摘要
轴承实时监测对于旋转机械运行的安全性和可靠性具有重要意义,已有研究侧重于轴承故障特征频率的提取,受限于解调频谱分辨率与采样时间限制,不能更加实时判断轴承故障类型。为实现轴承故障状态的有效识别,提出增强字典完备性以及稀疏系数稀疏度的拓展策略,建立基于字典学习的自适应特征向量提取,对于不同转速,混合负载下的4种轴承故障进行识别,结果表明:仅需少量样本数据(500采样点,250样本)就可达到较高的分类准确率(90%以上)。
Real-time monitoring of bearings is of great significance to the safety and reliability of rotating machinery operation.Existing research focuses on the extraction of bearing fault feature frequency,which is limited by the demodulation spectrum resolution and sampling time,and cannot determine the type of bearing faults in a more real-time manner.In order to realize the effective recognition of bearing fault state,the expansion strategy of enhancing dictionary completeness and sparse coefficient sparsity is proposed,and the adaptive feature vector extraction is established based on dictionary learning,which can recognize four kinds of bearing faults under different rotational speeds and mixed loads,and the results show that only a small number of samples are needed(500 samples,250 samples),and the classification accuracy can be realized with a higher accuracy of 90%or more.
作者
罗强
傅顺军
蔡洪钧
沈金平
王环
LUO Qiang;FU Shunjun;CAI Hongjun;SHEN Jinping;WANG Huan(Shanghai Marine Equipment Research Institute,Shanghai 200031,China;Institute of Process Equipment,College of Energy Engineering,Zhejiang University,Hangzhou 310027,China)
出处
《机电设备》
2024年第3期105-110,共6页
Mechanical and Electrical Equipment
关键词
轴承故障
字典学习
自适应特征向量提取
智能诊断
bearing fault
dictionary learning
adaptive feature vector extraction
intelligent diagnosis
