Abstract: An indexer retrieves a set of messages generated by a given data source of a plurality of data sources from a data storage. The indexer also processes the set of messages to generate processed data for the given data source and stores the processed data in a cache. The processed data includes a data file with data from the given data source and an index file identifying a location of the data file. The indexer determines when the processed data for the given generation source reaches a threshold size. The indexer also stores the data file of the processed data in the data storage and updates the index file for the processed data to identify the data storage as the location of the data file.

Abstract: In several aspects, a processor receives a set of parameters characterizing sensor data from a plurality of sensors corresponding to drilling operations of a drill assembly for boring the Earth, and a condition of the drill assembly. The processor selects, by a drill assembly machine learning model executed by the processor, a subset of parameters that are related to the condition of the drill assembly. The drill assembly machine learning model uses an historical record of the set of parameters and a set of conditions of the drill assembly collected over time to determine whether a parameter is related to each condition. The processor applies weights to each parameter of the subset of parameters and weights to different levels of each parameter of the subset of parameters, producing a weighted subset of parameters and outputting the weighted subset of parameters.

Abstract: A system for guiding a drill assembly boring the Earth can include a drill controller engine that receives parameters characterizing sensor data from a plurality of sensors of the drill assembly, and a condition of the drill assembly. The parameters and condition can be aggregated into a historical record. A subset of the parameters can be selected based on a relationship between the subset of parameters and the condition of the drill assembly, the relationship being determined by a drill assembly machine learning model. A Damage Index (DI) can be calculated from the subset of parameters. The DI can be matched with a plurality of DIs computed for the historical record to determine a risk of failure of the drill assembly. The drilling operations of the drill assembly can be adjusted by the drill controller engine in response to the risk of failure.

Abstract: A system for guiding a drill assembly boring the Earth can include a drill controller engine that receives parameters characterizing sensor data from a plurality of sensors of the drill assembly, and a condition of the drill assembly. The parameters and condition can be aggregated into a historical record. A subset of the parameters can be selected based on a relationship between the subset of parameters and the condition of the drill assembly, the relationship being determined by a drill assembly machine learning model. A Damage Index (DI) can be calculated from the subset of parameters. The DI can be matched with a plurality of DIs computed for the historical record to determine a risk of failure of the drill assembly. The drilling operations of the drill assembly can be adjusted by the drill controller engine in response to the risk of failure.

Abstract: An indexer retrieves a set of messages generated by a given data source of a plurality of data sources from a data storage. The indexer also processes the set of messages to generate processed data for the given data source and stores the processed data in a cache. The processed data includes a data file with data from the given data source and an index file identifying a location of the data file. The indexer determines when the processed data for the given generation source reaches a threshold size. The indexer also stores the data file of the processed data in the data storage and updates the index file for the processed data to identify the data storage as the location of the data file.

Abstract: A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

Abstract: Systems and methods for monitoring an operational system. An initial set of sensor data is accumulated from a system over a substantially shorter time than is required to collect data to characterize a regression model for an operating parameter of the system. An initial regression model is created based on the initial set of sensor data. A subsequent set of sensor data is received from the at least one sensor after creating the initial regression model. An expected dependent value for the subsequent independent value is determined using the initial regression model. An operator is prompted to update the initial regression model based on a difference between a subsequent dependent value and the expected dependent value. The initial regression model is updated to incorporate the subsequent set of sensor data. A notification is provided based on a difference between presently received sensor data and the updated regression model.

Abstract: A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

Abstract: A system for generating power includes an environmental engine operating on one or more computing devices that determines a Reynolds number for a wind turbine, wherein the Reynolds number characterizes wind flowing over a blade of the wind turbine that varies based on the wind speed, a rotor speed and characteristics of the blade of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine, the Reynolds number, wind speed and turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric; and simulates execution of the selected model to determine recommended operating parameters.

Abstract: A defect detector can receive measured data characterizing an operational performance of a population of sets of parallel operating power sources of a power supplier for a power distribution system. The defect detector can employ statistical analysis to detect a potential defect in a subset of the parallel operating power sources of the power supplier based on the measured data. Each of the parallel operating power sources can be configured to provide a given output in response to a given input.

Abstract: A system for generating power includes an environmental engine operating on one or more computing devices that determines a Reynolds number for a wind turbine, wherein the Reynolds number characterizes wind flowing over a blade of the wind turbine that varies based on the wind speed, a rotor speed and characteristics of the blade of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine, the Reynolds number, wind speed and turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric; and simulates execution of the selected model to determine recommended operating parameters.

Abstract: Systems and methods for monitoring an operational system. An initial set of sensor data is accumulated from a system over a substantially shorter time than is required to collect data to characterize a regression model for an operating parameter of the system. An initial regression model is created based on the initial set of sensor data. A subsequent set of sensor data is received from the at least one sensor after creating the initial regression model. An expected dependent value for the subsequent independent value is determined using the initial regression model. An operator is prompted to update the initial regression model based on a difference between a subsequent dependent value and the expected dependent value. The initial regression model is updated to incorporate the subsequent set of sensor data. A notification is provided based on a difference between presently received sensor data and the updated regression model.

Abstract: A defect detector can receive measured data characterizing an operational performance of a population of sets of parallel operating power sources of a power supplier for a power distribution system. The defect detector can employ statistical analysis to detect a potential defect in a subset of the parallel operating power sources of the power supplier based on the measured data. Each of the parallel operating power sources can be configured to provide a given output in response to a given input.