Accenture Global Solutions Limited

Jan Andre Nicholls

William Richard Gatehouse

Teresa Sheausan Tung

Jaeyoung Christopher Kang

Tiffany Cecilia Dharma

Jürgen Albert Weichenberger

Jurgen Albert Weichenberger

Rabinarayan Mishra

Loizos Markides

Rohit Banerji

Susarla Aditya

Subrahmanyam Vadrevu

Seshasai Rujuroop Kandrakota

Shereen Ashraf

Marius Meger

Giorgio Michele Scolozzi

Thanos Alifantis

Yassine Houari

Joel Titus

A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Feedback loop driven end-to-end state control of complex data-analytic systems

This disclosure relates to a reconfigurable simulative and predictive digital assistant/platform for simulation of a multi-stage processing facility. The digital assistant generates and assembles digital representations of the individual physical processing stages and components of the multi-stage processing facility in a reconfigurable manner according to a set of configuration commands generated using user inputs in a graphical user interface. At least one of the digital representations include a reusable predictive model that is trained when the digital representation is generated by the digital assistant. The digital assistant further performs simulation of the multi-state processing facility 'as is' or in alternative 'what-if' scenarios by simulating the digital representations according to a set of timing signals in the set of configuration commands.

Reconfigurable predictive and simulative digital platform for multi-stage processing facilities

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.

Machine learning systems for automated event analysis and categorization, equipment status and maintenance action recommendation

A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a monitored system to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

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Walton-on-Thames, United Kingdom

Jan Andre Nicholls