An equipment for simulating high-speed magnetic levitation operation includes a wheel, a driving mechanism, a first test guideway, a second test guideway, a first test object and a second test object. The wheel includes a rim and a hub arranged at a middle of the rim. The driving mechanism is configured to drive the wheel to rotate. The first test guideway and the second test guideway are arranged on an inner wall of the rim, and are respectively arranged on two sides of the hub. The first test object is arranged in the first test guideway, and the second test object is arranged in the second test guideway.

An equipment for simulating high-speed magnetic levitation operation includes a wheel, a driving mechanism, a first test guideway, a second test guideway, a first test object and a second test object. The wheel includes a rim and a hub arranged at a middle of the rim. The driving mechanism is configured to drive the wheel to rotate. The first test guideway and the second test guideway are arranged on an inner wall of the rim, and are respectively arranged on two sides of the hub. The first test object is arranged in the first test guideway, and the second test object is arranged in the second test guideway.

This abnormality monitoring device is equipped with: an acceleration data acquisition unit for acquiring measurement data about the acceleration of a vehicle chassis; an abnormality presence determination unit for determining whether an abnormality is present on the basis of a comparison between the acceleration and a threshold; a frequency analysis unit for analyzing the acceleration frequency when the abnormality presence determination unit determines that an abnormality is present, and an abnormality type identification unit for identifying abnormality type on the basis of the frequency pattern.

A travelling device may travel along a heavy machinery rail. The traveling device may collect positioning data to assess rail condition and/or installation. The positioning data may be collected using self-contained positioning sensors. Positional deviations indicating an elevated portion, a skewed portion, and/or a twisted portion of the heavy machinery rail may indicate issues with rail condition and/or installation.

A system and method for simulating a train management computer on a road-rail vehicle are provided. The system includes at least one portable enclosure, a power source disposed in the at least one portable enclosure, and a train management computer disposed in the at least one portable enclosure, the train management computer adapted to be connected to the power source.

A system and method for simulating a train management computer on a road-rail vehicle are provided. The system includes at least one portable enclosure, a power source disposed in the at least one portable enclosure, and a train management computer disposed in the at least one portable enclosure, the train management computer adapted to be connected to the power source.

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 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 railway vehicle condition monitoring apparatus includes a detection device for detecting vehicle information represented by a wheel load or the like of a wheel included in a railway vehicle running on a railroad track, and a determination device including a classifier to which the vehicle information detected by the detection device is input and which outputs a vehicle condition such as the presence or absence of an abnormality of the railway vehicle. The classifier is generated by means of machine learning that uses training data of a railway vehicle of which the vehicle condition is known, the training data being the vehicle information and the vehicle condition which is known, the machine learning being performed so that when the vehicle information of the training data is input to the classifier, the classifier outputs the vehicle condition of the training data.

A railway vehicle condition monitoring apparatus includes a detection device for detecting vehicle information represented by a wheel load or the like of a wheel included in a railway vehicle running on a railroad track, and a determination device including a classifier to which the vehicle information detected by the detection device is input and which outputs a vehicle condition such as the presence or absence of an abnormality of the railway vehicle. The classifier is generated by means of machine learning that uses training data of a railway vehicle of which the vehicle condition is known, the training data being the vehicle information and the vehicle condition which is known, the machine learning being performed so that when the vehicle information of the training data is input to the classifier, the classifier outputs the vehicle condition of the training data.