深度卷积长短期记忆网络的轴承故障诊断

Bearing Fault Diagnosis Using Deep CNN and LSTM

针对传统数据驱动故障诊断方法难以从轴承信号中自适应提取有效特征、没有充分利用故障数据的时序特点以及缺乏自适应处理动态信息能力的问题,提出了一种深度卷积神经网络与长短期记忆网络相结合的智能故障诊断方法。本文方法构建的深度模型能够从轴承原始信号中自适应地提取鲁棒性特征,然后利用长短期记忆网络学习特征中的时间依赖关系实现了高准确度的轴承故障诊断。该方法克服了传统特征提取方法依赖专家经验和信息利用不完全等问题,实现了故障的智能、准确诊断。实验结果表明,该方法可以提取更准确的特征而且由于利用了故障演变过程中的时序信息,使得故障诊断更加智能、可靠。

There exists some problems in traditional data-driven fault diagnosis methods,for example,it is difficult to adaptively extract effective features from bearing signals,cannot make full use of the timing characteristics of fault data,and lacks of the ability to adaptively process dynamic information.An intelligent bearing fault diagnosis method combined deep convolutional neural network and long-term and short-term memory network is proposed.This method constructed a kind of deep networks and can adaptively extract the robust features from the original bearing signals,and then utilized the long short-term memory network to learn the time-dependent relationship in these features,and achieved high-accuracy bearing fault diagnosis.The proposed method overcame the problems existed in the traditional feature extraction methods,such as heavy dependence on expert experience and incomplete utilization for time series information,and realized intelligent and accurate diagnosis of faults.The experimental results show that the proposed method can extract more accurate features and make the fault diagnosis more intelligent and reliable by utilizing the timing information in the process of fault degradation.