基于抗噪多核卷积神经网络的轴承故障诊断方法

针对噪声环境下滚动轴承故障难以诊断的问题，提出一种基于抗噪多核卷积神经网络（anti-noise multi-core convolutional neural network，AMCNN）的轴承故障识别新方法。首先，对滚动轴承振动信号进行预处理，得到数据样本，分为训练集和测试集；然后建立轴承寿命状态识别模型，将标签化的训练集数据样本输入AMCNN中进行训练；最后，将训练后的AMCNN模型应用于测试集，输出故障识别结果。在训练过程中，为抑制过拟合，对原始训练样本进行加噪处理；为提高模型抗干扰能力，将dropout层作为AMCNN的第一层。运用轴承实验数据对识别模型进行检验，通过对比验证，结果表明所提出的识别方法在高噪声环境下能更准确地实现轴承故障状态识别。

Bearing fault diagnosis based on an anti-noise multi-core convolutional neural network

In view of the difficulty in diagnosing rolling bearing faults in a noisy environment, a new method for bearing fault identification based on an anti-noise multi-core convolutional neural network (AMCNN) is proposed. First, the rolling bearing vibration signal is preprocessed to obtain data samples, which are divided into a training set and a test set. Then, the bearing life state recognition model is established, and the tagged training set data samples are input into the AMCNN for training. Finally, the trained AMCNN model is applied to the test set to output the fault identification result. In the training process, in order to suppress over-fitting, the original training samples are subjected to noise-adding processing. In order to improve the anti-jamming capability of the model, the dropout layer is used as the first layer of the AMCNN. At last, the bearing test data is used to test the identification model. Comparison with conventional methods shows that our method can more accurately idenfity a bearing fault in a high noisy environment.