Thomas J O'Connor

US Patent:

20110276165, Nov 10, 2011

Filed:

May 5, 2011

Appl. No.:

13/101497

Inventors:

Ramesh Vaidhyanathan - Vienna VA, US
Jeffrey Richter - Chantilly VA, US
Thomas F. O'Connor - Washington DC, US

Assignee:

EXXONMOBIL RESEARCH AND ENGINEERING COMPANY - Annandale NJ

International Classification:

G06F 19/00

US Classification:

700104

Abstract:

A computer-implemented integrated expert system for the identification of abnormal events in an industrial plant. The expert system integrates a model-based expert system with a rule-based expert system. The model-based expert system receives data on the operating conditions of a process unit in the industrial plant and calculates one or more results that determine whether the process unit is operating abnormally. The rule-based expert system also receives data on the operating conditions of the process unit and applies its rules to the process data. The rule-based expert system also applies its rules to the one or more results generated by the model-based expert system. The integrated system may also suppress any redundant messages generated by the model-based expert system.

US Patent:

20150219530, Aug 6, 2015

Filed:

Dec 1, 2014

Appl. No.:

14/556458

Inventors:

- Annandale NJ, US
Thomas F. O'CONNOR - Washington DC, US
Sourabh K. DASH - Beaumont TX, US
Jeffrey J. SOMMERS - Fairfax VA, US

Assignee:

ExxonMobil Research and Engineering Company - Annandale NJ

International Classification:

G01M 99/00
G06F 17/18

Abstract:

Detection of event conditions in an industrial plant includes receiving process data corresponding to one or more sensors, estimating normal statistics from the process data, estimating abnormal statistics from the process data with potentially abnormal operation of the one or more components, determining a fault model from the estimated normal and abnormal statistics, the fault model including a learning matrix, one or more fault indices indicating a likelihood of an occurrence of one or more fault events, and a fault threshold corresponding to the one or more sensors, determining one or more further fault indices from the further process data; applying the fault threshold to the one or more further fault indices, and indicating a further occurrence of the one or more fault events when a magnitude of the one or more further fault indices exceeds the fault threshold corresponding to the one or more sensors.