A wayside railway sensor package is provided to detect railway wheels for the purposes of assessing the speed and direction of a train in order to align any measured characteristic on said moving train with the proper vehicle. The stand-alone package is easily installed in the web of the rail using standard tools. When used in combination with recent processing techniques, the package can be used to replace one or more components or subsystems on all common wayside detectors while also providing enhanced capabilities and improved reliability. The package also contains sensors that provide data used for assessing additional rail, wheel, and vehicle conditions directly.

This application relates to methods and apparatus for monitoring data obtained as a train (202) travels on along a rail track (201) to detect the occurrence and/or severity of any significant in-train forces, such as may be caused by heavy braking or excessive acceleration. The method involves taking a first data set corresponding to measurement signals from a plurality of channels of at least one fibre optic distributed acoustic sensor (202) having a sensing fibre (101) deployed to monitor at least part of the rail track. The first data set corresponds to measurement signals acquired as the train passes along the rail track. The method involves analysing the measurement signals to detect a first characteristic signature (402) which consists of a sequence of acoustic transients that appear to propagate rearwards along the train.

This application relates to methods and apparatus for monitoring data obtained as a train (202) travels on along a rail track (201) to detect the occurrence and/or severity of any significant in-train forces, such as may be caused by heavy braking or excessive acceleration. The method involves taking a first data set corresponding to measurement signals from a plurality of channels of at least one fibre optic distributed acoustic sensor (202) having a sensing fibre (101) deployed to monitor at least part of the rail track. The first data set corresponds to measurement signals acquired as the train passes along the rail track. The method involves analysing the measurement signals to detect a first characteristic signature (402) which consists of a sequence of acoustic transients that appear to propagate rearwards along the train.

The invention relates to a portable device for measuring vertical rail measurements under service to record and report excessive vertical rail movement to prevent a train from derailing. The device may be pivoting with a tilt sensor and light-weight. The device may include a microprocessor, sensors, and a display. The device may be installed on the rail. The device may sense an approaching train, automatically turn on the device, and measure the real-time vertical displacement and the maximum/minimum vertical movement of the rail while the train is operating over the rail at all speeds.

A method for monitoring a track line by way of a monitoring device which is connected to a sensor extending along the track line. The sensor which is set in vibration delivers measurement data for the monitoring device. A rail vehicle travels on the track line, during which vibrations having known vibration values are introduced into the track line and transmitted to the sensor. Position data of the rail vehicle are recorded and an evaluation device derives a characteristic of the vibration transmission from the vibration values, the position data, and the measurement data for the track line. In this manner, a calibration of the system takes place with only a single run on the track line.

A method for recognizing a rail vehicle wheel as well as an arrangement for recognizing a rail vehicle wheel. A specific detection pattern of the rail vehicle wheel to be identified is ascertained using rollover signal of a rail vehicle wheel to be identified, which is ascertained with the aid of a load measuring device arranged on a rail, the rollover signal describing a time characteristic of a rail load induced by the rail vehicle wheel to be identified on the rail equipped with the load measuring device during the rollover, and the specific detection pattern comprising one or multiple identification parameter(s)/characteristic value(s) ascertained using the rollover signal. The ascertained specific detection pattern of the rail vehicle wheel to be identified is compared with one or multiple predefined reference-specific detection pattern(s) of rail vehicle wheels, and the rail vehicle wheel to be identified is identified by the comparison.

A railcar acoustic monitoring system including a first trackside frame assembly that includes a first outer frame assembly, a second outer frame assembly, and a first inner frame assembly. The first outer frame assembly including a first microphone assembly positioned on a first outer side of the first rail of the railroad track, the first microphone assembly oriented to receive acoustic signals associated with the passing train. The second outer frame assembly including a second microphone assembly positioned on a second outer side of a second rail of the railroad track, the second microphone assembly oriented to receive acoustic signals associated with the passing train. The first inner frame assembly including a first housing, a third microphone assembly oriented to receive acoustic signals emanating from the first rail, and a fourth microphone assembly oriented to receive acoustic signals associated with the passing train.

A computer-implemented method for identifying a defect of a passing train via acoustic monitoring. The method may include the steps of: receiving data from the passing train within a zone of observance using an array of microphone assemblies of an acoustic monitoring system that are positioned around a section of the track. The method may further include processing the data to determine pressure levels received by each of the array of microphone assemblies. The method may further include calculating a theoretical pressure level for a plurality of points within a three-dimensional space for each microphone of the array of microphone assemblies. The method may further include determining one or more locations within the three-dimensional coordinate space that represents an origin of a noise source indicating the defect. And the method may further include determining a type of defect based on the acoustic signatures.

A computer-implemented method for identifying a defect of a passing train via acoustic monitoring. The method may include the steps of: receiving data from the passing train within a zone of observance using an array of microphone assemblies of an acoustic monitoring system that are positioned around a section of the track. The method may further include processing the data to determine pressure levels received by each of the array of microphone assemblies. The method may further include calculating a theoretical pressure level for a plurality of points within a three-dimensional space for each microphone of the array of microphone assemblies. The method may further include determining one or more locations within the three-dimensional coordinate space that represents an origin of a noise source indicating the defect. And the method may further include determining a type of defect based on the acoustic signatures.

This application relates to an overload and unbalanced load detecting system for a railway and a detecting method. This system includes at least one steel rail. A rail web of each steel rail is provided with two sampling points at two sides between every two adjacent rail sleepers, respectively, and the two sampling points on one side are symmetrically disposed about the steel rail with respect to the two sampling points on the other side. A fiber-optic sensitive element used for continuously measuring a load when a train passes through the two sampling points is obliquely fixed at each sampling point, and two fiber-optic sensitive elements on the same side of each steel rail are disposed at an angle of 90 with each other.