作者:柳秀，马善涛，谢怡宁，何勇军
Authors:LIU Xiu,MA Shan-tao,XIE Yi-ning,HE Yong-jun摘要:摘要:近年来,深度学习技术在基于振动信号的轴承故障诊断中表现出了巨大的潜力。然而，在基于深度学习的故障诊断方法中，传统单一的网络拓扑结构特征提取的区分性弱和噪声鲁棒性低，故障诊断的准确率不高。此外，目前大多数的研究方法在变负载环境下故障识别率低。针对以上问题，提出了一种改进的神经网络端到端故障诊断模型。该模型将卷积神经网络（convolutional neural networks, CNN）和基于注意力机制的长短期记忆网络(the attention long short-term memory, ALSTM)相结合，借助ALSTM捕捉时间序列数据中的远距离相关性，有效抑制输入信号中的高频噪声。同时，引入多尺度和注意力机制，拓宽卷积核捕捉高低频特征的范围，突出故障的关键特征。经多个数据集测试，并且与现有方法进行比较，实验表明，该方法在准确率、噪声鲁棒性及变负载下的故障识别率方面表现突出。
Abstract:Abstract:In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. After testing on multiple data sets, and comparing with existing methods, experiments show that the method in this paper has significant performance in accuracy, noise robustness, and fault recognition rate under variable load conditions.

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