A computer-implemented method in which one or more processing devices perform operations may include obtaining a field image of a railcar collected from a field camera system and applying a machine-learning algorithm to the field image to generate a machine-learning algorithm output. The method may also include performing a post-processing operation on the machine-learning algorithm output to generate a filtered machine-learning algorithm output. Further, the method may include detecting a defect of the railcar using the filtered machine-learning algorithm output.

A computer-implemented method in which one or more processing devices perform operations may include obtaining a field image of a railcar collected from a field camera system and applying a machine-learning algorithm to the field image to generate a machine-learning algorithm output. The method may also include performing a post-processing operation on the machine-learning algorithm output to generate a filtered machine-learning algorithm output. Further, the method may include detecting a defect of the railcar using the filtered machine-learning algorithm output.

A method providing maintenance information on a door system for a vehicle that includes reading detected process data with respect to a movement process of a door of the door system, the process data representing a current behavior of at least one drive element of the door system during the movement process, correlating the process data to a position curve of positions of the door during the movement process to generate a characteristic line of the current behavior of the at least one drive element at the positions of the door, comparing the characteristic line with a reference characteristic line which represents a target behavior of the at least one drive element at positions of the door to ascertain a deviation of the characteristic line from the reference characteristic line, and assigning the deviation to a sub-region of the position curve, which is divided into a plurality of sub-regions based on the involvement of different movement elements of the door system in different phases of the movement process of the door to determine the maintenance information.

A method providing maintenance information on a door system for a vehicle that includes reading detected process data with respect to a movement process of a door of the door system, the process data representing a current behavior of at least one drive element of the door system during the movement process, correlating the process data to a position curve of positions of the door during the movement process to generate a characteristic line of the current behavior of the at least one drive element at the positions of the door, comparing the characteristic line with a reference characteristic line which represents a target behavior of the at least one drive element at positions of the door to ascertain a deviation of the characteristic line from the reference characteristic line, and assigning the deviation to a sub-region of the position curve, which is divided into a plurality of sub-regions based on the involvement of different movement elements of the door system in different phases of the movement process of the door to determine the maintenance information.

Even in a case where an external camera image-captures a door of a car, with high precision, it can be set to be determined whether or not a foreign substance inserted into the door is present. A monitoring apparatus acquires a camera image that results from a camera image-capturing the vicinity of a door of a car using an external camera, from a camera. A processor of the monitoring apparatus detects the door from the camera image, sets a determination area based on a position of the detected door, acquires an image of the determination area from the camera image, determines whether or not a foreign substance inserted into the door is present, based on the image of the determination area, and performs report outputting to a monitor and a reporting apparatus according to a result of the determination.

A system and method for train control system intrusion detection that uses Machine Learning (ML) to detect attacks on traction and braking operations performed by a TCMS. Control message history, which includes previously generated operational commands and control messages sent to each train and mobility information for each train at predetermined time intervals, is received. The received input data is checked for misbehavior and detect attacks.

A method for carrying out a test process relating to a rail vehicle achieves a fast-working, flexible, and in the event of a misdetection, precise monitoring environment, in particular without complex system or architectural adjustments to the rail vehicle. A stationary control unit and a simulation unit are provided on the land side. A data connection is established between the stationary control unit and the rail vehicle. A data connection is established between the stationary control unit and the simulation unit. The test process includes providing data traffic between the stationary control unit and the rail vehicle and the simulation unit. A device for carrying out a test process relating to a rail vehicle is also provided.

A diagnostic device for determining an out-of-roundness on wheels of rail vehicles within a specified measuring section, comprising a plurality of force sensors, which are designed to determine forces acting on them and are connected to an evaluating device, wherein the evaluating device is provided for determining the out-of-roundness by integrating a force signal F, which is forwarded by the force sensors over time in order to determine an impulse. A method is also provided for determining an out-of-roundness on wheels of rail vehicles within a specified measurement section, wherein the force signals of at least one force sensor are fed to an evaluating device, the evaluating device detects an impulse, and the evaluating device integrates a force signal over a defined time interval and thereafter uses said force signal to output a result.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (Al) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (Al) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.