Abstract: Systems and methods for monitoring an operator of an asset are described herein. The method includes receiving training data, the training data comprising training sensor data associated with one or more tasks performed by a plurality of operators of different skill levels and under different performance impairments. The method can also include training a machine learning model to recognize one or more operator conditions based on the received training data and receiving sensor data from a plurality of sensors associated with the asset or the operator. The method can further include determining an operator condition of the operator based on the received sensor data and the machine learning model and taking one or more actions in response to the determined operator condition.

Abstract: Systems and methods for improving recordkeeping and analysis of an asset include creating and maintaining an integrated record about the asset. In some embodiments, the systems and methods include collecting data about an asset to form an asset data collection, recording the asset data collection in a record, analyzing at least a portion of the asset data collection to determine a characteristic of the asset, and recording the characteristic of the asset in the record. In some embodiments, recording the characteristic in the record includes adding the characteristic to the asset data collection.

Abstract: Systems and methods for monitoring an operator of an asset are described herein. The method includes receiving training data, the training data comprising training sensor data associated with one or more tasks performed by a plurality of operators of different skill levels and under different performance impairments. The method can also include training a machine learning model to recognize one or more operator conditions based on the received training data and receiving sensor data from a plurality of sensors associated with the asset or the operator. The method can further include determining an operator condition of the operator based on the received sensor data and the machine learning model and taking one or more actions in response to the determined operator condition.

Abstract: Systems and methods for scheduling maintenance of equipment include collecting pre-emptive maintenance data associated with a pre-emptive maintenance task associated with a component of equipment, collecting preventive maintenance data associated with a scheduled preventive maintenance task associated with the component or another component, applying a model to predict a failure time value associated with a future failure of the component, analyzing the data and the time value to determine an optimal time to perform a maintenance event, and using that optimal time to schedule the next maintenance event. Determining the optimal time includes minimizing a total cost of the maintenance.

Abstract: The present technology relates to systems and methods for calculating vehicle longevity. The methods use steps that are repeatable and thus the results of the methods are objective can include projections regarding how long a longevity claim can be supported into the future.

Abstract: Disclosed is a system and method for managing operations of a system. The method includes partitioning the system into a plurality of subsystems. The method further includes, for each subsystem, creating an agent to associate with the subsystem, providing to the agent one or more goals for the subsystem, and providing to the agent an overall objective function for the system. The method also includes using an optimization algorithm involving the agents to manage system operation based on the goals and the overall objective function. The system includes an agent module for providing a set of agents associated with the subsystems, a throughput target module for providing a throughput target for each agent, an objective module for providing, for each agent, a common production throughput objective.

Abstract: Disclosed is a system and method for managing operations of a system. The method includes partitioning the system into a plurality of subsystems. The method further includes, for each subsystem, creating an agent to associate with the subsystem, providing to the agent one or more goals for the subsystem, and providing to the agent an overall objective function for the system. The method also includes using an optimization algorithm involving the agents to manage system operation based on the goals and the overall objective function. The system includes an agent module for providing a set of agents associated with the subsystems, a throughput target module for providing a throughput target for each agent, an objective module for providing, for each agent, a common production throughput objective.

Abstract: Disclosed is a system and method for managing operations of a system. The method includes partitioning the system into a plurality of subsystems. The method further includes, for each subsystem, creating an agent to associate with the subsystem, providing to the agent one or more goals for the subsystem, and providing to the agent an overall objective function for the system. The method also includes using an optimization algorithm involving the agents to manage system operation based on the goals and the overall objective function. The system includes an agent module for providing a set of agents associated with the subsystems, a throughput target module for providing a throughput target for each agent, an objective module for providing, for each agent, a common production throughput objective, and a sampled fictitious play module for providing instructions of a sampled fictitious play algorithm.

