基于卷积神经网络的层级化智能故障诊断算法

传统智能故障诊断算法需要依赖人工特征提取和专家知识,而旋转机械设备复杂的工作环境和工况使得传统算法在实际应用中缺乏良好的自适应性和泛化性.针对以上问题,提出基于卷积神经网络(Convolutional neural network,CNN)的层级化故障诊断算法(CNN based hierarchical fault diagnosis,CNN-HFD).首先,将原始振动信号进行分段预处理,以实现数据扩容;然后,分别根据故障类型和故障程度设计多个卷积神经网络,并将原始振动数据以某一时间步进行分割,作为卷积神经网络的输入进行训练;最后,将待识别信号送入CNN-HFD模型,经过分层故障诊断,在末端卷积神经网络输出相应故障类别和程度.通过滚动轴承振动数据库的实验表明,所提出的算法不仅具有高达99.5%以上的故障识别率,而且在负载发生变化时依然可以保持高达97%以上的故障识别率,具有较好的鲁棒性和泛化性能.

A hierarchical intelligent fault diagnosis algorithm based on convolutional neural network

Traditional intelligent fault diagnosis methods largely depend on manual feature extraction and expert knowledge. However, complex working conditions of rotatory machinery make traditional fault diagnosis lack adaptivity and generalization. Aiming to solve the problems mentioned above, a novel CNN-based hierarchical fault diagnosis algorithm called CNN-HFD is proposed. Firstly, raw temporal vibration signals are segmented to enlarge samples. Then, several simple CNN networks are constructed according to fault categories and severities. Training samples divided by a certain time-step are sent to the CNN. Finally, signals to be identified are utilized as the input of the CNN-HFD. After hierarchical analysis, the fault category and fault severity are output at the end of the model. Experiments on rolling bearing datasets demonstrate that the proposed method can not only achieve 99.5% fault recognition, but also keep a 97% accuracy under variable loads, which verifies its good robustness and generalization.