Bearing is a widely used rotating component in most of the industrial machinery. Failure of bearings can incur substantial losses in the industries. During operation, to prohibit failure in bearing, it becomes necessary to identify faults that occur in bearings. In the present work, bearing vibration signals have been taken for the detection of faults in bearings. In the next step, features obtained from various signal processing techniques such as ensemble empirical mode decomposition (EEMD), walsh hadamard transform (WHT) and discrete wavelet transform (DWT) have been used to detect bearing faults (inner race defect, outer race defect, and ball defects). To select the mother wavelet, the maximum energy to entropy ration criteria has been used. Mutual Information feature ranking algorithm is used to select the relevant features. Machine learning techniques such as Random Forest, Support Vector Machine, Artificial Neural Network, and IBK are used. Training and tenfold cross-validation procedures applied to all ranked features. Results reveal that random forest gives 100 % training accuracy with one ranked feature and 98.43 % ten-fold cross-validation accuracy with seven features. From the results, it is observed that the proposed methodology can be reliable and it may serve as an effective tool for fault diagnosis of bearing.

中文翻译：

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利用多融合信号处理技术和互信息诊断轴承故障

轴承是大多数工业机械中广泛使用的旋转组件。轴承故障会在行业中造成重大损失。在运行过程中，为防止轴承出现故障，有必要识别轴承中发生的故障。在目前的工作中，已采用轴承振动信号来检测轴承中的故障。下一步，从各种信号处理技术中获得的特征（如集成经验模式分解（EEMD），沃尔什哈达玛德变换（WHT）和离散小波变换（DWT））已用于检测轴承故障（内圈缺陷，外圈缺陷）以及焊球缺陷）。为了选择母子波，已经使用了最大能量熵比标准。互信息特征排序算法用于选择相关特征。使用了诸如随机森林，支持向量机，人工神经网络和IBK之类的机器学习技术。训练和十倍交叉验证程序应用于所有已排序的功能。结果表明，随机森林具有一种排名特征，可提供100％的训练准确性，而具有七个特征则具有98.43％的十倍交叉验证准确性。从结果可以看出，所提出的方法是可靠的，并且可以作为轴承故障诊断的有效工具。