. Experiments are conducted on two stroke IC engine which is driven by the 3HP DC motor. Vibration signals are acquired from the gearbox with triaxial accelerometer. Ball bearing with good and induced faulty (outer race fault, inner race fault, ball fault, inner and outer race fault) conditions were used in the analysis. Fault diagnosis of the ball bearing has been carried out using data mining (DM) techniques. In DM there are three stages viz.; feature extraction, feature selection and feature classification. For all the conditions of bearing, statistical and empirical mode decomposition (EMD) features are extracted from the vibration signals. Decision tree technique (J48 algorithm) is used in the analysis for selecting significant features from the feature vector. From the chosen features, ball-bearing conditions are classified using random forest algorithm. Results obtained from the different classifiers were compared, and a better classification algorithm with a decision tree will be suggested for condition monitoring of the rotating components.
机译：本文采用条件监测技术监测IC发动机齿轮箱中使用的滚珠轴承。实验在两个中风IC发动机上进行，该发动机由3HP直流电动机驱动。从带有三轴加速度计的齿轮箱获取振动信号。在分析中使用了具有良好和诱导故障（外部竞争故障，内部竞争故障，滚珠壁故障）条件的滚珠轴承在分析中使用。使用数据挖掘（DM）技术进行了滚珠轴承的故障诊断。在DM中，有三个阶段viz。功能提取，功能选择和特征分类。对于轴承的所有条件，统计和经验模式分解（EMD）特征是从振动信号中提取的。决策树技术（J48算法）用于从特征向量中选择有效特征的分析中。根据所选择的特征，使用随机森林算法进行滚珠轴承条件。比较了从不同分类器获得的结果，并且将建议具有决策树的更好的分类算法进行旋转部件的条件监测。

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