A method for ultrasonic inspection of composite parts includes providing a composite part to be inspected with a plurality of layers, determining the runtime and/or attenuation of an ultrasonic signal propagating through the composite part to be inspected, providing the runtime and/or attenuation of the ultrasonic signal propagating through a reference composite part, subtracting the runtime and/or attenuation of the ultrasonic signal in the reference composite part from the runtime and/or attenuation of the ultrasonic signal in the composite part to be inspected, or vice versa, and determining from the result of the subtraction one or more missing and/or additional layers in the inspected composite part. The difference of the runtime and/or attenuation of the ultrasonic signal in the inspected composite part relative to the reference composite part, and/or the difference of the thickness between the composite part and the reference part, is/are visualized.

A system and method directed to inspecting a high density polyethylene pipe. The system includes a pipe inspection tool that is positioned about a fused polyethylene pipe joint. The inspection tool may include search units, a pipe carriage, a pulser and a phased array testing instrument programmed to adjust an amplitude response signal from the search units based on a vertically established time corrected gain curve. The inspection tool is rotated around the high density polyethylene pipe joint while propagating acoustical waves at various patterns and angles through the polyethylene pipe joint. Prior to the joint inspection, the inspection tool is calibrated using a calibration tool which includes a block having an array of equal sized bores positioned along different axis' through the block's depth. The block is constructed of the same material type and grade as the pipes that were fused together to form the polyethylene pipe joint.

The present disclosure provides methods and systems for the characterization of a potential microtexture region (MTR) of a sample, component, or the like. The methods may include determining a threshold width of spatial correlation coefficient and/or a threshold spatial correlation coefficient slope for an actual MTR, characterizing a potential MTR as an actual MTR or a defect, characterizing an actual MTR as an acceptable MTR or not, and/or characterizing various components with potential MTRs as defective or not. The characterization may include calculating a width of spatial correlation coefficient and/or a spatial correlation coefficient slope of the potential MTR and comparing the width of spatial correlation coefficient to a threshold width of spatial correlation coefficient and/or comparing the spatial correlation coefficient slope to a threshold spatial correlation coefficient slope for the potential MTR to be characterized as an actual MTR or a defect (crack).

The present disclosure provides a system and method for real-time visualization of a material during ultrasonic non-destructive testing. The system includes a graphical user interface (GUI) capable of showing a three-dimensional (3-D) image of a composite laminate constructed of a series of two-dimensional (2-D) cross sections. The GUI is capable of displaying the 3-D image as each additional 2-D cross section is scanned by an ultrasonic testing apparatus in real time or near real time, including probable defect regions that contain a flaw such as a hole, crack, wrinkle, or foreign object within the composite. Furthermore, in one embodiment, the system includes an artificial intelligence capable of highlighting defect areas within the 3-D image in real time or near real time and providing data regarding each defect area, such as the depth, size, and/or type of each defect.

Methods and devices for detecting a defect in a rail of a railway track, include at least two sensors selected from among magneto-acoustic and/or piezoelectric and/or magnetostrictive transducers; each sensor being associated with a timestamping circuit of a GNSS satellite positioning system; a measuring circuit for measuring, by way of the sensors, the acousto-elastic waves propagating in the rail, the wave or signal measurements being timestamped. Some developments describe notably active and passive modes; the use of train crossings; the emission of waves; the determination of the existence and then of the position and finally the characterization of the defect, where applicable; preferred placements for installing the sensors; the use of inter-correlation, passive inverse filter or correlation of coda of correlation methods; the use of mobile robots and/or drones; the use of artificial noise sources.

A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

Detecting and classifying damage to a vehicle. One example system includes a microphone positioned to detect sound waves inside the vehicle, one or more sensors positioned on the vehicle and configured to sense a characteristic of a windshield of the vehicle, and an electronic processor communicatively coupled to the one or more sensors and the microphone. The electronic processor is configured to receive sensor information from the one or more sensors and to receive an electrical signal from the microphone. The electronic processor is configured to determine, based on the sensor information, whether a crack event has occurred. The electronic processor is configured to, in response to determining that a crack event has occurred, determine a cause of the crack event based on the electrical signal received from the microphone. The electronic processor is configured to execute a mitigation action based on the cause of the crack event.

A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.

The system includes an ultrasonic measuring device including ultrasonic transmitters and ultrasonic receivers, the ultrasonic measuring device carrying out ultrasonic measurements on a zone of interest of the component divided according to a gridding including cells, and carrying out the measurements cell after cell with the generation, by all the ultrasonic transmitters, of an ultrasonic signal that is sent into the component, and the measurement, by all the ultrasonic receivers, of the amplitude of the ultrasonic signal reflected by the cell in question of the component, a unit for computing, for all of the cells of the gridding, the sum of the amplitudes of all the measurements carried out for that cell, and a processing part for deducing the presence or absence of one or more defects. The system detects all the defects existing in the zone of interest of the component, whatever their orientation may be.