Finding significant temporal and spatial patterns in industrial process data

寻找工业过程数据中重要的时间和空间模式

• 批准号: 412198-2011
• 负责人: Sander, Jörg
• 金额: $ 3.2万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=498531
• 关键词: Finding; significant; temporal; spatial; patterns

Industrial processes, such as those found at Syncrude, generate vast amounts of temporal and spatial. This data is typically generated by sensors used in monitoring the processes in order to maintain safe, reliable operation within the designed envelope of operation. For example, power levels applied to shovel ropes during operation change with both different operators and different locations, or a complex oil extraction process, monitored in terms of flow rates, density, composition, temperature, etc.

工业流程，例如 Syncrude 的工业流程，会产生大量的时间和空间。这些数据通常由用于监控过程的传感器生成，以便在设计的操作范围内保持安全、可靠的操作。例如，在操作过程中应用于铲绳的功率水平会随着不同的操作员和不同的位置而变化，或者在复杂的石油开采过程中，根据流量、密度、成分、温度等进行监控。