Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: A computing system may be configured to monitor the operation of a plurality of nodes in a manufacturing network that comprises a plurality of edge nodes, a plurality of intermediate nodes, and a root node. While monitoring the operation of the plurality of nodes, the computing system may identify a given time at which at least one node in the manufacturing network satisfies node-level threshold criteria indicating anomalous operation of the node and responsively evaluate the operation of the manufacturing network at the given time using one or more of macro-level threshold, micro-level threshold criteria, path-level threshold criteria, and node-level threshold criteria. Based on the evaluation, the computing system may identify an anomaly in the manufacturing network at the given time and then cause a client station to present an alert indicating the anomaly.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.

Abstract: Examples of systems, devices, and methods related to assets and asset operating conditions involve determining health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.