Abstract:
Disclosed herein are systems, devices, and methods for sharing asset-related information between data platforms that are communicatively coupled via a network. According to an example, a first platform may receive asset-related data and determine that a portion of the received data should be pushed to another platform. Based on that determination, the first platform may prepare a portion of the received data to be transmitted to another platform and then push the portion of the data to another platform over a network connection. In addition, the first platform may be governed by a second platform (e.g., a master or seed platform). According to an example, the first platform may receive from the second platform, and then apply, criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network.
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. Additionally, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.
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.
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.
Abstract:
Disclosed herein are systems, devices, and methods for governing data platforms that are communicatively coupled via a network. According to an example, a first platform may receive, from a second platform (e.g., a master or seed platform), criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network and assess the reliability of the platform's stored asset-related data. In turn, the first platform may apply the criteria to the assessed reliability to determine whether the first platform is permitted to share asset-related data with the one or more other platforms and then operate in accordance with that determination.
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. Additionally, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.