Guo Huijuan, Ping Dongzhi, Wang Lijun, Zhang Weijie, Wu Junfeng, Ma Xiao, Xu Qiang, Lu Zhongyu
Department of Engineering, Huanghe Science and Technology University, Zhengzhou 450045, China.
School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
Sensors (Basel). 2025 Apr 4;25(7):2286. doi: 10.3390/s25072286.
The vibration signal of mechanical equipment in operating environments is the key to describing fault characteristics, but due to thez influence of equipment density and environmental interference, the accuracy of fault diagnosis is often affected by noise. In this paper, a fault diagnosis method based on a 1D Multi-Channel Improved Convolutional Neural Network (1DMCICNN) is proposed. By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. The model is verified through experiments under different signal-to-noise ratios and loads. The results show that the accuracy of 1DMCICNN is 98.67%, 99.71%, 99.04%, and 99.71% on different load and speed datasets. Meanwhile, compared with the unoptimized two-channel Convolutional Neural Network, the training parameters are reduced by 55.58%.
机械设备在运行环境中的振动信号是描述故障特征的关键，但由于设备密度和环境干扰的影响，故障诊断的准确性常常受到噪声的影响。本文提出了一种基于一维多通道改进卷积神经网络（1DMCICNN）的故障诊断方法。通过引入双向长短期记忆网络（BiLSTM）、注意力机制和双通道卷积神经网络的局部稀疏结构，在不同尺度上充分提取含噪定时信号的特征信息，同时减少计算参数。该模型通过在不同信噪比和负载下的实验进行了验证。结果表明，1DMCICNN在不同负载和速度数据集上的准确率分别为98.67%、99.71%、99.04%和99.71%。同时，与未优化的双通道卷积神经网络相比，训练参数减少了55.58%。
