基于复Morlet小波的主变压器温度监测仪表故障诊断

Research on Visual Behavior Extraction of Outdoor Marker System Based on Eye Tracker

针对传统主变压器温度监测仪表故障诊断准确率低的问题,设计一种基于谱峭度与复Morlet小波的主变压器温度监测仪表故障诊断系统。首先,通过采集板中的温度传感器进行主变压器温度和振动数据采集;然后将采集数据传输至处理板,利用谱峭度提取温度监测仪表中的脉冲信号,从而获取复Morlet小波的中心频率和尺度;最后通过复Morlet小波进行信号处理,以此提升温度监测仪表的故障诊断准确率。结果表明,采用谱峭度法进行主变压器内环轴承振动数据提取的中心频率和带宽分别为1.75 kHz和500 Hz,可确定内环轴承故障的谱峭度共振频率为1.5 kHz~2 kHz。设定的内环故障频率为162.2 Hz,采用复Morlet小波进行变换后,在包络频域的163 Hz处出现峰值,找到主变压器温度监测仪表内环轴承的故障。由此说明,本方法可实现温度监测仪表信号的共振频率提取。可有效屏蔽外部噪声干扰,提高主变压器温度监测仪表故障诊断精度。

Aiming at the problem of low fault diagnosis accuracy of traditional main transformer temperature monitoring instrument,a fault diagnosis system of main transformer temperature monitoring instrument based on spectral cliff and complex Morlet wavelet is designed.Firstly,the temperature and vibration data of the main transformer are collected by the temperature sensor in the acquisition board;then the collected data is transmitted to the processing board to extract the pulse signal in the temperature monitoring instrument for the center frequency and scale of the complex Morlet wavelet;finally,the signal processing through the complex Morlet wavelet to improve the fault diagnosis accuracy of the temperature monitoring instrument.The results show that the center frequency and bandwidth of the vibration data are 1.75 kHz and 500Hz respectively,and the resonance frequency of the inner ring bearing is 1.5~2 kHz.The set internal ring fault frequency is 162.2Hz.After transforming with complex Morlet wavelet,the peak occurs at 163Hz in the envelope frequency domain,and the fault of the inner ring bearing of the main transformer temperature monitoring instrument is found.This result shows that the present method can realize the resonance frequency extraction of the temperature monitoring instrument signal.It can effectively shield the external noise interference and improve the fault diagnosis accuracy of the main transformer temperature monitoring instrument.