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

Abstract

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 diagnosing method, the online fault diagnosis of the sensor can be rapidly and accurately realized, and the sensor-fault diagnosing method is particularly applicable to diagnosing the deviation faults and the drift faults of the sensor.

Description

technical field [0001] The invention relates to a sensor fault diagnosis method based on the least squares support vector machine online prediction, which is used to quickly and accurately locate the fault time, type and size of the sensor online, and is especially suitable for the diagnosis of sensor deviation and drift faults. Background technique [0002] In modern industrial production, especially in automation control, sensors play an important role. Sensor is a window to understand the state of the system process, and its effectiveness is the basis and premise of system process control and process optimization. Sensors are sensitive components and often work in harsh field environments. Electromagnetic interference, temperature changes and corrosion will cause certain damage to their performance. When the sensor fails, it will have a significant impact on the monitoring, control and fault diagnosis of the entire system. Common sensor faults include deviation fault, d...

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Problems solved by technology

However, this method also has the disadvantages of requiring a large number of samples, poor generalization ability, and easy to fall into local minimum points.

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