Abstract:
Among other things, one or more techniques and/or systems are provided for developing a health profile of an industrial asset based upon data pertaining to such an industrial asset. At least some of the data is transformed into primary state indicators, respectively representative of the status or condition of an aspect of the industrial asset. Using the primary state indicators, one or more events that are likely to occur to the industrial asset are identified and a health profile is developed based upon such events. The health profile may describe maintenance actions that will reduce a likelihood of an event(s) occurring, may describe a business impact on an entity associated with the industrial asset if an event(s) occurs, and/or may describe a performance impact on the industrial asset if an event(s) occurs, for example.
Abstract:
Among other things, one or more techniques and/or systems are provided for developing a health profile of an industrial asset based upon data pertaining to such an industrial asset. At least some of the data is transformed into primary state indicators, respectively representative of the status or condition of an aspect of the industrial asset. Using the primary state indicators, one or more events that are likely to occur to the industrial asset are identified and a health profile is developed based upon such events. The health profile may describe maintenance actions that will reduce a likelihood of an event(s) occurring, may describe a business impact on an entity associated with the industrial asset if an event(s) occurs, and/or may describe a performance impact on the industrial asset if an event(s) occurs, for example.
Abstract:
Among other things, one or more techniques and/or systems are provided for facilitating development, by a user, of a model describing a condition of an industrial asset. An output parameter of the model can be defined within a model development environment. The output parameter describes a condition of the industrial asset and can be defined using one or more variables. During deployment of the model within a deployment environment, respective variables of the model can be mapped to a data store where data associated with the variable is located. In this way, during development, a user can specify variables to be used within the model without specifying where to fetch and/or push data associated with the variable, for example. Further, the model can be executed within the deployment environment to assess the condition for a plurality of industrial assets.
Abstract:
Among other things, one or more techniques and/or systems are provided for developing a timeline chronicling events pertaining to an industrial asset. Data is received from a plurality of assets, processed (e.g., to reduce duplicative and/or redundant data), and organized chronologically for presentation in a timeline. The data is further grouped and/or prioritized to display some portions of the data more prominently relative to other portions of the data in the timeline (e.g., which may be hidden). Grouping rules and/or prioritization rules for grouping and/or prioritizing the data may be a function of user interaction with the timeline and/or a function of a machine learning algorithm which may be configured to identify patterns in how users interact with the timeline based upon, among other things, a role the user plays relative to the industrial asset and/or an operating state of the industrial asset.
Abstract:
Among other things, one or more techniques and/or systems are provided for developing a timeline chronicling events pertaining to an industrial asset. Data is received from a plurality of assets, processed (e.g., to reduce duplicative and/or redundant data), and organized chronologically for presentation in a timeline. The data is further grouped and/or prioritized to display some portions of the data more prominently relative to other portions of the data in the timeline (e.g., which may be hidden). Grouping rules and/or prioritization rules for grouping and/or prioritizing the data may be a function of user interaction with the timeline and/or a function of a machine learning algorithm which may be configured to identify patterns in how users interact with the timeline based upon, among other things, a role the user plays relative to the industrial asset and/or an operating state of the industrial asset.
Abstract:
Among other things, one or more techniques and/or systems are provided for facilitating development, by a user, of a model describing a condition of an industrial asset. An output parameter of the model can be defined within a model development environment. The output parameter describes a condition of the industrial asset and can be defined using one or more variables. During deployment of the model within a deployment environment, respective variables of the model can be mapped to a data store where data associated with the variable is located. In this way, during development, a user can specify variables to be used within the model without specifying where to fetch and/or push data associated with the variable, for example. Further, the model can be executed within the deployment environment to assess the condition for a plurality of industrial assets.