Abstract:

For the complex control systems，it is too hard to model precisely，so model-based fault detection methods are
not efficacious in application.A fault detection method based on parameter estimation of Symmetric Alpha-Stable distributions
for a class of model-unknown nonlinear system is proposed.Firstly，the output series is predicted，and the prediction error signal
with obvious abrupt impulses is obtained.Then the value of parameter alpha can be estimated through the method of parameter
estimation of Symmetric Alpha-Stable distributions.Therefore，the curse of parameter alpha is established.It is explicit
to detect system fault on the basis of this curse.The proposed method still has good robustness for signals corrupted by
large amplitude colored noise.The proposed method is applied to fault detection of bearing system.The fault state of bearing
can be detected exactly.The simulation result indicates that the method mentioned above is effective and feasible.

摘要： 因为复杂系统难以建立精确的数学模型，基于模型的故障检测方法在实际复杂控制系统中应用时往往难以获得很好的效果。针对这类数学模型未知的非线性系统，提出了一种基于SαS分布参数估计的系统故障检测方法。首先应用预测方法对系统输出序列进行预测建模，利用预测误差放大信号的脉冲突变，然后利用SαS分布的参数估计方法对预测误差序列的参数α进行估计，获得α的变化曲线，根据α的变化可以直观地判断出故障的发生。该方法对大幅值的有色噪声污染的信号仍然有很好的检测鲁棒性。以轴承系统的故障检测为例进行仿真实验，通过分析轴承振动信号故障条件下α曲线的变化情况，判断轴承的故障状态。仿真结果证实了该方法有效且可行。