An apparatus and a computer implemented method for determining material quality of a component, the method comprising: receiving ultrasonic scan data for a plurality of scanned components; maintaining the scan data within a data storage system; determining historical data associated with multiple parameters based on the scan data of the data storage system; generating a testing model using the historical data, wherein the testing model is configured to define multiple quality ranges for each parameter; scanning a component using at least one ultrasonic probe to provide component data; and determining quality information of the component using the testing model and the component data.

A rotating machine abnormality detection device of an embodiment includes a non-contact acoustic emission sensor, an analyzer, and a diagnoser. The non-contact acoustic emission sensor, arranged at a position spaced away by a predetermined distance from a measurement target acting as a rotating member or a measurement target rotatably supporting the rotating member, is configured to detect acoustic emission that occurs during rotation of the measurement target or the rotating member supported by the measurement target and propagates in an atmosphere. The analyzer is configured to perform time-frequency analysis on a detection signal of the non-contact acoustic emission sensor. The diagnoser is configured to detect occurrence of a rotation abnormality when a frequency component equal to or larger than a predetermined threshold value is present in a predetermined frequency band, based on an analysis result of the analyzer.

A method of determining wear/degradation levels of a consumable assembly of a welding/plasma torch may utilize a controlled sound signal in order to determine an acoustic profile or full spectral audio analysis dataset of the consumable assembly that facilitate the identification of patterns that correlate to certain wear/degradation levels of the consumable assembly. The full spectral audio analysis dataset may be obtained by subjecting a given consumable assembly to a controlled sound signal between operations and as the consumable assembly degrades over time. The full spectral audio analysis may serve as a wear/degradation profile over the life of the given consumable assembly. With a full dataset known for a particular consumable assembly model, an acoustic profile of another consumable assembly of the same model may be obtained and compared to the full dataset in order to identify the wear/degradation level of the tested consumable assembly.

Techniques of performing acoustic echo cancellation involve providing a bi-magnitude filtering operation that performs a first filtering operation when a magnitude of an incoming audio signal to be output from a loudspeaker is less than a specified threshold and a second filtering operation when the magnitude of the incoming audio signal is greater than the threshold. The first filtering operation may take the form of a convolution between the incoming audio signal and a first impulse response function. The second filtering operation may take the form of a convolution between a nonlinear function of the incoming audio signal and a second impulse response function. For such a convolution, the bi-magnitude filtering operation involves providing, as the incoming audio signal, samples of the incoming audio signal over a specified window of time. The first and second impulse response functions may be determined from an input signal input into a microphone.

A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

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).

A sonic speed measurement device includes a reception array in which a plurality of reception elements which output reception signals in response to reception of an ultrasonic wave are disposed in one direction, a phase difference detection portion that detects a phase difference between the reception signals output from the reception elements adjacent to each other in a case where the plurality of reception elements receive the ultrasonic wave which propagates in a spherical wave shape from a target point, and a sonic speed calculation portion that calculates a sonic speed of the ultrasonic wave on the basis of the phase difference.

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.

A method for checking a component to be produced in an additive manner, having the steps of mechanically exciting at least one additively constructed layer of the component during the additive production of the component, measuring a mechanical response signal of the component, and displaying a warning and/or interrupting the additive production of the component if the mechanical response signal lies outside of a specified tolerance range. A device for the additive production of a component, includes a device for mechanically exciting the at least one additively constructed layer of the component, a measuring unit for measuring the mechanical response signal of the component, and a control unit. The control unit is designed to display the warning and/or interrupt the additive production if the mechanical response signal lies outside of a specified tolerance range.

Systems and techniques for measuring process characteristics including electrolyte distribution in a battery cell. A non-destructive method for analyzing a battery cell includes determining acoustic features at two or more locations of the battery cell, the acoustic features based on one or more of acoustic signals travelling through at least one or more portions of the battery cell during one or more points in time or responses to the acoustic signals obtained during one or more points in time, wherein the one or more points in time correspond to one or more stages of electrolyte distribution in the battery cell. One or more characteristics of the battery cell are determined based on the acoustic features at the two or more locations of the battery cell.