Abstract: Methods and systems are provided for monitoring a health of a vehicle component. In one embodiment, a method is provided, comprising dividing a population of vehicles of a connected vehicle population into a plurality of vehicle classes; for each vehicle class of the plurality of vehicle classes, training a class-specific model of the vehicle class to predict a health status variable of a vehicle component included in the vehicle class based on labelled data from historic databases and calibration data; and for each vehicle class of the plurality of vehicle classes, using a first Federated Learning strategy to request local model data from each vehicle of a plurality of vehicles of the vehicle class; receive the local model data from the plurality of vehicles; update the class-specific model based on the received local model data; and send updated parameters of the class-specific model to vehicles included in the vehicle class.

Abstract: Systems and methods for personalized route prediction including receiving, at a first time, first input data associated with a first route; populating a first database with the input data; receiving, at a third time, second input data associated with a second route; comparing the second input data to the first input data included within the first database; determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data; populating a second database based on the first cluster or the second cluster; determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data; and causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with a vehicle.

Abstract: The present invention extends to methods, systems, and computer program products for path planning for autonomous moving devices. Aspects of the invention include planning a path for a mobile robot to move autonomously in an environment that includes other static and moving obstacles, such as, for example, other mobile devices and pedestrians, without reference to a prior map of the environment. A planned path for a mobile robot can be determined, adjusted, and adapted using diffusion maps to avoid collisions while making progress towards a global destination. Path planning can include using transition probabilities between grid points to find a feasible path through parts of the environment to make progress towards the global destination. In one aspect, diffusion maps are used in combination with a receding horizon approach, including computing diffusion maps at specified time intervals.

Abstract: Embodiments describe a system configured with a brain machine interface (BMI) system implemented in a vehicle for performing vehicle functions using electrical impulses from motor cortex activity in a user's brain. The system uses fuzzy states for increased robustness. The fuzzy states are defined by sets of Gaussian kernel-type membership functions that are defined for steering and velocity action function states. The membership functions define fuzzy states that provide overlapping control tiers for increasing and decreasing vehicle functionality. An autonomous vehicle may perform control and governance of transitions between membership functions that may overlap, resulting in smooth transitioning between the states.

Abstract: Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.

Abstract: This disclosure describes systems and methods for personalized route prediction. An example method may include receiving, at a first time, first input data associated with a first route traversed by a vehicle. The example method may also include populating a first database with the input data. The example method may also include receiving, at a third time, second input data associated with a second route traversed by the vehicle. The example method may also include comparing the second input data to the first input data included within the first database. The example method may also include determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data. The example method may also include populating a second database based on the first cluster or the second cluster.

Abstract: Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.

Abstract: Methods and systems are provided for monitoring a health of a vehicle component. In one embodiment, a method is provided, comprising dividing a population of vehicles of a connected vehicle population into a plurality of vehicle classes; for each vehicle class of the plurality of vehicle classes, training a class-specific model of the vehicle class to predict a health status variable of a vehicle component included in the vehicle class based on labelled data from historic databases and calibration data; and for each vehicle class of the plurality of vehicle classes, using a first Federated Learning strategy to request local model data from each vehicle of a plurality of vehicles of the vehicle class; receive the local model data from the plurality of vehicles; update the class-specific model based on the received local model data; and send updated parameters of the class-specific model to vehicles included in the vehicle class.

Abstract: Embodiments describe a system configured with a brain machine interface (BMI) system implemented in a vehicle for performing vehicle functions using electrical impulses from motor cortex activity in a user's brain. The system uses fuzzy states for increased robustness. The fuzzy states are defined by sets of Gaussian kernel-type membership functions that are defined for steering and velocity action function states. The membership functions define fuzzy states that provide overlapping control tiers for increasing and decreasing vehicle functionality. An autonomous vehicle may perform control and governance of transitions between membership functions that may overlap, resulting in smooth transitioning between the states.

Abstract: Systems and methods for detecting and reporting vehicle damage events are provided herein. An example method includes detecting any of a key-on or key-off event for a vehicle at a second point in time; in response to detecting the key-on or key-off event, obtaining a current set of images of one or more surfaces of the vehicle using one or more onboard cameras of the vehicle; accessing a baseline set of images for the vehicle obtained at a first point in time that precedes the second point in time; comparing the current set of images to the baseline set of images to determine damage to the one or more surfaces; and presenting a message through a human machine interface of the vehicle that is indicative of the damage.

