US-12626209-B2 - Method and system for predicting KPI values, plant states and alarms in an industrial process

US12626209B2US 12626209 B2US12626209 B2US 12626209B2US-12626209-B2

Abstract

Predetermined Key Performance Indicators (KPIs) of an industrial process may be predicted. A plurality of tags each identify a corresponding KPI of the industrial process and historical values for the KPIs that are identified by the plurality of tags. A KPI forecast model is trained for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. A forecasted KPI value is generated for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI.

Inventors

Anand Narayan
Rahul Ravi
Rakshitha Prabhu
Akriti Kedia
Priyanshu Sinha
Varshaneya V

Assignees

HONEYWELL INTERNATIONAL INC.

Dates

Publication Date: 20260512
Application Date: 20230906

Claims (16)

1 . A method for predicting predetermined Key Performance Indicators (KPIs) of an industrial process, the method comprising: receiving, by a controller, a plurality of tags, wherein each tag identifies a corresponding KPI of the industrial process; receiving, by the controller, historical values for each of the KPIs that are identified by the plurality of tags; training, by the controller, a KPI forecast model for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for one or more KPIs of a plurality of the KPIs that are identified by the plurality of tags; and generating, by the controller, a forecasted KPI value for each of the KPIs identified by the plurality of tags based at least in part on the KPI forecast model that corresponds to the respective KPI; receiving, by the controller, one or more alarm limits, historical alarms, and/or events associated with the one or more KPIs of the plurality of KPIs; identifying, by the controller, one or more overlapping episodes associated with the one or more KPIs and related to the historical alarms and/or the events; excluding, by the controller, the one or more overlapping episodes of the historical alarms and/or the events that are associated with the one or more KPIs of the plurality of KPIs; training, by the controller, an alarm forecast model based on at least on the events associated with the one or more KPIs exclusive of the historical alarms and/or the events related to the one or more overlapping episodes of the historical alarms and/or the events; and determining, by the controller, using the trained alarm forecast model whether the forecasted KPI value is outside the corresponding alarm limit; forecasting, by the controller, an alarm for each of the one or more KPIs based on the trained alarm forecast model, wherein the alarm forecasted by the trained alarm forecast model excludes the one or more overlapping episodes; and automatically adjusting, by the controller, one or more parameters associated with the industrial process based on the forecasted KPI values and the forecasted alarms, wherein the one or more parameters associated with the industrial process are adjusted to negate the forecasted alarm.
2 . The method of claim 1 , comprising: receiving a time to forecast KPI value for each of the plurality of KPIs; and generating the forecasted KPI value for each of the plurality of KPIs identified by the plurality of tags at the time in future that corresponds to the time to forecast the KPI value.
3 . The method of claim 2 , wherein each of the KPI forecast models is trained based at least in part on the received historical values for the one or more KPIs of the plurality of KPIs of the industrial process that are identified by the plurality of tags and one or more of the forecasted KPI values.
4 . The method of claim 1 , wherein the industrial process comprises a plurality of plants, wherein each plant comprising two or more tags of the plurality of tags that each identify a corresponding KPI of the corresponding plant, and each plant has two or more predetermined plant states, and the method further comprising: training a plant state forecast model for each of the plurality of plants, wherein each of the plant state forecast models is trained to forecast the plant state of the respective plant based at least in part on the received historical values for the corresponding KPIs that are associated with the respective plant and/or the forecasted KPI values for the corresponding KPIs that are associated with the respective plant; and generating a forecasted plant state for at least one of the plurality of plants of the industrial process based at least in part on the plant state forecast model that corresponds to the respective plant.
5 . The method of claim 4 , wherein each of the plant state forecast models is used to forecast the plant state of the respective plant based at least in part on the received historical values for the KPIs that are associated with the respective plant and one or more of the forecasted KPI values for one or more of the plurality of KPIs that are associated with the respective plant.
6 . The method of claim 4 , further comprising: receiving the one or more alarm limits for the one or more KPIs of the plurality of KPIs associated with each of the plurality of plants; and wherein each of the plant state forecast models is trained to forecast the plant state of the respective plant based at least in part on the received historical values for the KPIs that are associated with the respective plant and the one or more alarm limits for the one or more KPIs of the plurality of KPIs associated with the respective plant.
7 . The method of claim 6 , wherein each of the plant state forecast models is trained to forecast the plant state of the respective plant based at least in part on the received historical values for the KPIs that are associated with the respective plant, the one or more alarm limits for the one or more KPIs of the plurality of KPIs associated with the respective plant, and one or more of the forecasted KPI values for one or more of the plurality of KPIs that are associated with the respective plant.
8 . The method of claim 1 , further comprising: training the alarm forecast model for the one or more KPIs of the plurality of KPIs, wherein the alarm forecast model is trained based on the received historical values for the one or more KPIs of the plurality of KPIs, the historical alarms and/or the events associated with one or more KPIs of the plurality of KPIs, and one or more of the alarm limits for one or more KPIs of the plurality of KPIs.
9 . The method of claim 8 , wherein the alarm forecast model is also trained based on one or more of the forecasted KPI values.
