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作 者:焦华超[1] 孙文磊[1] 王宏伟[1] JIAO Huachao;SUN Wenlei;WANG Hongwei(Intelligent Manufacturing Modern Industrial College,Xinjiang University,Urumqi,830017)
机构地区:[1]新疆大学智能制造现代产业学院,乌鲁木齐830017
出 处:《中国机械工程》2025年第3期546-557,共12页China Mechanical Engineering
基 金:新疆维吾尔自治区重点研发计划(2022B02016)。
摘 要:利用数据生成方法生成时域特征和频域特征与轴承故障真实信号一致的高质量数据,构建平衡数据集,对数据不平衡情况下建立高效的轴承故障诊断模型具有重要意义。针对现有数据生成方法仅关注时域或频域单一特征的局限,提出了类小波辅助分类生成对抗网络。基于小波变换原理,使用多层神经网络构建类小波变换(WLT)网络,模拟小波变换及逆变换,建立时域与频域信号的映射关系;将WLT网络嵌入辅助分类生成对抗网络(ACGAN)模型中,作为模型生成器的主体;构建两个不同功能的判别器,使得改进的ACGAN在一次训练中能同时学到真实轴承振动信号的时域和频域特征信息。试验结果表明,WLT-ACGAN模型生成的轴承振动信号具有与真实轴承振动信号一致的时域特征和频域特征,数据不平衡时,利用生成信号扩增的平衡数据集构建的故障诊断模型具有较高的准确率。Using data generation method to generate high-quality data which made time-domain and frequency-domain features consistent with the real signals of bearing faults,and constructing balanced dataset,were of great significance for the establishment of an efficient diagnostic model of bearing faults in the case of data imbalance.In order to address the limitations of the existing data generation methods,which focused on a single feature in time or frequency domains,WLT-ACGAN was proposed herein.Firstly,a WLT network was constructed with a multi-layer neural network based on the principle of wavelet transform.The wavelet transform and inverse transform were simulated,and the mapping relationship between time-domain signal and frequency-domain signal was established.Secondly,the WLT network was embedded into ACGAN model as the primary component of model generator.Finally,two discriminators were constructed with different functions,enabling the improved ACGAN to learn time-domain and frequency-domain feature information of authentic bearing vibration signals concurrently.Experimental results show that the bearing vibration signals generated by WLT-ACGAN model exhibit consistent time-domain and frequency-domain features with those of the actual bearing vibration signals.Furthermore,the fault diagnostic model constructed with the balanced dataset augmented by the generated signals exhibits a high degree of accuracy when the data are imbalanced.
关 键 词:辅助分类生成对抗网络 类小波变换 轴承故障诊断 数据生成
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