首页|基于AVMD-ASWT-PCNN的滚动轴承故障识别方法

基于AVMD-ASWT-PCNN的滚动轴承故障识别方法

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针对传统方法直接舍弃高频分量导致信号降噪不充分和信号在时、频域表征效果不好的问题,提出一种基于自适应变分模态分解融合自适应同步压缩小波变换(AVMD-ASWT)的少噪声时频图像生成方法,在此基础上结合动态惯性权重粒子群优化卷积神经网络(PCNN)实现滚动轴承故障的识别.采用AVMD-ASWT算法对轴承振动信号进行二次处理,同时引入互信息熵-相关系数准则,获得高分辨率的少噪声时频图像.将少噪声时频图像作为网络模型的输入进行故障识别,同时采用动态惯性权重粒子群优化算法(PSO)对卷积神经网络模型(CNN)参数进行优化,可解决模型结构难以确定的问题,模型识别正确率和识别速度均有明显提升.工程实例表明:运用AVMD-ASWT方法得到的时频图像具有更高的分辨率,显著降低了信号中噪声的影响,且提出的PCNN模型的故障识别正确率达99%以上.
Rolling bearing fault identification method based on AVMD-ASWT-PCNN
Aiming at the problems of inadequate noise reduction caused by the direct discarding of the high frequency component of the signals using with the traditional method and poor signal characterization in the time and frequency domains,a noise less time-frequency image generation method based on Adaptive Variational Modal Decomposition fused with Adaptive Synchrosqueezing Wavelet Transform(AVMD-ASWT)is proposed,based on which a Convolutional Neural Network combined with Particle Swarm Optimization is used to realize the identification of rolling bearing faults.The AVMD-ASWT algorithm is used for processing of bearing vibration signals,and the mutual information entropy-correlation coefficient criterion is also introduced,which can obtain high-resolution time-frequency images with less noise.The less noisy time-frequency image is used as the input of the network model for fault identification,while the dynamic inertia weight particle swarm optimization algorithm(PSO)is used to optimize the parameters of the convolutional neural network model(CNN),which can solve the problem of difficult to determine the structure of the model,and the correct rate of the model identification and the identification speed have been significantly improved.Engineering examples show that the time-frequency image obtained by using the AVMD-ASWT method has higher resolution,significantly reduces the influence of noise in the signal,furthermore the correct rate of bearing fault identification has reached more than 99%.

rolling bearingadaptive variational mode decompositionadaptive synchrosqueezing wavelet transformconvolutional neural networkfault identification

刘志卫、邱明、李军星、刘静涛、高锐

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河南科技大学 机电工程学院,河南 洛阳 471003

机械装备先进制造河南省协同创新中心,河南 洛阳 471003

自适应变分模态分解 自适应同步压缩小波变换 卷积神经网络 故障识别

国家重点研发计划项目国家自然科学基金项目河南省高校青年骨干教师培养计划项目河南省青年托举人才项目

2020YFB2007303520051592021GGJS0482023HYTP050

2024

10.11809/bqzbgcxb2024.09.031

兵器装备工程学报
重庆市（四川省）兵工学会 重庆理工大学

兵器装备工程学报

CSTPCD北大核心
影响因子：0.478
ISSN：2096-2304
年,卷(期)：2024.45(9)
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