基于CNN-BiLSTM的滚动轴承变工况故障诊断方法

Fault Diagnosis Method of Rolling Bearing Based on CNN-BiLSTM Under Variable Working Conditions

针对变工况和噪声背景下滚动轴承故障难以诊断的问题,提出一种将注意力机制、DropConnect和Dropout混合加入到卷积神经网络双向长短时记忆网络(convolutional neural network-bidirectional long short-term memory,简称CNN-BiLSTM)模型的滚动轴承故障诊断方法。首先,将滚动轴承不同工况下的各类故障状态的原始振动信号进行预处理,构建标签化的训练数据集和测试数据集;其次,把注意力机制引入到BiLSTM中提取更加重要的深层故障特征,同时将DropConnect和Dropout混合使用分别抑制CNN和BiLSTM网络在训练过程中的过拟合问题,从而得到改进的CNN-BiLSTM模型;然后,将处理后的标签化数据集输入改进的模型中训练;最后,利用Softmax分类器进行故障类别诊断。通过选取不同数据集验证,证明该方法均有较好的泛化性和抗噪能力。

Aiming at the difficulty of rolling bearing fault diagnosis under variable working conditions and noise background,a rolling bearing fault diagnosis method is proposed,which combines the attention mechanism,DropConnect and Dropout into the convolutional neural network-bidirectional long short-term memory network(CNN-BiLSTM)model.Firstly,the original vibration signals of various fault states of rolling bearings under different working conditions are preprocessed,and the labeled training data set and test data set are constructed.Secondly,the attention mechanism is introduced into BiLSTM to extract more important deep fault features.At the same time,DropConnect and Dropout are mixed to suppress the over fitting problem of CNN network and BiLSTM network in the training process,respectively,so as to obtain an improved CNN-BILstM model.Thirdly,the processed labeled data set is input into the improved model for training.Finally,Softmax classifier is used for fault diagnosis.By selecting different data sets,the method is proved to have good generalization and anti-noise capability.