基于系统特性盲辨识的齿轮箱故障诊断

Fault Diagnosis of Gearbox based on System Characteristic Blind Identification

盲系统辨识是仅由输出数据来获得系统特性函数的一种信号处理方法。系统特性只与自身的结构相关,一种工况就对应着一种特定的系统特性。将系统结构及工况两者结合分析,可有效应用于齿轮箱的故障诊断。首先,利用独立分量分析对获得的的信号进行预处理,提取出包含故障频率的信号作为系统模型的响应信号。其次,高阶累积量具有消除和衰减高斯噪声的特性,使用高阶累积量构建时间序列模型。最终,依据模型的系数计算得到的ARMA双谱定性分析,用量子自组织特征映射网络给出定量的判据。实验结果表明,此方法对齿轮箱故障的存在和故障类型的诊断,可以提供一些有价值的结论。

Blind system identification is a signal processing technology, which obtains the function of system characteristics from its output data only. The system characteristics are associated with its own structure, per operating mode corresponding to a particular working state. Gearbox fault diagnosis can be conducted by combining the system structure and working condition. Firstly, using independent component analysis to preprocess the output data, the fault frequency of signal as the responding signal of a system model is extracted. Secondly, the time series model is built by using high order cumulants of eliminating and attenuating characteristics of Gaussian noise. Finally, the ARMA bis- pectrum qualitatively analysis is obtained according to the coefficients of the model. In the meanwhile, the quantitative criteria is obtained by using quantum self- organizing feature map neural network. The results show that, the method can provide some valuable conclusions for presence and type of the fault diagnosis of the gearbox