基于深度学习的机械轴承故障智能诊断

智能故障诊断对于提高智能制造的可靠性具有重要意义。基于深度学习的故障诊断方法在工业领域已经取得了很大的成功，但是不同的模型提取的特征存在一定的差异。针对数据特征提取不全面等问题，提出一种基于深度学习的融合网络模型(CLOD)。首先通过傅里叶变换对故障信号进行时频分析，得到时频谱样本，然后将样本送入经过LSTM模型和改进的CNN模型融合后的卷积网络模型(CLOD)中训练学习，最后通过更新网络参数来提高模型性能，实现轴承故障精确智能诊断。与传统方法比较，CLOD在保证准确率的基础上，极大的增加了模型的拟合速度和稳定性。

Intelligent Diagnosis of Mechanical Bearing Faults based on Deep Learning

Intelligent fault diagnosis is important for improving the reliability of smart manufacturing. Fault diagnosis methods based on deep learning have been very successful in industry, but there are some differences in the features extracted by different models. A fusion network model (CLOD) based on deep learning is proposed for problems such as incomplete data feature extraction. Firstly, a time-frequency analysis of the fault signal is carried out by Fourier transform to obtain a time-frequency spectrum sample, then the sample is fed into a convolutional network model (CLOD) trained and learned after the fusion of LSTM model and improved CNN model, and finally the model performance is improved by updating the network parameters to achieve accurate and intelligent diagnosis of bearing faults. Compared with the traditional method, CLOD greatly increases the fitting speed and stability of the model on the basis of guaranteed accuracy.