Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: System includes a control system used to control operation of a vehicle system as the vehicle system moves along a route. The vehicle system includes a plurality of system vehicles in which adjacent system vehicles are operatively coupled such that the adjacent system vehicles are permitted to move relative to one another. The control system includes one or more processors that are configured to (a) receive operational settings of the vehicle system and (b) input the operational settings into a system model of the vehicle system to determine an observed metric of the vehicle system. The one or more processors are also configured to (c) compare the observed metric to a reference metric and (d) modify the operational settings of the vehicle system based on differences between the observed and the reference metrics.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: A power control system for a vehicle system identifies coupler nodes in the vehicle system for travel of the vehicle system along a route. The coupler nodes represent slack states of couplers between vehicles in the vehicle system. The system also determines combined driving parameters at locations along the route where a state of the coupler nodes in the vehicle system will change within the vehicle system during the upcoming movement of the vehicle system. The system determines a restriction on operations of the vehicle system to control the coupler nodes during the upcoming movement of the vehicle system and to distribute the combined driving parameters among two or more of the vehicles.

Abstract: A method includes obtaining one or more images of a segment of a route from a camera while a vehicle is moving along the route. The segment of the route includes one or more guide lanes. The method also includes comparing, with one or more computer processors, the one or more images of the segment of the route with a benchmark visual profile of the route based at least in part on an overlay of the one or more images onto the benchmark visual profile or an overlay of the benchmark visual profile onto the one or more images. The one or more processors identify a misaligned segment of the route based on one or more differences between the one or more images and the benchmark visual profile and respond to the identification of the misaligned segment of the route by modifying an operating parameter of the vehicle.

Abstract: A system and method that includes determining a relationship between a moving resistance of a vehicle system and one or more characteristics of the vehicle system. A trip plan may be generated based at least in part on the relationship for movement of the vehicle system through one or more sections of one or more routes based at least in part on the relationship. One or more operational settings of the vehicle system are then designated for implementing the trip plan to drive movement of the vehicle system to achieve one or more objectives.

Abstract: A manufacturing computer device for dynamically adapting additive manufacturing of a part is configured to store a model of the part including a plurality of build parameters. The manufacturing computer device is also configured to receive current sensor information of at least one current sensor reading of a melt pool from a build of the part in progress. The computer device is further configured to determine one or more attributes of the melt pool based on the current sensor information. Moreover, the computer device is configured to calculate at least one unseen attribute of the melt pool. In addition, the computer device is configured to determine an adjusted build parameter based on the at least one unseen attribute, the one or more attributes, and the plurality of build parameters. The computer device is also configured to transmit the adjusted build parameter to a machine currently manufacturing the part.

Abstract: A system includes a locator device and one or more processors operably connected to the locator device. The locator device determines a trailing distance between a trailing vehicle system that travels along a route and a leading vehicle system that travels along the route ahead of the trailing vehicle system in a same direction of travel. The one or more processors compare the trailing distance to a first proximity distance relative to the leading vehicle system. In response to the trailing distance being less than the first proximity distance, the one or more processors set a permitted power output limit for the trailing vehicle system to be less than a maximum achievable power output for the trailing vehicle system, the permitted power output limit being set based on a power-to-weight ratio of the leading vehicle system.

Abstract: A convoy management system and method determine determining an inter-vehicle spacing in a convoy formed from two or more vehicles traveling together along one or more routes. Controllers onboard the two or more vehicles are instructed to automatically change movement of at least one of the vehicles in the convoy to maintain the inter-vehicle spacing. The inter vehicle spacing is dynamically changed during movement of the convoy along the one or more routes.

Abstract: A system and method generate a trip plan for a trip of a vehicle system along a route. The usage of an engine during the trip is determined based on engine operational parameters, energy storage device operational parameters, and one or more objectives of the trip desired to be achieved. The usage of the energy storage device during the trip is also determined based on the engine operational parameters, the energy storage device operational parameters, and the one or more objective, including when to charge or discharge the energy storage device during the trip.

