摘要
泵站的智能化建设过程中呈现的自动化和少人化特点,要求系统关键参数的监测具有更高的可靠性,按照上述要求,构建多种传感器配合工作的冗余监测机制。针对多传感器产生的数据,通过卡尔曼滤波,获得动态参数在校验时刻的最优参数估计值,基于拉依达准则,确定传感器在噪声环境下的异常甄别阈值,对完成校验的多路监测值,通过加权融合,得到参数最终监测值。该方法可为自动化、无人化智能系统的监测数据可靠性保障措施提供借鉴。
The characteristics of automation and fewer people in the intelligent construction of the pumping station require the monitoring of the key parameters of the system to have higher reliability.According to the above requirements,a redundant monitoring mechanism with a variety of sensors is constructed.In view of the data generated by multi-sensor,the optimal parameter estimation value of dynamic parameters at the calibration time is obtained by Kalman filtering.Based on the Raita criterion,the anomaly discrimination threshold of the sensor in the ambient noise environment is determined.For the multi-channel monitoring values that complete the verification,through weighted fusion,the final monitoring values of the parameters are obtained.The method can be used as a reference for the reliability guarantee of monitoring data in automatic and unmanned intelligent system.
出处
《科技创新与应用》
2023年第22期185-188,192,共5页
Technology Innovation and Application
关键词
智能泵站
冗余监测
卡尔曼滤波
拉依达准则
无人化智能系统
intelligent pumping station
redundant monitoring
Kalman filter
Raita criterion
unmanned intelligent system
