Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

• 作者: RONG Ming-xing
• 来源期刊: 国际设备工程与管理：英文版
• 学科: 工学
• 关键词: BP神经网络 转子故障诊断 电机故障 小波变换 故障信号检测 故障特征提取 非平稳随机信号 故障诊断技术
• 年，卷（期）: 2012,（2）
• 页码范围: 104-111
• 页数: 8页
• 分类号: TP183|TP18
• 语种: 英文

摘要

In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	RONG Ming-xing		1	0	0.0	0.0