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针对轴承故障信号在传输过程中发生非线性畸变、混叠等特点,将核独立分量分析(KICA)技术引入到了轴承故障诊断中,并结合EMD这种先进的自适应的信号分解方法,提出了基于KICA算法的阶次EMD方法,将其应用于轴承故障诊断中,对齿轮箱的瞬态声信号进行分析处理,试验结果和对比研究表明,该算法可有效地增强信号的信噪比,使故障特征更加明显,提高了故障诊断的准确度。
Aiming at the characteristics of bearing fault signal nonlinear distortion and aliasing during the transmission, the KICA technology is introduced into bearing fault diagnosis. Combined with EMD, an advanced and adaptive signal decomposition method, The order EMD method based on KICA algorithm is applied to bearing fault diagnosis, and the transient sound signal of gearbox is analyzed and processed. The experimental results and comparative studies show that this algorithm can effectively enhance the signal to noise ratio of the signal, Fault features more obvious, improve the accuracy of fault diagnosis.