Sensor-fault diagnosing method based on online prediction of least-squares support-vector machine

The invention discloses a sensor-fault diagnosing method based on the online prediction of a least-squares support-vector machine. In the method, a least-squares support-vector machine online-predicting model is established, and then the measured data of a sensor is acquired on line and used as an input sample of the least-squares support-vector machine online-predicting model to realize that theoutput value of the sensor at the next moment is predicted in real time as the predicting model is trained on line. Whether sensor faults occur or not is detected by comparing residual errors generated by the predicting value and the actual output value of the sensor. When the faults occur, the unary linear regression for a residual-error sequence is carried out by a least-squares method to realize the identification of the deviation and drift faults of the sensor, and furthermore, measures can be more effectively taken to carry out real-time compensation for the output of the sensor. Throughthe sensor-fault diagnosin