Extraction of Information from Time-Series Data

从时间序列数据中提取信息

• 批准号: 9529578
• 负责人: Babu Joseph
• 金额: $ 13.61万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-15 至 1999-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9529578&HistoricalAwards=false
• 关键词: Extraction; Information; Time; Series; Data

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.

摘要- Joseph和Motard - 9529578 许多化工厂具有分布式计算机控制系统（DCS），其从数千个传感器收集时间序列数据。 包含在该过去操作历史中的数据用于监视、控制和优化工厂操作。 这项工作的目标是开发和测试用于从在线过程传感器生成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，监控工厂运行状态和检测异常事件。 PIO将在传感器级别、单元级别和工厂级别调查问题。 这一项目涉及的具体问题包括：（I）传感器指纹识别的问题，即从传感器产生的原始信号中提取关于潜在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来提高从噪声输入/输出时间序列数据产生的过程模型中的参数的准确性的问题，以及（iii）事件检测和生成事件数据库的问题，所述事件数据库包含从包括质量、控制、经济、环境、维护和故障检测的各种观点监视和诊断工厂性能所需的信息。