Propagating authored packages to asset management platforms

A computer system that uses a machine learning (ML) model to predict a state of an asset, the computer system comprising:

• a processor system;
• and
• a storage system that stores instructions that are executable by the processor system to cause the computer system to:
• use bitemporal modeling to track status information of an asset, wherein the status information includes at least one of an uptime or a downtime of the asset, and wherein the bitemporal modeling includes a first record associated with a current state of the asset's status information and a second record associated with the asset's status information at a selected point in time earlier than the current state;
• feed the tracked status information used by the bitemporal modeling to the ML model, which is trained to detect anomalies with respect to at least one of the uptime or the downtime of the asset;
• cause the ML model to generate a prediction regarding a future uptime or a future downtime of the asset, the future uptime or the future downtime being predicted for a given range of time that is to subsequently transpire;
• subsequently, collect data reflecting an actual uptime or an actual downtime of the asset during the given range of time;
• compare the ML model's prediction of the future uptime or the future downtime of the asset with the data reflecting the actual uptime or the actual downtime of the asset during the given range of time;
• further train ML model based on said comparison, such that the ML model is further instructed to correct for discrepancies between the ML model's prediction of the future uptime or the future downtime of the asset and the data reflecting the actual uptime or the actual downtime of the asset;
• using the trained ML model to identify at least one of:
• a trend and a pattern;
• and
• generating an updated package based on the identified at least one of:
• a trend and a pattern, wherein the updated package includes updated information on at least one of:
• operation, maintenance, and use of an asset;
• and
• causing the updated package to be installed on the asset to change operational behavior of the asset to reflect contents of the updated package.

A method for using a machine learning (ML) model to predict a state of an asset, the method comprising:

• using bitemporal modeling to track status information of an asset, wherein the status information includes at least one of an uptime or a downtime of the asset, and wherein the bitemporal modeling includes a first record associated with a current state of the asset's status information and a second record associated with the asset's status information at a selected point in time earlier than the current state;
• feeding the tracked status information used by the bitemporal modeling to an ML model, wherein the ML model, during a first training stage, is trained to detect anomalies with respect to at least one of the uptime or the downtime of the asset;
• causing the ML model to generate a prediction regarding a future uptime or a future downtime of the asset, the future uptime or the future downtime being predicted for a given range of time that is to subsequently transpire;
• subsequently during the given range of time, collecting data reflecting an actual uptime or an actual downtime of the asset during the given range of time, wherein the collected data includes sensor data obtained for the asset;
• comparing the ML model's prediction of the future uptime or the future downtime of the asset with the data reflecting the actual uptime or the actual downtime of the asset during the given range of time, wherein said comparing is performed using a chart that identifies deviations between the actual uptime or the actual downtime and the predicted further uptime or the predicted further downtime;
• further training the ML model based on said comparison, such that the ML model is subjected to a second training stage to correct for the deviations between the ML model's prediction of the future uptime or the future downtime of the asset and the data reflecting the actual uptime or the actual downtime of the asset;
• using the trained ML model to identify at least one of:
• a trend and a pattern;
• and
• generating an updated package based on the identified at least one of:
• a trend and a pattern, wherein the updated package includes updated information on at least one of:
• operation, maintenance, and use of an asset;
• and
• causing the updated package to be installed on the asset to change operational behavior of the asset to reflect contents of the updated package.