Systems, methods, and devices for asset simulation and analytics

1 . A method, comprising:

identifying a target value of one or more first operational parameters associated with an asset, wherein the asset is associated with at least one industrial process;

receiving a first data set comprising historical data associated with the asset and a second data set comprising real time data associated with the asset;

comparing the first data set and the second data set;

periodically collecting real time data from the asset at predetermined intervals of time based on the comparing of the first data set and the second data set;

providing the target value of the one or more first operational parameters and the periodically collected real time data to a machine learning model;

incrementally retraining the machine learning model using the periodically collected real time data;

receiving, in response to the machine learning model processing the target value of the one or more first operational parameters, a second target value of one or more second operational parameters associated with the asset;

simulating one or more operations of the asset using the second target value of the one or more second operational parameters; and

providing, via a user interface, one or more recommended actions in response to a result associated with simulating the one or more operations.

2 . The method of claim 1 , wherein the machine learning model provides the second target value of the one or more second operational parameters based on a correlation between a plurality of operational parameters of the asset, wherein the plurality of operational parameters comprise the one or more first operational parameters and the one or more second operational parameters.

3 . The method of claim 2 , wherein the correlation between the plurality of operational parameters of the asset comprises:

a first correlation between the one or more first operational parameters and the one or more second operational parameters; and

a second correlation between one or more third operational parameters associated with the asset and the one or more second operational parameters.

4 . The method of claim 2 , wherein the correlation is based on:

a first weighted impact of the one or more first operational parameters with respect to the correlation; and

a second weighted impact of the one or more second operational parameters with respect to the correlation,

wherein the first weighted impact is different from the second weighted impact.

5 . The method of claim 1 , further comprising:

training the machine learning model based on a training dataset that comprises second historical data associated with the at least one industrial process, the asset, or both, wherein training the machine learning model comprises determining a correlation between the one or more first operational parameters and the one or more second operational parameters.

6 . The method of claim 5 , wherein:

the asset comprises a chiller; and

the second historical data is associated with at least one of: power consumption, water supply temperature, water return temperature, and water flow associated with the chiller.

7 . The method of claim 5 , wherein:

the asset comprises a pump; and

the second historical data is associated with at least one of: power consumption, discharge pressure, discharge flow, and wet well level associated with the pump.

8 . The method of claim 1 ,

wherein incrementally retraining the machine learning model comprises updating a correlation between the one or more first operational parameters and the one or more second operational parameters.

9 . The method of claim 8 , further comprising:

predicting an event associated with the asset based on the periodically collected real time data and based on updating the correlation; and

displaying temporal information associated with the event and the asset via the user interface.

10 . The method of claim 1 , wherein the one or more first operational parameters are associated with energy consumption of the asset.

11 . The method of claim 1 , wherein the one or more first operational parameters comprise at least one of:

a cooling load associated with the asset; and

a discharge flow rate associated with the asset.

12 . The method of claim 1 , further comprising:

obtaining second real time data associated with the asset in response to comparing a prediction accuracy of the machine learning model with respect to detecting one or more anomalies associated with the asset and a target prediction accuracy.

13 . The method of claim 1 , wherein providing the one or more recommended actions comprises displaying, via the user interface, a graph indicating one or more predictions associated with the asset.

14 . The method of claim 1 , further comprising:

automatically or semi-automatically controlling the one or more operations of the asset in response to receiving the one or more recommended actions,

wherein automatically or semi-automatically controlling the one or more operations of the asset comprises using the second target value of the one or more second operational parameters.

15 . The method of claim 1 , wherein the machine learning model comprises a prediction model, and wherein incrementally retraining the machine learning model using the periodically collected real time data includes consistently adjusting the prediction model in association with achieving a target prediction accuracy.

16 . The method of claim 1 , wherein incrementally retraining the machine learning model using the periodically collected real time data comprises configuring one or more trigger criteria that include a temporal variable in association with the retraining, and wherein the machine learning model is incrementally retrained based on a schedule set by a schedule determination module.

17 . The method of claim 1 , wherein periodically collecting the real time data from the asset comprises collecting the real time data from a data platform, and wherein the data platform is integrated with a module comprising the machine learning model to reduce a time for secure information exchange between the data platform and the module.

18 . A system, comprising:

a graphical user interface;

a processor; and

a memory storing data thereon that, when processed by the processor, cause the processor to:

identify a target value of one or more first operational parameters associated with an asset, wherein the asset is associated with at least one industrial process;

receive a first data set comprising historical data associated with the asset and a second data set comprising real time data associated with the asset;

compare the first data set and the second data set;

periodically collect real time data from the asset at predetermined intervals of time based on the comparing of the first data set and the second data set;

provide the target value of the one or more first operational parameters and the periodically collected real time data to a machine learning model, wherein the machine learning model is incrementally retrained using the periodically collected real time data;

receive, in response to the machine learning model processing the target value of the one or more first operational parameters, a second target value of one or more second operational parameters associated with the asset;

simulate one or more operations of the asset using the second target value of the one or more second operational parameters; and

provide, via a user interface, one or more recommended actions in response to a result associated with simulating the one or more operations.

19 . An asset health management system, comprising:

asset management circuitry that is to aggregate real time data associated with an asset, wherein the asset is associated with at least one industrial process; and

analytics circuitry that is to:

identify a target value of one or more first operational parameters associated with the asset;

receive a first data set comprising historical data associated with the asset and a second data set comprising real time data associated with the asset;

compare the first data set and the second data set;

periodically collect real time data from the asset at predetermined intervals of time based on the comparing of the first data set and the second data set;

update a machine learning model based on a training dataset that comprises at least a portion of the periodically collected real time data, wherein updating the machine learning model comprises updating a correlation between the one or more first operational parameters associated with the asset and one or more second operational parameters associated with the asset;

provide the target value of the one or more first operational parameters to the machine learning model, wherein the machine learning model provides a second target value of the one or more second operational parameters in response to processing the target value of the one or more first operational parameters; and

simulate one or more operations of the asset using the second target value of the one or more second operational parameters,

wherein the asset management circuitry provides, via dashboard management circuitry, one or more recommended actions in response to a result associated with simulating the one or more operations.

20 . The asset health management system of claim 19 , wherein:

the dashboard management circuitry establishes a connection with the asset management circuitry in response to receiving a request for the periodically collected real time data associated with the asset; and

the asset management circuitry retrieves the periodically collected real time data in response to the establishment of the connection, wherein retrieving the periodically collected real time data is based on a mapping between a representation of the asset at a dashboard interface and a representation of the asset at the asset management circuitry.