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

A support vector machine and sensor failure technology, applied in the direction of instruments, computer components, character and pattern recognition, etc., can solve problems such as poor generalization ability, need, and large number of samples

Active Publication Date: 2012-01-18
BEIJING INSTITUTE OF TECHNOLOGYGY
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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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  • Sensor-fault diagnosing method based on online prediction of least-squares support-vector machine
  • Sensor-fault diagnosing method based on online prediction of least-squares support-vector machine
  • Sensor-fault diagnosing method based on online prediction of least-squares support-vector machine

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Embodiment Construction

[0026] The present invention provides a sensor fault diagnosis method based on least square support vector machine online prediction, its core idea is, as figure 1 As shown, the least squares support vector machine is selected to construct the online prediction model of the sensor. In the process of sensor sampling, a large window is used to slide in the measurement data to obtain the training data pool, and a small window is used to slide from the training data pool to obtain multiple groups. Training sample: use the sensor's rolling historical output data as a training sample to train the least squares support vector machine prediction model, and then when a new sample is input, the prediction model will predict the output value of the sensor at the next moment. By comparing the actual output of the sensor with the residual error generated by the least squares support vector machine prediction model output value, it is judged whether the fault occurs. If a fault is detected,...

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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 the output 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. Through the 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 least squares support vector machine online prediction, which is used for quickly and accurately locating the time, type and size of sensor faults 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. The sensor is a window to understand the process status of the system, and its effectiveness is the basis and premise of the system process control and process optimization. The sensor is a sensitive component, and it often works in a harsh field environment. Electromagnetic interference, temperature changes and corrosion will cause certain damage to its 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 deviat...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01D18/00
Inventor 邓方蔡涛徐丽双陈杰窦丽华
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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