Honeywell International Inc.

Viraj Srivastava

Prangya Priyadarsini

Ramakrishnan Ganapathi

Anand Narayan

Sanjay Kantilal Dave

Michael Niemiec

Ananthapadmanabha Krishnamurthy

Greg Stewart

Srikant Srinivasan

Karthikeyan Sundarasamy

Suresh Thombare

Vijoy Akavalappil

Akanksha Jain

A trained machine learning algorithm processes time series production data. The time series production data are representative of a control process within a facility control loop. The machine learning training algorithm is trained using positive training data that are representative of a normal operation of components within the facility control loop and negative training data that are representative of an abnormal operation of components within the facility control loop. Output of the trained machine learning algorithm identifies abnormalities in the facility control loop.

Machine learning based direct method of determining status of facility control loop components

A system processes historical facility data that relate to facility states and modes of operation. The historical facility data are clustered into groups representing the facility states and the modes of operation. The groups are used to determine a current state and mode of the facility. When the facility is in a normal state, the system determines whether an event in the facility is an abnormality. If an abnormality is identified, the system transmits a signal indicating the abnormality.

Identification of facility state and operating mode in a particular event context

A method of alarm notification includes providing data for an industrial process including stored alarm event data and stored Key Performance Indicator (KPI) data, and an alarm event-KPI correlation and operator notification system. Patterns of relationships are discovered between the stored alarm event and KPI data to provide a reference pattern database that identifies KPI violation events in the KPI data as a function of the alarm event data or alarm events in the alarm event data as a function of the KPI data. Pattern matching uses a real-time alarm event and real-time KPI data to determine a pattern similarity by comparing a current sequence spanning a predetermined time of the real-time alarm event and KPI data to the patterns. When the pattern matching identifies a current alarm event or KPI violation event, an operator is notified and a predicted KPI value or feature or a predicted alarm event.

Process performance issues and alarm notification using data analytics

An apparatus, method, and non-transitory machine-readable medium provide for accurate automatic determination of alarm-operator action linkage for operator assessment and alarm guidance using custom graphics and control charts. The apparatus includes a memory and a processor operably connected to the memory. The processor receives process control system data regarding a field device in an industrial process control and automation system; extracts information from the process control system data; generates a tuple based on the extracted information; and performs a rectifying operation in the industrial process control and automation system based on the generated tuple of the field device.

System and method for accurate automatic determination of 'alarm-operator action' linkage for operator assessment and alarm guidance using custom graphics and control charts

A method includes receiving one or more operating conditions from one or more field devices of one or more industrial plants in response to receiving one or more alarm signals. The method also includes determining one or more industrial plant states based on the one or more operating conditions. The method further includes identifying one or more historical alarm episodes stored in one or more data stores based on the one or more industrial plant states. In addition, the method includes identifying one or more recommended historical alarm episodes of the one or more historical alarm episodes based on one or more benchmark metrics of the one or more historical alarm episodes. The method also includes generating the one or more recommended historical alarm episodes for display on a user interface.

System and method for plant operation gap analysis and guidance solution

A method includes identifying changes in an alarm count for at least one alarm to be generated in an industrial process control and automation system. The changes in the alarm count are based on a plurality of different sets of alarm settings. The method also includes presenting the changes in the alarm count to a user and receiving a selection of one of the sets of alarm settings. The method further includes configuring the industrial process control and automation system with the selected set of alarm settings to enable the industrial process control and automation system to generate alarms using the selected set of alarm settings.

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New Delhi, India

Viraj Srivastava