Abstract: Among other things, one or more techniques and/or systems are provided for developing a criticality profile of an industrial asset. The criticality profile describes the importance of the industrial asset to a system in terms of one or more criticality metrics and/or one or more confidence profiles. Such metrics may include an operational metric indicative of the operation impact of the industrial asset on a system if the industrial asset remains unchanged or enters a degraded or improved state, a restoration metric indicative of a complexity of restoring the industrial asset to an operational state from the unchanged or degraded state and/or complexity of improving the industrial asset to an improved state, and/or an interdependency metric indicative of a relationship between the industrial asset and one or more other industrial assets and/or between the industrial asset and one or more structures in an environment associated with the industrial asset.

Abstract: Among other things, one or more techniques and/or systems are provided for updating a model for generating a health profile of an industrial asset based upon data pertaining to the industrial asset. The health profile comprises a maintenance plan(s), which respectively comprise one or more recommended maintenance actions to be performed with respect to the industrial asset during a prediction period. The model may be periodically and/or intermittently updated based upon a comparison between the recommended maintenance actions and actual events of the industrial asset during the prediction period. Moreover, in some embodiments, different models may be selected for generating future health profiles of the industrial asset based upon a comparison between the recommended maintenance actions and actual events of the industrial asset during the prediction period.

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 identifying event images captured during an event associated with power equipment, such as a maintenance action. An entity, such as a maintenance technician, is equipped with an image recorder and an audio recorder. In some embodiments, the image recorder and/or audio recorder are wearable, such as mounted to eyewear and/or headwear worn by the entity. Audio that is recorded by the audio recorder is processed to identify events (e.g., key actions in a maintenance procedure). Based upon the identification of an event from the audio, event images (e.g., images captured temporally proximate to when the event was identified in the audio) associated with the event are identified and stored in a library. In some embodiments, one or more of such event images are later displayed to entities via a heads-up display, for example.

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: A preferred method for evaluating a transformer design using data representing test results from a plurality of transformers and stored in a data base includes comparing the data representing test results to predetermined criteria for the test results to determine whether the test results satisfy the predetermined criteria. The preferred method also includes counting the number of the test results that do not satisfy the predetermined criteria, and generating an indication that a predetermined quantity of the test results do not satisfy the predetermined criteria if at least the predetermined quantity of the test results do not satisfy the predetermined criteria.

Abstract: A method for transformer testing includes receiving a failing test result of a transformer, the test result being determined from a test taken during transformer manufacture, determining, via a knowledge-based system, a predicted root cause of the failure based on the test result and a knowledge base of transformer information, and determining, via the knowledge-based system, a suggested course of action for the failure based on the test result and the knowledge base of transformer information.