Computer system and method for causality analysis using hybrid first-principles and inferential model
US10739752B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 21, 2018 |
| Grant date | Aug 11, 2020 |
| Priority date | — |
| Expiry date | Feb 20, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B23/0281
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Computer-based methods and system perform root-cause analysis on an industrial process. A processor executes a hybrid first-principles and inferential model to generate KPIs for the industrial process using uploaded process data as variables. The processor selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the selected data into time series. The system and methods select time intervals from the time series based on data variability and perform a cross-correlation between the loaded process variables and the selected time intervals, resulting in a cross-correlation score for each loaded process variable. Precursor candidates from the loaded process variables are selected based on the cross-correlation scores, and a strength of correlation score is obtained for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores, and analyze the root-cause of the event.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.