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

A technology for support vector machines, sensor failures, applied in instruments, computer parts, character and pattern recognition, etc., can solve problems such as demand, large number of samples, poor generalization ability, etc.

Active Publication Date: 2012-08-15
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.

Method used

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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 squares support vector machine on-line prediction, the core idea of ​​which is as follows: figure 1 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 sets of data. Training sample: Use the rolling historical output data of the sensor 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 and 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, the residual sequence is used to identify the type and size of the fault, so that the o...

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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 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...

Claims

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Application Information

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Patent Type & Authority Patents(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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