基于高分辨率谱估计的早期转子断条故障诊断

以快速傅里叶变换(FFT)为基础的电机电流信号特征分析(MCSA)具有频率分辨率低的固有缺陷,从而严重影响了鼠笼电机早期转子断条故障的诊断性能。为解决这一问题,提出基于高分辨率谱估计的早期转子断条故障诊断方法。首先利用Hilbert变换和离散小波变换对单相定子电流信号预处理,然后采用扩展Prony算法对预处理后的信号进行定性/定量分析。运用该方法对不同故障严重程度、不同负载条件下的3 k W电机稳态定子电流信号进行分析,并与FFT分析结果做对比。实验结果表明,即使在短时数据条件下所提方法仍然能够准确诊断出早期转子断条故障,验证了该方法的有效性和优越性。

High resolution spectral analysis for incipient broken rotor bar diagnosis

Motor current signal analysis (MCSA) based on fast Fourier transform (FFT) has its inherent drawback such as low resolution in frequency domain, thus, the detection performance becomes inaccurate for incipient broken rotor bar in squirrel cage induction motor. In this paper, a high resolution spectral analysis method is proposed to solve this issue. Firstly, data pre processing for single phase stator current is achieved by Hilbert transform and discrete wavelet transform (DWT). Then, extended Prony algorithm is utilized for pre processed signal qualitative and quantitative analysis. The steady state stator current of 3kw squirrel cage induction motor can be analyzed under different fault degrees and different operating conditions. The comparison is conducted with FFT. The experimental results shows the effectiveness and superiority on incipient broken rotor bar fault diagnosis even for short time data sequence.