摘要
目的轴承作为印刷设备中的旋转核心元件,其运行状态对印刷设备的健康监测作用较大。通过融合小波时频处理与Inception v3模型的优势,提出一种用于印刷设备轴承故障智能诊断方法。方法利用Morlet小波对采集到的印刷设备轴承原始振动信号进行处理,得到对应的二维时频图像,从时域和频域两方面对轴承故障进行表征;将时频图像作为Inception v3模型的输入,利用其模型的稀疏特性,快速从时频图像中自动学习故障特征,并对其模型参数进行调整;最后,利用训练好的模型实现印刷设备轴承故障诊断。结果利用印刷设备轴承实验平台对提出方法的有效性进行了验证,实验结果表明该方法的平均诊断精度可达92.53%。结论与传统智能诊断方法相比,所提方法在诊断精度与稳定性方面均具有一定的优势,可实现高精度印刷设备轴承故障诊断。
As a core rotating component in printing press,the operation status of bearing plays a major role in the health monitoring of printing press.The work aims to propose an intelligent diagnosis method of bearing faults in printing press by mixing the advantages of wavelet time-frequency processing with the Inception v3 model.The Morlet wavelet was used to process the raw vibration signals collected from bearing,and the corresponding two-dimensional time-frequency images were obtained to characterize the bearings faults from the time-domain and frequency-domain.The time-frequency images were used as input of the Inception v3 model,and the filter-level sparsity of the Inception v3 model was used to quickly and automatically learn the fault features from the time-frequency images and adjust the model parameters;finally,the trained model was used to implement the fault diagnosis of printing press bearing.The effective-ness of the proposed method was verified with a printing press experimental platform,and the results indicated that the average diagnostic accuracy of the method can reach 92.53%.Compared with traditional intelligent diagnosis methods,the proposed method has higher diagnosis accuracy and stability to achieve the bearings fault diagnosis of high-precision printing press.
作者
胡兵兵
唐嘉辉
武吉梅
HU Bing-bing;TANG Jia-hui;WU Ji-mei(School of Printing,Packaging Engineering and Digital Media Technology,Xi'an University of Technology,Xi'an 710048,China;School of Mechanical and PrecisionInstrument Engineering,Xi'an University of Technology,Xi'an 710048,China)
出处
《包装工程》
CAS
北大核心
2022年第13期189-195,共7页
Packaging Engineering
基金
国家自然科学基金(51705420,52075435)
陕西省自然科学基础研究计划(2020JQ-630,20JY054)
西安理工大学博士学位论文创新基金(252072105)。