10 . The method of claim 8 , further comprising: generating a forecasted alarm for each of the one or more KPIs of the plurality of KPIs based the forecasted KPI value for the respective KPI.
11 . The method of claim 8 , wherein each of the historical alarms and/or the events associated with the one or more KPIs of the plurality of KPIs identify a source of the historical alarm and/or the event, a category of the historical alarm and/or the event and a condition of the historical alarm and/or the event, and wherein the alarm forecast model is trained based on the source, the category and/or the condition of one or more of the historical alarms and/or the events associated with the one or more KPIs of the plurality of KPIs of the industrial process.
12 . A system for predicting predetermined Key Performance Indicators (KPIs) of an industrial process, the system comprising: an I/O port; a memory; a controller operatively coupled to the I/O port and the memory, the controller configured to: receive, via the I/O port, a plurality of tags, wherein each tag identify a corresponding KPI of the industrial process; receive, via the I/O port, historical values for each of the KPIs that are identified by the plurality of tags; train a KPI forecast model for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for one or more KPIs of a plurality of KPIs that are identified by the plurality of tags; and generate a forecasted KPI value for each of the plurality of KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI; receive one or more alarm limits, historical alarms, and/or events associated with the one or more KPIs of the plurality of KPIs; identify one or more overlapping episodes associated with the one or more KPIs of the plurality of KPIs and related to the historical alarms and/or the events; exclude, the one or more overlapping episodes of the historical alarms and/or the events that are associated with the one or more KPIs of the plurality of KPIs; training, by the controller, an alarm forecast model based on at least on the events associated with the one or more KPIs of the plurality of KPIs exclusive of the historical alarms and/or the events related to the one or more overlapping episodes of the historical alarms and/or the events; and determine, using the trained alarm forecast model whether the forecasted KPI value is expected to fall outside the corresponding alarm limit; forecast, an alarm for each of the one or more KPIs based on the trained alarm forecast model, wherein the alarm forecasted by the trained alarm forecast model excludes the one or more overlapping episodes; and automatically adjust, one or more parameters associated with the industrial process based on the forecasted KPI values and the forecasted alarms, wherein the one or more parameters associated with the industrial process are adjusted to negate the forecasted alarm.
13 . The system of claim 12 , wherein each of the KPI forecast models is trained based at least in part on the received historical values for the one or more KPIs of the plurality of KPIs of the industrial process that are identified by the plurality of tags and one or more of the forecasted KPI values.
14 . The system of claim 12 , wherein the industrial process comprises a plurality of plants, wherein each plant comprising two or more tags of the plurality of tags that each identify a corresponding KPI of the corresponding plant, and each plant has two or more predetermined plant states, and the controller is configured to: train a plant state forecast model for each of the plurality of plants, wherein each of the plant state forecast models is trained to forecast the plant state of the respective plant based at least in part on the received historical values for the corresponding KPIs that are associated with the respective plant; and generate a forecasted plant state for at least one of the plurality of plants of the industrial process based at least in part on the plant state forecast model that corresponds to the respective plant.
15 . The system of claim 12 , wherein the controller is configured to: train the alarm forecast model for the one or more KPIs of the plurality of KPIs, wherein the alarm forecast model is trained based on the received historical values for the one or more KPIs of the plurality of KPIs, the historical alarms and/or the events associated with the one or more KPIs of the plurality of KPIs, and one or more of the alarm limits for the one or more KPIs of the KPIs.
16 . A non-transitory computer readably medium storing instructions that when executed by one or more processors causes the one or more processors to: receive a plurality of tags wherein each tag identify a corresponding KPI of an industrial process; receive historical values for each of the KPIs that are identified by the plurality of tags; train a KPI forecast model for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for one or more KPIs of a plurality of KPIs that are identified by the plurality of tags; and generate a forecasted KPI value for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI; receive one or more alarm limits, historical alarms, and/or events associated with the one or more KPIs of the plurality of KPIs; identify one or more overlapping episodes associated with the one or more KPIs of the plurality of KPIs and related to the historical alarms and/or the events; exclude, the one or more overlapping episodes of the historical alarms and/or the events that are associated with the one or more KPIs of the plurality of KPIs; train, by the controller, an alarm forecast model based on at least on the events associated with the one or more KPIs of the plurality of KPIs exclusive of the historical alarms and/or the events related to the one or more overlapping episodes of the historical alarms and/or the events; and determine, using the trained alarm forecast model whether the forecasted KPI value is expected to fall outside the corresponding alarm limit; forecast, an alarm for each of the one or more KPIs based on the trained alarm forecast model, wherein the alarm forecasted by the trained alarm forecast model excludes the one or more overlapping episodes; and automatically adjust, one or more parameters associated with the industrial process based on the forecasted KPI values and the forecasted alarms, wherein the one or more parameters associated with the industrial process are adjusted to negate the forecasted alarm.

