Wavelet Transformation Fualt Diagnosis of Acitve Magnetic Beraings Based on Wavelet Transformation
公强 王 · Mechanical Engineering and Technology · 2021
电磁轴承故障信号中电磁振动成分和机械振动成分相互耦合，呈现较强的非平稳性。以傅里叶变换(FFT)为基础的传统频域分析方法无法满足分析的需要，对此提出小波变换对电磁轴承故障信号进行频谱分析，本文基于小波变换，对原始信号进行多层次分解，有效地从电流信号中剥离高强度噪声，强化故障特征表达，为磁轴承故障诊断提供更为有效的数据信号。仿真结果表明利用小波变换能够检测出FFT不能检测出的故障信号。 The electromagnetic vibration and the mechanical vibration in the active magnetic bearing (AMB) fault signal are coupled with each other, which are strong non-stationarity. The traditional frequency domain analysis method based on Fourier transform (FFT) cannot meet the needs of analysis. In this regard, wavelet transform is proposed to analyze the frequency spectrum of the electromagnetic bearing fault signal. Based on wavelet transform, this paper decomposes the original signal at multiple levels, effectively separates the high-intensity noise from the current signal, strengthens the fault feature expression, and provides a more effective data signal for magnetic bearing fault diagnosis. The simulation results show that the use of wavelet transform can detect fault signals that FFT cannot detect.