摘要
在基于数据驱动的机械系统故障诊断领域中,关于特征提取和诊断模型已经取得了一定的进展。但在整套诊断进程中,失效信号的缺失一直是诊断中不可避免的问题。结合在图像处理领域卓有成效的GAN模型,文中以生成信号和真实信号间的概率分布对比作为部分超参数的选择依据,以轴承正常阶段信号作为模型的输入信号,以轴承仿真信号作为生成的目标,同时结合正常阶段的机械特性和仿真信号的故障特性进行失效信号的生成。通过概分布、包络谱、峭度及裕度特征的全寿命拟合曲线等方法验证了生成的信号比仿真信号更接近真实失效信号。
In the field of data-driven mechanical system fault diagnosing,there have been many advances in feature extraction and diagnosis models.But in the whole set of diagnostic processes,the lack of failure signals has always been an inevitable problem in diagnosis.Therefore,combined with the GAN model which was effective in the field of image processing,the probability distribution between the generated signal and the real signal were taken as the selection basis of the partial hyperparameter,and the normal phase signal of the bearing was taken as the input of the model.The bearing simulation signal was used as the target of generation,and the failure signal was generated in combination with the mechanical characteristics of the normal stage and the fault characteristics of the simulation signal.Finally,the proposed signal was proved closer to the true failure signal than the simulated signal by means of the general distribution,envelope spectrum,kurtosis and full-life fitting curve of the margin feature.
作者
佟博
刘韬
刘畅
TONG Bo;LIU Tao;LIU Chang(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650000,China)
出处
《电子科技》
2020年第4期28-34,共7页
Electronic Science and Technology
基金
国家自然科学基金(51675251)
云南省应用基础研究计划项目重点项目(201601PE00008).
关键词
生成对抗网络
轴承
故障诊断
失效信号
生成信号
生成模型
generative adversarial networks
bearing
fault diagnosis
failure signal
generating signal
generating model
作者简介
佟博(1992-),硕士研究生。研究方向:机械设备状态监测与故障诊断技术;刘韬(1980-),博士,副教授。研究方向:现代信号处理理论与方法在故障特征提取中的应用,基于机器学习方法的智能诊断、设备性能评估和寿命预测。
