机械研究与应用
機械研究與應用
궤계연구여응용
Mechanical Research & Application
2015年
5期
184-187
,共4页
小波包分解%神经网络%振动信号%转子不平衡
小波包分解%神經網絡%振動信號%轉子不平衡
소파포분해%신경망락%진동신호%전자불평형
wavelet packet decomposition%neural network%vibration signal%rotor unbalance
针对振动信号非平稳的特点，为了快速准确地判断引起转子不平衡的原因，提出了一种基于小波包分解和神经网络相结合的电机故障检测研究方法，对采集的振动信号进行小波包分解，利用分解的小波系数求取各个频带峭度，根据频带峭度的变化提取特征向量，应用RBF神经网络进行检测识别，并通过MATLAB仿真实现。
針對振動信號非平穩的特點，為瞭快速準確地判斷引起轉子不平衡的原因，提齣瞭一種基于小波包分解和神經網絡相結閤的電機故障檢測研究方法，對採集的振動信號進行小波包分解，利用分解的小波繫數求取各箇頻帶峭度，根據頻帶峭度的變化提取特徵嚮量，應用RBF神經網絡進行檢測識彆，併通過MATLAB倣真實現。
침대진동신호비평은적특점，위료쾌속준학지판단인기전자불평형적원인，제출료일충기우소파포분해화신경망락상결합적전궤고장검측연구방법，대채집적진동신호진행소파포분해，이용분해적소파계수구취각개빈대초도，근거빈대초도적변화제취특정향량，응용RBF신경망락진행검측식별，병통과MATLAB방진실현。
Aiming at the non-station of vibration signals, this paper presents a motor fault diagnosis method based on the wavelet packet decomposition and the neural network for the sake of quick and accurate detection to the reasons of rotor unbal-ance. This method strikes the kurtosis of frequency band through the coefficient of wavelet packet, and gains the feature vector from various changes in the kurtosis of each frequency band, and identifies fault with RBF neural network, and uses the MAT-LAB software to realize it. The experimental results show that the method is effective and feasible for the detection of the rotor unbalance, and provides effective way for the detection of motor fault.
