A rail sensor unit (10) for attachment to a rail (12) of a rail track, and for sensing by attachment to the rail, acoustic signals and vibrations in the rail. The sensor unit (10) comprises a housing body (20) made in one piece, having a contoured sensing wall portion (22), and an interior compartment (24). The contour is tailored for fitting against a head, web or foot of a rail. At least one piezo-electric transducer (42) within the housing body (20) is coupled to the sensing wall portion (22) for sensing acoustic signals. The housing body (20) efficiently provides substantially all of the contact surfaces form-fitting to the rail. Electronic circuitry (52) in the housing has a controllable dynamic range configuration for both weak signal detection and strong signal detection. The electronic circuitry and electromagnetic shielding protection (48a, 50a) are mounted on a rigid-flex printed circuit substrate (46) folded in the interior compartment (24).

A rail sensor unit (10) for attachment to a rail (12) of a rail track, and for sensing by attachment to the rail, acoustic signals and vibrations in the rail. The sensor unit (10) comprises a housing body (20) made in one piece, having a contoured sensing wall portion (22), and an interior compartment (24). The contour is tailored for fitting against a head, web or foot of a rail. At least one piezo-electric transducer (42) within the housing body (20) is coupled to the sensing wall portion (22) for sensing acoustic signals. The housing body (20) efficiently provides substantially all of the contact surfaces form-fitting to the rail. Electronic circuitry (52) in the housing has a controllable dynamic range configuration for both weak signal detection and strong signal detection. The electronic circuitry and electromagnetic shielding protection (48a, 50a) are mounted on a rigid-flex printed circuit substrate (46) folded in the interior compartment (24).

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.

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.

A microphone assembly including an outer housing, an inner housing, a printed circuit board (PCB) and a cord. The outer housing includes an outer surface, an inner surface opposite the outer surface and defining an opening there through, and a plurality of attachment structures protruding from the inner surface thereof. The inner housing includes an outer surface, an opening, and a plurality of attachment structures protruding from the outer surface thereof. The PCB includes at least one micro-electromechanical systems (MEMS) microphone including an acoustic port. The PCB is coupled to the inner housing such that the acoustic port of the at least one MEMS microphone is positioned within the opening. The cord interconnects the attachment structures of the outer and inner housings, respectively, together.

A microphone assembly including an outer housing, an inner housing, a printed circuit board (PCB) and a cord. The outer housing includes an outer surface, an inner surface opposite the outer surface and defining an opening there through, and a plurality of attachment structures protruding from the inner surface thereof. The inner housing includes an outer surface, an opening, and a plurality of attachment structures protruding from the outer surface thereof. The PCB includes at least one micro-electromechanical systems (MEMS) microphone including an acoustic port. The PCB is coupled to the inner housing such that the acoustic port of the at least one MEMS microphone is positioned within the opening. The cord interconnects the attachment structures of the outer and inner housings, respectively, together.

Various embodiments include a sensor device for checking a rail section comprising: a sensor element; and a magnetic fastening element. The magnetic fastening element is configured to attach the sensor element to the rail section.

This application relates to methods and apparatus for the detection of anomalies in the wheelsets of rail vehicles, for instance for detection of defects such as wheel flats (303) of a wheel (301). The method using a distributed acoustic sensor (106) having a sensing optical fiber (104) deployed along at least part of a rail track (201) as a train (202) moves along that part of the track. The distributed acoustic sensor detects acoustic signals from a plurality of longitudinal sensing portions of the sensing optical fiber. A processor 108 analyzes the acoustic signals to determine the train speed v. Having determined the train speed the processor also analyzes the acoustic signals for a characteristic acoustic signal so as to detect an anomaly in a wheelset where the characteristic acoustic signal is based on the determined train speed. In particular the method may involve at least a first section of track where the train travels with a first speed v1 and a second section of track where the train travels with a second different speed v2 the characteristic acoustic signal may be a repetitive signal at a frequency that varies proportional to the train speed.

This application relates to methods and apparatus for the detection of anomalies in the wheelsets of rail vehicles, for instance for detection of defects such as wheel flats (303) of a wheel (301). The method using a distributed acoustic sensor (106) having a sensing optical fiber (104) deployed along at least part of a rail track (201) as a train (202) moves along that part of the track. The distributed acoustic sensor detects acoustic signals from a plurality of longitudinal sensing portions of the sensing optical fiber. A processor 108 analyzes the acoustic signals to determine the train speed v. Having determined the train speed the processor also analyzes the acoustic signals for a characteristic acoustic signal so as to detect an anomaly in a wheelset where the characteristic acoustic signal is based on the determined train speed. In particular the method may involve at least a first section of track where the train travels with a first speed v1 and a second section of track where the train travels with a second different speed v2 the characteristic acoustic signal may be a repetitive signal at a frequency that varies proportional to the train speed.

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.