Abstract: Apparatuses, systems, methods, and computer program products are disclosed for adaptive machine learning. An apparatus includes a monitoring module that continuously monitors one or more interactions of a user while the user performs one or more simulated tasks digitally presented to the user that are associated with a learning path. The apparatus includes a metadata module that tracks data describing the user's interactions during the user's performance of one or more simulated tasks. The apparatus includes a machine learning module that, dynamically and in real-time, optimizes the user's learning path by simulating multiple different learning paths using one or more machine learning processes and tracked data. The apparatus includes a recommendation module that presents one or more recommendations to the user for optimizing the user's learning path. One or more recommendations may be generated as a function of the optimized learning path.

Abstract: A method is provided for repair of a mechanical or electromechanical system of a machine. An inference engine receives indication of a failure mode of the system, and measurements of operating conditions of the machine, and the inference engine defines a current problem including (a) the failure mode of the system, and (b) a pattern in the measurements. The inference engine searches a knowledge base with historical problems including (a) failure modes of systems of the machine, and (b) patterns in measurements of the operating conditions, and respective solutions with (c) repair actions performed to address the respective ones of the failure modes, for a respective solution to a historical problem most similar to the current problem. This is inferred as a solution to the current problem, and the inference engine generates an output display indicating the repair action of the solution to address the failure mode of the system.

Abstract: A system for predicting a triggered lightning strike upon an aircraft, which includes an antenna for collecting an RF signal. The system includes one or more processors and a memory coupled to the processor, the memory storing data into a database and program code that, when executed by the one or more processors, causes the system to receive as input a filtered, amplified form of the RF signal collected by the antenna and a non-filtered, amplified form of the RF signal collected by the antenna. The system determines a difference between the filtered, amplified form of the RF signal and the non-filtered and amplified form of the RF signal. The difference is a static signal. The system determines the triggered lightning strike upon the aircraft based on the static signal.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for adaptive machine learning. An apparatus includes a monitoring module that continuously monitors one or more interactions of a user while the user performs one or more simulated tasks digitally presented to the user that are associated with a learning path. The apparatus includes a metadata module that tracks data describing the user's interactions during the user's performance of one or more simulated tasks. The apparatus includes a machine learning module that, dynamically and in real-time, optimizes the user's learning path by simulating multiple different learning paths using one or more machine learning processes and tracked data. The apparatus includes a recommendation module that presents one or more recommendations to the user for optimizing the user's learning path. One or more recommendations may be generated as a function of the optimized learning path.

Abstract: A system for predicting a triggered lightning strike upon an aircraft, which includes an antenna for collecting an RF signal. The system includes one or more processors and a memory coupled to the processor, the memory storing data into a database and program code that, when executed by the one or more processors, causes the system to receive as input a filtered, amplified form of the RF signal collected by the antenna and a non-filtered, amplified form of the RF signal collected by the antenna. The system determines a difference between the filtered, amplified form of the RF signal and the non-filtered and amplified form of the RF signal. The difference is a static signal. The system determines the triggered lightning strike upon the aircraft based on the static signal.

Abstract: A system for assessing effects of lightning strikes upon a specific aircraft based on a plurality of field reports is disclosed. The system includes one or more processors and a memory coupled to the processors, the memory storing data into a database and program code that, when executed by the one or more processors, causes the system to receive as input refined data extracted from the plurality of field reports. The refined data includes text indicating a plurality of lightning strikes upon the specific aircraft and at least a portion of the text is structured into a sentence format. The system parses a unique sentence contained within the refined data to create a dependency parse graph that defines grammatical relationships between at least one word indicating a specific lightning strike upon the specific aircraft with remaining words within the unique sentence. The unique sentence indicates the specific lightning strike.

Abstract: A geometry extraction and analysis (“GEA”) computer device is provided. The GEA computer device includes at least one processor in communication with at least one memory device. The GEA computer device is configured to receive at least one image of at least one view of one or more parts, divide the at least one image into a plurality of segments based on one or more contours contained in the at least one image, identify one or more geometric shapes in the at least one image based on the plurality of segments, identify the one or more parts based on the one or more geometric shapes, and generate a three-dimensional image of the one or more parts based on the one or more geometric shapes.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for adaptive machine learning. An apparatus includes a monitoring module that continuously monitors one or more interactions of a user while the user performs one or more simulated tasks digitally presented to the user that are associated with a learning path. The apparatus includes a metadata module that tracks data describing the user's interactions during the user's performance of one or more simulated tasks. The apparatus includes a machine learning module that, dynamically and in real-time, optimizes the user's learning path by simulating multiple different learning paths using one or more machine learning processes and tracked data. The apparatus includes a recommendation module that presents one or more recommendations to the user for optimizing the user's learning path. One or more recommendations may be generated as a function of the optimized learning path.

