Rolling bearing feature extraction and fault identification techniques using deep learning algorithms have been widely adopted in recent years. We proposed a method for diagnosing composite faults in rolling bearings by employing multisensor decision fusion and convolutional neural networks. Different types of bearing faults and eccentricity faults have different fault eigenfrequencies in vibration signals. In the proposed method, vibration and acoustic signals are collected, their characteristics are analyzed, and multisensor data fusion processing is conducted. A neural network is then used to identify the signals containing bearing fault characteristics to diagnose bearing faults at different rotational speeds. We demonstrated the effectiveness of the proposed method by conducting comparative experiments on existing methods.

近年来，基于深度学习算法的滚动轴承特征提取和故障识别技术已被广泛采用。我们提出了一种利用多传感器决策融合和卷积神经网络诊断滚动轴承复合故障的方法。不同类型的轴承故障和偏心故障在振动信号中具有不同的故障特征频率。该方法收集振动和声学信号，分析其特征，并进行多传感器数据融合处理。然后使用神经网络识别包含轴承故障特征的信号，以诊断不同转速下的轴承故障。我们通过对现有方法进行对比实验证明了该方法的有效性。