Acoustic non-destructive corrosion mapping

Abstract

A correlation-based approach can be used to enhance acoustic time-of-flight determination, such as for thickness measurement or associated mapping. For example, a cross-correlation, or more specifically, numerical approaches to implement an autocorrelation operation, can be used on an acquired acoustic echo signal time series. This can help to provide a time translation that is invariant with respect to initial (e.g., interface) echo and backwall echo absolute time indices. Such an approach does not require an initial synchronization gate to be set or a detection gate to be set for identification of backwall echoes. In general, the approaches described herein can be referred to as "semi-supervised," such as where a user is not required to manually enter all inspection parameters such as those related to time-gating.

Description

ACOUSTIC NON DESTRUCTIVE CORROSION MAPPINGCLAIM OF PRIORITY

[0001] This patent application claims the benefit of priority of Alain Le Duff, U.S. Provisional Patent Application Number 63 / 566,545, titled “ SEMI-SUPERVISED NON-DESTRUCTIVE CORROSION MAPPING,” filed on March 18, 2024 (Attorney Docket No. 6409.280PRV), which is hereby incorporated by reference herein in its entirety.FIELD OF THE DISCLOSURE

[0002] This document pertains generally, but not by way of limitation, to apparatus and techniques for non-destructive inspection such as facilitating mechanical inspection, and more particularly, to apparatus and techniques for performing corrosion inspection using acoustic energy.BACKGROUND

[0003] Non-destructive testing (NDT) can refer to use of one or more different techniques to inspect regions on or within an object, such as to ascertain whether flaws or defects exist, or to otherwise characterize the object being inspected. For example, a material can be inspected such as using a magnetic or acoustic (e.g., ultrasonic) technique. As an illustration, an acoustic time-of-flight determination can be used to estimate a material thickness, such as a wall of a pipe or vessel, or other structure. Thickness measurements can be aggregated into profiles or maps. Thickness mapping can be used during manufacturing or in support of ongoing corrosion monitoring, as illustrative examples.

[0004] Generally, in corrosion mapping using an acoustic inspection technique, one or more electroacoustic transducers are used to insonify a region within an object under test, and acoustic energy that is scattered or reflected can be detected and processed. Such scattered or reflected energy can be referred to as an acoustic echo signal. A time-of-flight can be determined corresponding to a time duration between indications of an interface (e.g., front wall) and backwall echo, or between respective backwall echoes. Generally, such an acoustic inspection scheme involves use of acoustic frequencies in an ultrasonic range of frequencies, such as including pulses having ienergy in a specified range that can include value from, for example, a few hundred kilohertz, to tens of megahertz, as an illustrative example.SUMMARY OF THE DISCLOSURE

[0005] The present subject matter can include use of a correlation-based approach to enhance acoustic time-of-flight determination, such as for thickness measurement or associated mapping. For example, a cross-correlation, or more specifically, numerical approaches to implement an autocorrelation operation, can be used to process an acquired acoustic echo signal time series. The present inventor has recognized that, among other things, this can help to provide a time translation that is invariant with respect to initial (e.g., interface) echo and backwall echo absolute time indices. The present inventor has also recognized that such an approach does not require an initial synchronization gate to be established (e.g., “gate I” is not required, in the context of generally-available corrosion mapping gate configuration parameters such as associated with an Omniscan X3 instrument available from Evident Scientific, Inc.).

[0006] The present inventor has recognized that the approach herein can also be less sensitive to gain variation over the duration of an acquired time series, such as reducing or eliminating use of time-corrected gain (TCG) compensation. Another aspect of the present subject matter is that a backwall echo detection does not require a separate time gate to be established (e.g., “gate A” and “gate B” are not required in the context of generally-available corrosion mapping gate configuration parameters). In general, the approaches described herein can be referred to as “semi-supervised,” where a user is not required to manually enter all inspection parameters such as those related to time-gating, and where the technique can perform synchronization automatically to adapt to a time delay before an initial interface echo, for example.

[0007] In an example, a technique such as a machine-implemented method for performing non-destructive acoustic inspection can include receiving acoustic echo data captured from an object under inspection, the acoustic echo data elicited in response to an acoustic pulse transmission coupled into the object under inspection, generating an autocorrelation signal representative of a correlation between a representation of the acoustic echo data and time-delayed versions of the representation of the acoustic echo data, identifying at least one extremum in the autocorrelation signal, and determining a time-of-flight corresponding to a durationbetween the at least one extremum and an index. For example, the index can be a beginning of the autocorrelation signal, or another extremum. The technique can include determining a thickness value using the determined time-of-flight and at least one of storing or presenting the determined thickness value. The technique can include assembling a map of respective thickness values using respective determined times-of-flight. The technique can include suppressing the determining a time-of- flight corresponding to the duration between the at least one extremum and the index in response to a detection criterion. For example, the detection criterion can include determining a metric, the metric comprising a central tendency of a series of determinations of a ratio of a magnitude of a respective sample in the autocorrelation signal to a value of an identified extremum in the autocorrelation signal and comparing the metric to a threshold. For example, the determining the time-of-flight can be suppressed in response to the metric being below the threshold as indicated by the comparison.

[0008] In an example, a system can be configured to perform non-destructive acoustic inspection, the system comprising an analog front-end circuit that can be coupled to a probe assembly comprising at least one acoustic transducer, at least one processor circuit communicatively coupled with the analog front-end circuit, and at least one memory circuit communicatively coupled with the at least one processor circuit, the at least one memory circuit comprising instructions that, when executed by the at least one processor circuit, cause the system to perform the technique of the examples above or described elsewhere herein.