Abstract: A process for analyzing a sub-model associated with a full system model as often depicted as an influence diagram. The process includes defining the sub-model as a collection of entities where some entities are calculation entities and some are data entities. The process converts the calculation entities in the sub-model that depend on entities in the fall model outside of the sub-model to temporary data entities. Further, the process deletes any data entity in the sub-model from which any converted temporary data entity depends. The process also determines if there are any isolated cycles in the sub-model, and, if so, which of the entities in the isolated cycle will be an output entity.

Abstract: A method for determining whether to provide automatic reset instructions to a machine with reset capability with respect to a fault code event, the method comprising, in a database, collecting records comprising the identification of the machine, a fault code associated therewith and the duration of a fault associated with the fault code that the machine experienced within a time period, and performing a statistical analysis on the records to analyze whether executing an automatic reset on the machine with respect to the fault code will probabilistically rectify the fault and to generate resultant data.

Abstract: A system, method, and apparatus are disclosed for managing utility consumption. An embodiment is disclosed for reducing peak demand for a utility by staggering requests for that utility. Another embodiment is disclosed in which sensors monitor utility consumption throughout a facility. Those items of equipment not in active use, but still drawing utility resources, are shut down after a predetermined interval. Another embodiment is disclosed in which storage and conversion technologies are used to offset peak demand and the associated peak demand costs.

Abstract: A method for providing a functional visualization of a spreadsheet. The method includes correlating spreadsheet cells with both data entities and calculation entities in an influence diagram, and then automatically updating the entities in the influence diagram in response to changes made to the spreadsheet, or automatically updating the spreadsheet in response to changes made to the entities in the influence diagram. The method includes identifying one or more cells in a spreadsheet as a data cell or a calculation cell, and then identifying corresponding data entities and calculation entities in the influence diagram. The entities in the influence diagram can have predetermined attributes (color, shape, images, etc.) depending on their function.

Abstract: A data analysis tool that provides a technique for visualizing changes of a system model. The analysis tool includes constructing an influence diagram consisting of a series of calculation entities and data entities interconnected by arrows, where the entities correspond to parameters of a system model being analyzed. Input values are assigned to each of the data entities for a particular scenario, and the corresponding values are computed for the calculation entities. A new scenario is implemented by changing one or more of the input values to the data entities, thus providing different results for some or all of the calculation entities. A mathematical function is applied to each entity to modify its appearance based on the values of the entities for current or other scenarios. Changes to each entity from the input of the new scenario can be in a variety of forms, such as size of the entity, color of the entity, shading of the entity, etc.

Abstract: A method for providing a functional visualization of a spreadsheet. The method includes correlating spreadsheet cells with both data entities and calculation entities in an influence diagram, and then automatically updating the entities in the influence diagram in response to changes made to the spreadsheet, or automatically updating the spreadsheet in response to changes made to the entities in the influence diagram. The method includes identifying one or more cells in a spreadsheet as a data cell or a calculation cell, and then identifying corresponding data entities and calculation entities in the influence diagram. The entities in the influence diagram can have predetermined attributes (color, shape, images, etc.) depending on their function.

Abstract: A process for analyzing a sub-model associated with a full system model as often depicted as an influence diagram. The process includes defining the sub-model as a collection of entities where some entities are calculation entities and some are data entities. The process converts the calculation entities in the sub-model that depend on entities in the fall model outside of the sub-model to temporary data entities. Further, the process deletes any data entity in the sub-model from which any converted temporary data entity depends. The process also determines if there are any isolated cycles in the sub-model, and, if so, which of the entities in the isolated cycle will be an output entity.

Abstract: A data analysis tool that provides a technique for visualizing changes of a system model. The analysis tool includes constructing an influence diagram consisting of a series of calculation entities and data entities interconnected by arrows, where the entities correspond to parameters of a system model being analyzed. Input values are assigned to each of the data entities for a particular scenario, and the corresponding values are computed for the calculation entities. A new scenario is implemented by changing one or more of the input values to the data entities, thus providing different results for some or all of the calculation entities. A mathematical function is applied to each entity to modify its appearance based on the values of the entities for current or other scenarios. Changes to each entity from the input of the new scenario can be in a variety of forms, such as size of the entity, color of the entity, shading of the entity, etc.