Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband ...Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.展开更多
针对具有双通道结构的无刷直流电动机(BLDCM,Brushless DC Motor)容错系统的高可靠性要求和大范围调速特点,在不增加额外设备的条件下,通过分析两个通道中功率电路直流母线电流波形的特点,提出一种采用归一化快速傅里叶变换(FFT,Fast Fo...针对具有双通道结构的无刷直流电动机(BLDCM,Brushless DC Motor)容错系统的高可靠性要求和大范围调速特点,在不增加额外设备的条件下,通过分析两个通道中功率电路直流母线电流波形的特点,提出一种采用归一化快速傅里叶变换(FFT,Fast FourierTransform)方法提取频率特征,再结合基于规则的专家系统进行故障检测与识别的方法,并通过实际电动机系统的试验验证了方法的正确性.试验结果表明:规一化FFT方法可以消除不同转速和不同负载对判断结果的影响;专家系统中阈值的选取可以有效避免实际应用中出现的噪声等因素的影响.算法复杂度低,可靠性高,易于应用,具有很强的实际操作性.展开更多
针对MUSIC(Multiple Signal Classification Algorithm)算法用于故障诊断的两大缺点:运算量大和可能产生的虚假波峰对故障特征的混淆带来的不利影响,给出了相应的解决方法。定子电流Hilbert幅值包络信号包含了低频的故障特征频率调制信...针对MUSIC(Multiple Signal Classification Algorithm)算法用于故障诊断的两大缺点:运算量大和可能产生的虚假波峰对故障特征的混淆带来的不利影响,给出了相应的解决方法。定子电流Hilbert幅值包络信号包含了低频的故障特征频率调制信号,故通过对定子电流Hilbert幅值包络信号进行降采样率重采样、减少分析信号长度,可避免低频段频谱混叠,再对减少的重采样数据进行具有超分辨率的MUSIC低频段估计,可大大降低MUSIC算法频谱估计时间;运用连续细化傅里叶变换(SFFT)和转子齿槽谐波转差率估计技术,可预知故障特征频率精确值,从而可有目的地查询故障分量,消除MUSIC虚假波峰对故障检测混淆的影响,大大提高了故障检测的灵敏度和可靠性,以此形成异步电动机转子断条故障检测新方法。试验表明,该方法可行有效。展开更多
基金Project supported by the Second Stage of Brain Korea 21 Projects
文摘Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.
文摘针对具有双通道结构的无刷直流电动机(BLDCM,Brushless DC Motor)容错系统的高可靠性要求和大范围调速特点,在不增加额外设备的条件下,通过分析两个通道中功率电路直流母线电流波形的特点,提出一种采用归一化快速傅里叶变换(FFT,Fast FourierTransform)方法提取频率特征,再结合基于规则的专家系统进行故障检测与识别的方法,并通过实际电动机系统的试验验证了方法的正确性.试验结果表明:规一化FFT方法可以消除不同转速和不同负载对判断结果的影响;专家系统中阈值的选取可以有效避免实际应用中出现的噪声等因素的影响.算法复杂度低,可靠性高,易于应用,具有很强的实际操作性.
文摘针对MUSIC(Multiple Signal Classification Algorithm)算法用于故障诊断的两大缺点:运算量大和可能产生的虚假波峰对故障特征的混淆带来的不利影响,给出了相应的解决方法。定子电流Hilbert幅值包络信号包含了低频的故障特征频率调制信号,故通过对定子电流Hilbert幅值包络信号进行降采样率重采样、减少分析信号长度,可避免低频段频谱混叠,再对减少的重采样数据进行具有超分辨率的MUSIC低频段估计,可大大降低MUSIC算法频谱估计时间;运用连续细化傅里叶变换(SFFT)和转子齿槽谐波转差率估计技术,可预知故障特征频率精确值,从而可有目的地查询故障分量,消除MUSIC虚假波峰对故障检测混淆的影响,大大提高了故障检测的灵敏度和可靠性,以此形成异步电动机转子断条故障检测新方法。试验表明,该方法可行有效。