Abstract: The present invention extends to methods, systems, and computer program products for path planning for autonomous moving devices. Aspects of the invention include planning a path for a mobile robot to move autonomously in an environment that includes other static and moving obstacles, such as, for example, other mobile devices and pedestrians, without reference to a prior map of the environment. A planned path for a mobile robot can be determined, adjusted, and adapted using diffusion maps to avoid collisions while making progress towards a global destination. Path planning can include using transition probabilities between grid points to find a feasible path through parts of the environment to make progress towards the global destination. In one aspect, diffusion maps are used in combination with a receding horizon approach, including computing diffusion maps at specified time intervals.

Abstract: Systems and methods for detecting and reporting vehicle damage events are provided herein. An example method includes detecting any of a key-on or key-off event for a vehicle at a second point in time; in response to detecting the key-on or key-off event, obtaining a current set of images of one or more surfaces of the vehicle using one or more onboard cameras of the vehicle; accessing a baseline set of images for the vehicle obtained at a first point in time that precedes the second point in time; comparing the current set of images to the baseline set of images to determine damage to the one or more surfaces; and presenting a message through a human machine interface of the vehicle that is indicative of the damage.

Abstract: A constraint analysis and reliability agent executes a method for analyzing operation of a manufacturing asset, and includes the steps of collecting operation data for a machine over a plurality of predetermined time periods. The operation data includes a plurality of mutually exclusive events that describe operation of the machine. For each of the predetermined time periods, it is determined whether the machine is in an “ON” or an “OFF” state. Data for the “OFF” states is removed from the collected data to generate a filtered data set. Reliability information is then generated based, at least in part, on the filtered data set. This facilitates predictions of future machine operation.

Abstract: A method for controlling weld energy used in a welding process. The method includes establishing a weld energy profile having a total weld energy used over the period of the weld cycle. During the welding process, the expulsion rate is monitored and used to modify the weld energy. Based on the expulsion rate occurring during the welding process, the total weld energy can be shifted between various phases or cycles. In addition, the overall amount of or total weld energy can be increased and decreased.

Abstract: A constraint analysis and reliability agent executes a method for analyzing operation of a manufacturing asset, and includes the steps of collecting operation data for a machine over a plurality of predetermined time periods. The operation data includes a plurality of mutually exclusive events that describe operation of the machine. For each of the predetermined time periods, it is determined whether the machine is in an “ON” or an “OFF” state. Data for the “OFF” states is removed from the collected data to generate a filtered data set. Reliability information is then generated based, at least in part, on the filtered data set. This facilitates predictions of future machine operation.

Abstract: A method for controlling weld energy used in a welding process. The method includes establishing a weld energy profile having a total weld energy used over the period of the weld cycle. During the welding process, the expulsion rate is monitored and used to modify the weld energy. Based on the expulsion rate occurring during the welding process, the total weld energy can be shifted between various phases or cycles. In addition, the overall amount of or total weld energy can be increased and decreased.

Abstract: A method for predictive maintenance of a machine includes collecting feature data for the machine which includes a plurality of feature vectors. At least some of the feature vectors are standardized to facilitate compatibility between different vectors. At least some of the standardized feature vectors are transformed into corresponding two-dimensional feature vectors. At least some of the two-dimensional feature vectors are clustered together based on operating modes of the machine. Similar steps are performed on additional feature data collected from the machine. Recently gathered two-dimensional feature vectors are compared to previously clustered feature vectors to provide predictive maintenance information for the machine.

Abstract: A system and method for interactively optimizing shipping density of racked parts by a user is provided. The system includes a user computer system, a communications network, a remotely located computer system, a data storage device a computer-generated model of a component part; a computer-generated model of a container for transporting the component part and an executable shipping density optimization software program. The methodology includes the steps of the user selecting the component part model and container. The methodology also includes the steps of analyzing the shipping density of component parts within the container. The methodology further includes the steps of identifying a bottleneck feature and modifying the bottleneck feature and determining the optimized density of the modified component parts in the container.

Abstract: The present invention provides a method of optimizing a painting process for applying a paint layer on an article. The method comprises defining a functional relationship paint processing parameters and a paint layer property (i.e., the average paint layer thickness) using a neural network. This functional relationship is then used in a paint optimization function that measures a combination of quality control parameters and efficiency parameters. Finally, the paint optimization function is optimized by adjusting the paint processing parameters utilizing the functional relationship formed by the neural network. The invention also provides a system that implements the methods of the invention.

Abstract: The present invention provides a method of optimizing a painting process for applying a paint layer on an article. The method comprises defining a functional relationship paint processing parameters and a paint layer property (i.e., the average paint layer thickness) using a neural network. This functional relationship is then used in a paint optimization function that measures a combination of quality control parameters and efficiency parameters. Finally, the paint optimization function is optimized by adjusting the paint processing parameters utilizing the functional relationship formed by the neural network. The invention also provides a system that implements the methods of the invention.