Description

TECHNICAL FIELD The present disclosure relates to industrial processes and more particularly to methods and systems for predicting KPI values, plant states and/or alarms in an industrial process. BACKGROUND A wide range of industrial processes are known. Many industrial processes are large, complex processes that can be difficult to monitor and control, in part because an industrial process may involve a large number of different equipment, each of which may generate large volumes of operational data. As an example, in a complex process plant such as a refinery or a petrochemical complex, an operator may be responsible for monitoring thousands of parameters such as pressures, levels, flows, and temperatures. Current automation systems are not able to take all necessary actions when deviations occur, and human interaction is needed. Experienced operators with years of service can often manage an industrial process fairly well by taking appropriately timed control actions when deviations occur. However, some operators may be inexperienced, and may not have the experience in dealing with each possible deviation that may occur. Taking an inappropriate action may cause the deviation to become worse, or may cause deviations in other parameters. What would be desirable are methods and systems for monitoring and controlling industrial processes. What would be desirable are methods and systems for predicting KPI values, plant states and/or alarms in an industrial process to aid an operator in taking appropriately timed actions to properly manage the industrial process. SUMMARY The present disclosure relates to industrial processes and more particularly to methods and systems for predicting KPI values, plant states and/or alarms in an industrial process. An example may be found in a method for predicting predetermined Key Performance Indicators (KPIs) of an industrial process. The illustrative method includes receiving a plurality of tags that each identify a corresponding KPI of the industrial process and receiving historical values for those KPIs that are identified by the plurality of tags. A KPI forecast model is trained for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. A forecasted KPI value is generated for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI. Another example may be found in a system for predicting predetermined Key Performance Indicators (KPIs) of an industrial process. The system includes an I/O port, a memory, and a controller operatively coupled to the I/O port and the memory. The controller is configured to receive via the I/O port a plurality of tags that each identify a corresponding KPI of the industrial process and to receive via the I/O port historical values for those KPIs that are identified by the plurality of tags. The controller is configured to train a KPI forecast model for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. The controller is configured to generate a forecasted KPI value for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI. Another example may be found in a non-transitory computer readably medium storing instructions that when executed by one or more processors causes the one or more processors to receive a plurality of tags that each identify a corresponding KPI of an industrial process and to receive historical values for those KPIs that are identified by the plurality of tags. The one or more processors are caused to train a KPI forecast model for each of the KPIs that are identified by the plurality of tags, wherein each of the KPI forecast models is trained based at least in part on the received historical values for at least some of the KPIs that are identified by the plurality of tags. The one or more processors are caused to generate a forecasted KPI value for each of the KPIs identified by the plurality of tags based at least in part on the corresponding KPI forecast model that corresponds to the respective KPI. The preceding summary is provided to facilitate an understanding of some of the innovative features unique to the present disclosure and is not intended to be a full description. A full appreciation of the disclosure can be gained by taking the entire specification, claims, figures, and abstract as a whole. BRIEF DESCRIPTION OF THE FIGURES The disclosure may be more completely understood in consideration of the following description of various examples