Abstract: An energy management system determines two or more fuel components that represent fuel consumption by a vehicle system completing a trip over one or more routes. A trip plan that designates operational settings of the vehicle system at one or more of different locations, different distances along the one or more routes, or different times is generated or modified. The trip plan is based on the fuel components. The fuel components include a delta elevation component of the one or more routes, a delta speed component of the trip, a mean drag component of the vehicle system, a curvature component of the one or more routes, a base fuel component of the vehicle system, a minimum braking component of the vehicle system, a braking auxiliaries component of the vehicle system, and/or a drag variation of the vehicle system.

Abstract: A system and method identify vehicles to be included in a multi-vehicle system that is to travel along one or more routes for an upcoming trip, and determines plural different potential builds of the multi-vehicle system. The different potential builds represent different sequential orders of the vehicles in the multi-vehicle system. The system and method also simulate travels of the different potential builds for the upcoming trip, calculate a safety metric, consumption metric, and/or build metric for the different potential builds based on travels that are simulated, and generates a quantified evaluation of the safety metric, consumption metric, and/or build metric for the different potential builds for use in selecting a chosen potential build of the different potential builds. The chosen potential build is used to build the multi-vehicle system for the upcoming trip.

Abstract: A distributed control system includes a remote control system configured to be communicatively coupled with plural separate vehicle systems. The remote control system is configured to remotely control operation of the vehicle systems and/or communicate with the local vehicle control system or operator. The remote control system also is configured to one or more of change how many of the vehicle systems are concurrently controlled by the remote control system or change how many remote operators of the remote control system concurrently control the same vehicle system of the vehicle systems.

Abstract: An additive manufacturing system includes a laser device, a build plate, a first scanning device, and an alignment system. The laser device is configured to generate a laser beam. The build plate has a position relative to the laser device. The first scanning device is configured to selectively direct the laser beam across the build plate. The laser beam generates a melt pool on the build plate. The alignment system includes a fiducial marks projector configured to project a plurality of fiducial marks across the build plate. Each fiducial mark has a location on the build plate. The alignment system also includes an optical detector configured to detect the location of each of the fiducial marks on the build plate. The alignment system is configured to detect the position of the build plate relative to the laser device.

Abstract: A location determination system includes one or more processors configured to determine a location of a vehicle system based on output from a location determination system onboard the vehicle system. The one or more processors also are configured to determine whether the location of the vehicle system as determined by the location determination system is accurate based on one or more of performance of the vehicle system, communication between two or more vehicles in the vehicle system, one or more other locations determined by one or more other location determination systems onboard the vehicle system, and/or operation of a brake system of the vehicle system.

Abstract: A manufacturing computer device for dynamically adapting additive manufacturing of a part is provided. The manufacturing computer device includes at least one processor in communication with at least one memory device. The at least one memory device stores a build file for building the part including a plurality of geometries that each include one or more values of a first build parameter. The processor is programmed to receive sensor information of a build of the part by a machine, compare the sensor information for each geometry of the plurality of geometries to the corresponding one or more values of the first build parameter, determine one or more values for a second build parameter for each of the geometries based on the one or more differences, and generate an updated build file for the part including the one or more values for the second build parameter.

Abstract: A manufacturing computer device for dynamically adapting additive manufacturing of a part is configured to store a build file for building the part including one or more build parameters and receive build information. The manufacturing computer device is also configured to compare the sensor information to the one or more build parameters to determine one or more differences. The computer device is further configured to determine one or more adjustments to the one or more build parameters. Moreover, the computer device is configured to generate an updated build file based on the one or more adjustments. In addition, the computer device is further configured to transmit the updated build file to at least one machine of the plurality of machines for manufacture.

Abstract: A system includes one or more processors that are configured to obtain a constraint on movement for a first vehicle system along a route. The constraint is based on movement of a separate second vehicle system that is concurrently traveling along the same route. The processor(s) are configured to determine a speed profile that designates speeds for the first vehicle system according to at least one of distance, location, or time based on the constraint such that the first vehicle system maintains a designated spacing from the second vehicle system along the route.

Abstract: A system (e.g., a control system) includes a sensor configured to monitor an operating condition of a vehicle system during movement of the vehicle system along a route. The system also includes a controller configured to designate one or more operational settings for the vehicle system as a function of time and/or distance along the route.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.