Disclosed is a method for measuring a deviation angle of a fatigue microcrack based on nonlinear ultrasound, comprising: preliminarily positioning a fatigue microcrack to obtain a center of the microcrack; selecting a horizontal positive direction, and defining an orientation angle; drawing a positive circumference on a surface of a metal plate, and selecting a fixed interval angle; placing an excitation sensor and a receiving sensor on the drawn positive circumference according to the orientation angle; ultrasonically testing each group of ultrasonic sensing paths, and recording time domain waveform signals formed by each group of ultrasonic sensing paths; converting each group of time domain waveform signals into a corresponding frequency domain graph, extracting an ultrasonic fundamental wave signal amplitude and a second harmonic waveform amplitude, and calculating a relative nonlinear coefficient; drawing an orientation angle-relative nonlinear coefficient polar coordinate graph; and determining a deviation angle of the microcrack.

A method of testing for thickness loss in a metal wall is disclosed. The method includes mounting a first and a second ultrasonic transducer to the metal such that the transducers are in ultrasonic communication along a beam line and moving the first and second ultrasonic transducers along a scan line. A series of composites of received signal measurements are obtained by, at multiple locations along the scan line, using the first ultrasonic transducer to transmit ultrasonic signals through the metal wall along the beam line at a plurality of transmission angles and obtaining composites of received signal amplitudes by combining signal amplitudes measured by the second ultrasonic transducer. The series of composites are input into a predetermined relationship to obtain a thickness profile indicative of a proportion of remaining wall thickness. The predetermined relationship is experimentally obtained to characterize a given metal wall of nominal thickness.

The invention relates to an acoustic sensor system (1) for detecting the wall thickness (WT1, WT2) of a material layer (2) of a pipeline wall (3), having at least one transmitter unit (4), which is configured to emit ultrasound in the direction of a material layer (2) and detect an ultrasound echo reflected by the material layer (2), and a control unit (5), which is connected to the at least one transmitter unit (4) for signaling purposes and is configured to detect the wall thickness (WT1, WT2) of the material layer (2) using the ultrasound echo. The invention additionally relates to an in-line inspection device comprising the sensor system (1), to a method for detecting the wall thickness (WT1, WT2) of a material layer (2) of a pipeline wall (3), to a computer program, to a data carrier signal, and to a data storage unit.

Method for monitoring the operation of a pair of turboprop engines of an aircraft comprising the steps of: detecting the sound pressure generated by the first or second turboprop engine generating a respective first or second signal x(t); iteratively calculating by means of a function Rx/Ry the similarity between the first/second signal x(t)/y(t) at a time T1 and at a time T2 subsequent to time T1; and storing the degrees of similarity calculated in successive iterations in order to detect situations of normal operation of the engines when the degrees of similarity fall in successive iterations within the interval of a first value and to detect a potential fault situation in the engines when the degrees of similarity depart from this interval.

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.

Aspects of the disclosure include systems, methods, and program products for evaluating the condition of a component using an acoustic sensor embedded within a lighting device. A system according to the present disclosure can include a first lighting device configured to illuminate an area of an industrial plant; a first acoustic sensor embedded within the first lighting device and configured to detect an acoustic signature of a component in the industrial plant; a computing device communicatively connected to the first acoustic sensor and configured to evaluate a condition of the component in the industrial plant based on the acoustic signature.

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).

In some implementations, a system may receive a cable map for a deployed cable. The system may receive vibration data indicating a vibration associated with a first section of the cable. The system may determine a characteristic associated with the first section of the cable based on the vibration. The system may determine a location associated with the characteristic based on the cable map. The system may determine that the first section of the cable is associated with the location based on the location being associated with the characteristic. The system may associate the location and a length of a second section of the cable extending from an initial location to the location. The system may receive an input identifying the length of the second section of the cable and may output the location based on associating the location and the length of the second section of the cable.

Automated real-time characterization of resistance spot welds using ultrasound-based nondestructive evaluation requires a computational process and system to accurately and rapidly interpret the ultrasonic data in real time. Such a process can be automatically learned using artificial intelligence, from a dataset of exemplary ultrasonic data from nondestructive evaluation of resistance spot welds for which a corresponding ideal evaluation of each weld is provided. The process can then be implemented into a system to automatically interpret data from non-destructive evaluation in real-time. The ideal evaluation of each weld requires identification a large set of features that are observable in the ultrasonic signature and comprehensively characterize the corresponding weld process.