Abstract: A processor implemented method for scoring a challenge taken by a user, and training the user using a simulation based learning platform is provided. The processor implemented method includes (i) obtaining, from a database, challenges to be taken by a user and associated at least one of information, (iii) processing a selection of, the challenge from the challenges with actions or steps performed by the user, (iv) rendering, hints to solve the challenge, (v) recording, steps taken by the user to solve the challenge, (vi) comparing the steps taken by the user with steps taken by an expert to solve the challenge to compute a deviance of the user from a reference path, (vii) scoring, the challenge based on the deviance of the user from the reference path to obtain a score, and (viii) notifying, a result associated with the challenge to the user based on the score.

Abstract: A processor implemented method for scoring a challenge taken by a user, and training the user using a simulation based learning platform is provided. The processor implemented method includes (i) obtaining, from a database, challenges to be taken by a user and associated at least one of information, (iii) processing a selection of, the challenge from the challenges with actions or steps performed by the user, (iv) rendering, hints to solve the challenge, (v) recording, steps taken by the user to solve the challenge, (vi) comparing the steps taken by the user with steps taken by an expert to solve the challenge to compute a deviance of the user from a reference path, (vii) scoring, the challenge based on the deviance of the user from the reference path to obtain a score, and (viii) notifying, a result associated with the challenge to the user based on the score.

Abstract: A method is provided for identifying a root cause of a fault in a serviced vehicle based on analytical symptoms. Parameter identification data associated with identified DTCs is retrieved. Parameter identification data from a plurality of vehicles experiencing the DTCs is collected. A first set of diagnostic rules is generated that identify vehicle operating parameters for executing a DTC algorithm or for triggering a DTC. A second set of diagnostic rules is generated that identify vehicle operating parameters used for selecting field failure data obtained when the DTC is triggered. Statistically significant rules are extracted from the second set of diagnostic rules. The first set of rules and the statistically significant rules are cooperatively applied to the parameter identification data for identifying a subset of the parameter identification data that represents anomalies. A subject matter expert analyzes the anomalies for identifying a root cause of the fault.

Abstract: A method is provided for identifying a root cause of a fault in a serviced vehicle based on analytical symptoms. Parameter identification data associated with identified DTCs is retrieved. Parameter identification data from a plurality of vehicles experiencing the DTCs is collected. A first set of diagnostic rules is generated that identify vehicle operating parameters for executing a DTC algorithm or for triggering a DTC. A second set of diagnostic rules is generated that identify vehicle operating parameters used for selecting field failure data obtained when the DTC is triggered. Statistically significant rules are extracted from the second set of diagnostic rules. The first set of rules and the statistically significant rules are cooperatively applied to the parameter identification data for identifying a subset of the parameter identification data that represents anomalies. A subject matter expert analyzes the anomalies for identifying a root cause of the fault.

Abstract: A system and method for determining the status of a vehicle battery to determine whether the battery may not have enough charge to start the vehicle. The method includes collecting data relating to the battery on the vehicle and collecting data relating to the battery at a remote back-office. Both the vehicle and the remote data center determine battery characteristics based on the collected data and the likelihood of a vehicle no-start condition, where the algorithm used at the remote back-office may be more sophisticated. The data collected at the remote back-office may include vehicle battery information transmitted wirelessly from the vehicle, and other information, such as temperature, battery reliability, miles that the vehicle has driven per day, ambient temperature, high content vehicle, etc. Both the vehicle and the remote back-office may determine the battery open circuit voltage.

Abstract: A system and method for reducing or eliminating built-in tests and diagnostic trouble codes that are set as a result of improper parameter values. The method includes collecting field failure data that identifies diagnostic trouble codes and parameters of the system that are used to set diagnostic trouble codes. The method transforms the collected data into a format more appropriate for human analysis and pre-processes the transferred data to identify and remove information that could bias the human analysis. The method includes plotting linear and nonlinear combinations of operation parameters, performing data mining and analysis for detecting inappropriate settings of fault codes in the pre-processed data and providing the mined data to a subject matter expert for review to determine whether a diagnostic trouble code has been issued because of improper parameters.

Abstract: A system and method for determining the status of a vehicle battery to determine whether the battery may not have enough charge to start the vehicle. The method includes collecting data relating to the battery on the vehicle and collecting data relating to the battery at a remote back-office. Both the vehicle and the remote data center determine battery characteristics based on the collected data and the likelihood of a vehicle no-start condition, where the algorithm used at the remote back-office may be more sophisticated. The data collected at the remote back-office may include vehicle battery information transmitted wirelessly from the vehicle, and other information, such as temperature, battery reliability, miles that the vehicle has driven per day, ambient temperature, high content vehicle, etc. Both the vehicle and the remote back-office may determine the battery open circuit voltage.