ICA在模态参数识别和故障诊断中的应用

The Application of ICA in the Modal Parameter Identification and Fault Diagnosis

模态参数识别是故障诊断中的常用手段之一。通过独立分量分析(ICA)技术将结构的自由振动响应信号分解为若干个解耦的单自由度信号,进而结合Hilbert变换识别模态参数。通过数值仿真分析了不同ICA算法在不同工况下识别结构模态参数的可行性,并将识别精度较高的二阶盲辨识(SOBI)方法应用于悬臂梁裂纹故障实验。实验结果表明,裂纹故障产生时结构的各阶固有频率都存在不同程度的降低,且高阶固有频率降低的尤为明显,为机械设备故障诊断提供了有力的参考依据。

Modal parameter identification is one of the commonly means in fault diagnosis. The free vibration response signal of structure is decomposed into a number of single degree of freedom decoupling signal by the ICA technology, and then through the Hilbert transform to identify modal parameters. The feasibility of different ICA algorithm to identify the structural modal parameters under different condition was analyzed through numerical simulation, and the SOBI of high identification precison was applied to cantilever beam crack fault experiment. The results show that natural frequency of the structure have different degrees of reduction especially the high order natural frequency when the crack fault occurs, it provides a powerful reference for fault diagnosis of mechanical equipment.