Extraction of Information from Time-Series Data

摘要：许多化工厂都有分布式计算机控制系统（DCS），它从数千个传感器收集时间序列数据。过去运行历史中包含的数据用于监视、控制和优化工厂运行。这项工作的目的是开发和测试从在线过程传感器产生的时间序列数据中提取信息的工具和算法。信息提取任务包括将原始传感器数据转换为有意义的过程变量，识别过程模型，如输入-输出传递函数，监测工厂运行状态和检测异常事件。pio将在传感器级、单元级和工厂级调查问题。本项目处理的具体问题包括：(I)传感器指纹问题，即从传感器产生的原始信号中提取有关底层过程现象的信息的问题；（ii）通过选择性预滤波以最大化信噪比，提高由噪声输入/输出时间序列数据生成的过程模型中参数的准确性的问题；（iii）事件检测和生成事件数据库的问题，该数据库包含从质量、控制、经济、环境、维护和故障检测等各个角度监测和诊断工厂性能所需的信息。

Abstract - Joseph and Motard - 9529578 Many chemical plants have distributed computer control systems (DCS) which collect time series data from thousands of sensors. The data contained in this past operational history is used to monitor, control and optimize the plant operation. The objective of this work is to develop and test tools and algorithms for extracting information from time series data generated by on-line process sensors. Information extraction tasks include converting raw sensor data to meaningful process variables, identifying process models such as input-output transfer functions, monitoring the status of plant operation and detection of abnormal events. The PIOs will investigate the problem at the sensor level, the unit level and the plant level. Specific issues addressed by this project include: (I) the problem of sensor fingerprinting, i.e. the problem of extracting information about the underlying process phenomena from the raw signal generated by the sensor, (ii) the problem of improving the accuracy of parameters in process models generated from noisy input/output time series data by selective prefiltering to maximize the signal to noise ratio, and (iii) the problem of event detection and generation of an event data base containing information needed to monitor and diagnose performance of the plant from various points of view including quality, control, economics, environmental, maintenance, and fault detection.