基于改进一维卷积和双向长短期记忆神经网络的故障诊断方法

针对工业领域中故障诊断数据存在时序性和夹杂强噪声的特点导致的收敛速度慢以及诊断精度低的问题，提出了一种基于改进一维卷积和双向长短期记忆（1DCNN-BiLSTM）神经网络融合的故障诊断方法。该方法包括故障振动信号的预处理、特征的自动提取以及振动信号的分类。首先，采用自适应白噪声的完整经验模态分解（CEEMDAN）技术对原始振动信号进行预处理；其次，构建1DCNN-BiLSTM双通道模型，将处理后信号输入双向长短期记忆（BiLSTM）神经网络模型和一维卷积神经网络（1DCNN）模型两个通道，从而对信号的时序相关性特征、局部空间的非相关性特征和弱周期性规律进行充分提取；然后，针对信号夹杂强噪声的问题，对压缩与激励网络（SENet）模块进行改进并将其作用于两个不同的通道；最后，输入全连接层将双通道提取的特征进行融合并借助Softmax分类器实现对设备故障的精确识别。使用凯斯西储大学轴承数据集进行实验，结果表明改进后的SENet模块同时作用于1DCNN通道和stacked BiLSTM通道，1DCNN-BiLSTM双通道模型在保证快速收敛的情况下有最高诊断精度96.87%，优于传统单通道模型，有效提高了机械设备故障诊断效率。

Fault diagnosis method based on improved one-dimensional convolutional and bidirectional long short-term memory neural networks

Aiming at the problems of the slow model convergence and low diagnosis accuracy due to the time-series fault diagnosis data with strong noise in the industrial field， an improved one-Dimensional Convolutional and Bidirectional Long Short-Term Memory（1DCNN-BiLSTM） Neural Network fault diagnosis method was proposed. The method includes preprocessing of fault vibration signals， automatic feature extraction and vibration signal classification. Firstly， Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） technology was used to preprocess the original vibration signal. Secondly， the 1DCNN-BiLSTM dual channel model was constructed， and the processed signal was input into the Bidirectional Long Short-Term Memory （BiLSTM） model channel and the One-dimensional Convolution Neural Network （1DCNN） model channel to fully extract the timing correlation characteristics， the non-correlation characteristics of the local space and the weak periodic laws of the signal. Thirdly， in response to the problem of strong noise in the signal， the Squeeze and Excitation Network （SENet） module was improved and applied to the two different channels. Finally， the features extracted from the two channels were fused by putting them into the fully connected layer， and the accurate identification of equipment faults was realized by the help of the Softmax classifier. The bearing dataset of Case Western Reserve University was used for experimental comparison and verification. The results show that after applying the improved SENet module to the 1DCNN channel and the stacked BiLSTM channel at the same time， the 1DCNN-BiLSTM dual channel model performs the highest diagnosis accuracy 96.87% with fast convergence， which is better than traditional one-channel models， thereby effectively improving the efficiency of equipment fault diagnosis.