载荷影响下的机电阻抗协整结构损伤识别方法

针对滚动轴承早期故障特征信息十分微弱难以提取以及可调品质因子小波变换(tunable Q-factor wavelet transform，简称TQWT)参数设置依赖使用者经验的问题，提出改进的TQWT的滚动轴承早期故障诊断方法。首先，设定Q因子的区间范围，利用TQWT对滚动轴承故障振动信号进行分解得到若干个分量；其次，对各分量进行包络导数能量算子解调，在能量谱中根据特征频率强度系数这一指标自适应地确定TQWT的最佳分解参数，实现对故障信号的最优分解；最后，通过对最佳分量的包络导数能量谱分析即可准确地提取到轴承故障特征信息。通过对仿真信号、实验数据以及工程案例分析表明，该方法能够有效提取滚动轴承早期微弱故障特征并准确判断出滚动轴承故障类型，具有一定的工程应用价值。

Structural Damage Identification Under Load Based on Electromechanical Impedance Co-integration

The early fault feature information of rolling bearing is very weak and difficult to extract, and the selection of parameters of the tunable Q-factor wavelet transform (TQWT) depend on the user''s experience. The method of early fault diagnosis of rolling bearing based on improved TQWT is proposed to solve the above problems. Firstly, the range of Q-factor is presented. The several components are obtained by decomposing the fault bearing acceleration vibration signal using TQWT. Then, the each component is demodulated using the envelope derivative energy operator. The best decomposition parameters is selected adaptively according to the index of characteristic frequency strength factor in the energy spectrum. The optimal decomposition results of the fault signal can be obtained. Finally, the fault feature information can be accurately extracted by analyzing the envelope derivative energy spectrum of the optimal component. The method is verified by analyzing simulation signal, experiment data and engineering case. The results show that the method can effectively extract the early weak fault characteristics of rolling bearing and accurately judge the type of fault. It has a certain value of engineering application.