Wang Zhenyu, Chiang Leo
Continuous Improvement Center of Excellence, The Dow Chemical Company, Lake Jackson, TX 77566, USA.
Sensors (Basel). 2019 May 15;19(10):2240. doi: 10.3390/s19102240.
With the emergence of Industry 4.0, also known as the fourth industrial revolution, an increasing number of hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality and process safety. The accuracy of individual sensors can be easily impaired by a variety of factors. To improve process monitoring accuracy and reliability, a sensor fusion scheme based on Bayesian inference is proposed. The proposed method is capable of combining multi-rate sensor data and eliminating the spurious signals. The efficacy of the method has been verified using a process implemented at the Dow Chemical Company. The sensor fusion approach has improved the process monitoring reliability, quantified by the rates of correctly identified impurity alarms, as compared to the case of using an individual sensor.
随着工业4.0（也称为第四次工业革命）的出现，越来越多的硬件和软件传感器已应用于化学生产过程中，以监测与产品质量和过程安全相关的关键变量。单个传感器的精度很容易受到多种因素的影响。为了提高过程监测的准确性和可靠性，提出了一种基于贝叶斯推理的传感器融合方案。该方法能够融合多速率传感器数据并消除虚假信号。通过陶氏化学公司实施的一个过程验证了该方法的有效性。与使用单个传感器的情况相比，传感器融合方法提高了过程监测的可靠性，这通过正确识别杂质警报的速率来量化。
