多变量过程传感器故障检测的SVM方法

提出一种适用于具有纯滞后多变量过程的传感器故障检测方法.该方法结合支持向量机回归算法与数据驱动的信息融合技术,给出一种采用广义支持向量机观测器的传感器故障检测、分离和数据恢复系统的架构.每个关键传感器都配置一个由过程输入和除被监视传感器之外的过程输出共同驱动的观测器,对传感器实际输出与观测器输出进行了比较,并对数据的有效性进行了确认.多组分精馏塔系统实验表明,该方法能够对过程传感器故障进行检测,并且具有较好的鲁棒性.

SVM Approach for Sensor Fault Detection in Multi-Variable Processes

An approach for sensor fault detection in case of multi-variable processes with time-delays is presented.Combining support vector machines(SVM) regression algorithm with data driven information fusion technique,a system scheme of sensor fault detection,isolation and data recovery based on support vector machines generalized observer(SGO) is proposed.Each key sensor is dedicated with an observer driven by process inputs and outputs,except for the sensor to be monitored.Data is validated by comparing sensor outputs with the observer outputs.Experiments of multi components distillation column(MCDC) showed that the above method can detect sensor fault in processes effectively and has preferable robustness.