摘要
针对从单一振动信号或电流信号中估计转速精度不高的问题,提出了一种基于振动和电流信号深度融合的方法,并应用在变转速工况下电机轴承的故障诊断中。首先利用自适应调频模态分解法(ACMD)提取振动和电流信号的瞬时频率(IF)曲线;然后采用卷积-长短时记忆网络(CNN-LSTM)将2条曲线融合得到电动机转子的IF曲线和转速曲线;最后根据电动机转子的IF曲线计算累计转角曲线,采用阶次跟踪(OT)方法对振动信号进行重采样进而识别电动机故障。在变转速工况下针对开关磁阻电机轴承外圈和内圈故障的试验验证了该方法的有效性,转速估计的均方根误差低至15.5 r/min。
To solve the problem of low accuracy of speed estimation from a single vibration signal or current signal, a method based on deep fusion of vibration and current signals is proposed and applied to fault diagnosis of motor bearings under variable speed conditions. Firstly, the adaptive chirp mode decomposition(ACMD) is used to extract the instantaneous frequency(IF) curves of vibration and current signals. Then, the convolutional neural network-long and short term memory(CNN-LSTM) is used to fuse the two curves to obtain the IF curve and speed curve of motor rotor. Finally, the cumulative rotation angle curve is calculated according to the IF curve of motor rotor, and the order tracking(OT) method is used to resample the vibration signal and identify the motor fault. The experiments on outer ring and inner ring faults of switched reluctance motor bearings under variable speed conditions verify the effectiveness of the method, and the root mean square error of speed estimation is as low as 15.5 r/min.
作者
陈康
安康
王骁贤
宋俊材
陆思良
CHEN Kang;AN Kang;WANG Xiaoxian;SONG Juncai;LU Siliang(School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China;Department of Precision Machinery and Precision Instrumentation,University of Science and Technology of China,Hefei 230027,China)
出处
《轴承》
北大核心
2023年第2期54-60,共7页
Bearing
基金
国家自然科学基金资助项目(52075002)。
关键词
滚动轴承
电机轴承
故障诊断
瞬时频率
转速
rolling bearing
motor bearing
fault diagnosis
instantaneous frequency
speed
