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目的应用单一传感器信号检测控制系统中的传感器故障. 方法采用由径向基函数网络组成一种神经预测器,神经预测器采用由n-均值分簇和Kalman组成的混合算法在线对传感器信号进行学习,并在此基础上预测传感器输出,神经预测器的预测输出和传感器实际输出值之差如果大于一个阈值,则可检测出相应的传感器发生了故障. 故障检测阈值的选取与噪声方差和可能的预测误差有关. 结果与结论计算机仿真结果表明该方法有效地检测出了水翼艇动力装置中的陀螺故障.“,”Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on-line with a hybrid algorithm composed of n-means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.