【摘要】 提出了一种新的故障检测方法 ,该方法采用小波系数描述系统的故障特征 ,以模糊树模型作为系统故障特征的分类模型 ,并将此方法应用于直升机作动器的实时故障检测 .仿真结果表明 ,这种故障检测方法结合了小波变换和模糊树模型的特点 ,具有检测精度高、抗噪声和跟踪系统参数变化的能力强及特别适合于处理高维数据等优点更多还原

【Abstract】 A new fault detection approach was presented, in which a key coefficient group consisting of partial wavelet coefficients was used to describe fault features, a fuzzy-tree was taken as the model of fault feature classification. As an example,the method was applied to the real-time fault detection of the actuators of UH-60A helicopter. Simulation results showed that the proposed fault detection technique possesses satisfactory detecting accuracy, good capability of overcoming noises, and the function of trailing changes in system parameters. Furthermore, the detecting precision can be markedly improved by increasing the number of wavelet coefficients in the key coefficient group.更多还原