Early detection of bearing defects may prevent the occurrence of catastrophic failures of the whole associated system. Condition monitoring strategies such as vibration and acoustic signal analyses are employed for incipient fault diagnosis of bearings. The current investigation attempts to compare the fault diagnostic capabilities in terms of their effectiveness in early detection of local bearing defects. Experiments are performed on a three-stage gearbox under constant and fluctuating operating conditions of speed. Wavelet coefficients are achieved from the acquired raw signals by discrete wavelet transform and various statistical features are obtained. Most contributing features among them are chosen by decision tree. Further, the extracted features are classified based on their fault severity levels using support vector machine algorithm. The experimental investigation revealed that vibration signal analysis outperformed the acoustic signal analysis under the experimental operating conditions.

中文翻译：

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变速作用下多级齿轮箱轴承故障严重程度分析

轴承缺陷的早期检测可以防止整个相关系统发生灾难性故障。振动和声学信号分析等状态监测策略用于轴承的早期故障诊断。当前的调查试图比较故障诊断能力在早期检测局部轴承缺陷方面的有效性。实验是在一个三级齿轮箱上在恒定和波动的速度运行条件下进行的。通过离散小波变换从获取的原始信号中获得小波系数，并获得各种统计特征。其中最有贡献的特征是由决策树选择的。此外，使用支持向量机算法根据故障严重程度对提取的特征进行分类。