[0009] This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the invention. The detailed description is included to provide further information about the present patent application.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, variousembodiments discussed in the present document.

[0011] FIG. 1 illustrates generally an example comprising an acoustic inspection system, such as can be used to perform at least a portion one or more techniques as shown and described herein.

[0012] FIG. 2A illustrates generally an illustrative example of a time-of-flight determination that can be performed using a measured duration between received acoustic echo signals, such as corresponding to a thickness of a structure being inspected.

[0013] FIG. 2B illustrates generally an illustrative example of inhibition of time-of- flight measurement that can occur if one or more thresholds are not met, or gate parameters are otherwise not established correctly.

[0014] FIG. 3A illustrates generally a workflow that can be used in relation to the present subject matter, and potential effects of use of such a workflow.

[0015] FIG. 3B shows various plots, including how the workflow of FIG. 3 A can be used to implement a time-of-flight-based thickness measurement technique as shown and described herein.

[0016] FIG. 4A shows an illustrative example of signal processing operations that can be performed in relation to the present subject matter, with FIG. 4B, FIG. 4C, and FIG. 4D showing different combinations of such processing operations to cover different implementations associated with various examples.

[0017] FIG. 5 A shows an illustrative example of a pitted test piece and a corresponding three-dimensional scan of the test piece to provide a “ground truth” map of the surface topology of the test piece for comparison with other measurement techniques.

[0018] FIG. 5B shows an illustrative example of a thickness map corresponding to the same test piece as shown in FIG. 5 A, but where the measurements are performed using a correlation-based approach as shown and described herein.

[0019] FIG. 5C shows an illustrative example of a thickness map corresponding to the same test piece as shown in FIG. 5 A, but where measurements have been obtained using a gate-based approach (e.g., with manually-configured gates A and I, and the plot generated using a time delay calculated as Gate A / minus Gate I / , corresponding to the rising edges of each detected peak within the corresponding Gate A and Gate Itime windows).

[0020] FIG. 6 shows an illustrative example comprising a representative cross- sectional profile corresponding to the horizontal line drawn through each of FIG. 5 A, FIG. 5B, and FIG. 5C, for purposes of comparison between the ground truth three- dimensional scan of FIG. 5 A, the correlation-based approach of FIG. 5B, and the gates-based approach of FIG. 5C.

[0021] FIG. 7A, FIG. 7B, and FIG. 7C illustrate generally different probe positions in relation to a deep pit or channel in the object under test.

[0022] FIG. 8A, FIG. 8B, and FIG. 8C show that depending on the probe position relative to the deep pit or channel, a doublet waveform can be generated showing two or more peaks instead of a distinct single echo corresponding to the backwall.FIG. 9 illustrates a technique that can be used to suppress triggering or detection of peaks in an acoustic echo signal falling below a specified amplitude threshold.

[0023] FIG. 10 A, FIG. 10B, and FIG. 10C illustrate respective examples showing a technique that can be used to detect whether a valid time series has been acquired, such as to avoid reporting false thickness values when a correlation-based technique is executed on noise or noisy data that is not representative of a valid measurement.

[0024] FIG. 11 illustrates generally a technique, such as a machine-implemented method that can be used to perform a time-of-flight determination.

[0025] FIG. 12 illustrates a block diagram of an example comprising a machine upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed.DETAILED DESCRIPTION

[0026] The present inventor has recognized that non-destructive inspection using acoustic measurements can present various challenges that can impede efficiency or measurement reliability. One challenge is that gate-based approaches for acoustic thickness measurements can be unreliable or difficult to configure. Such measurements can be affected by specific configuration of gating options available in generally available test instrumentation. Such test instrumentation may offer only limited guidance on optimal parameter selection, with various modes and parameters that may be selected or configured by a user. Accordingly, measurements are generally highly dependent on configuration of parameters such as gain, calibration,and gating options. This dependency means that variation in operator expertise and judgment can lead to inconsistencies, such as unrepeatable measurements between successive inspections. This variation is also magnified when many different configuration options are available, because there is a significant learning curve associated with the complexity of the measurement instrumentation and associated inspection parameters.

[0027] The present inventor has recognized that at least some parameter selection can be automated to ease such burden on operators, using the present subject matter. As mentioned above, the present subject matter can include use of a correlation -based approach to enhance acoustic time-of-flight determination, such as for thickness measurement or associated mapping. For example, a cross-correlation, or more specifically, numerical approaches to implement an autocorrelation operation can be used on an acquired acoustic echo signal time series. The present inventor has also recognized that use of a correlation-based approach can enhance signal detection, such as by suppressing uncorrelated noise in a processed A-scan time series, and such a technique can be effective on various representations of such an A-scan time series, including RF data (e.g., unrectified data), rectified data, and data that is both rectified and filtered (e.g., “video”) data.

[0028] FIG. 1 illustrates generally an example comprising an acoustic inspection system 100, such as can be used to perform at least a portion one or more techniques as shown and described herein. The inspection system 100 can include a test instrument 140, such as a hand-held or portable assembly. The test instrument 140 can be electrically coupled to a probe assembly 150, such as using a multi -conductor interconnect 130. The probe assembly 150 can include one or more electroacoustic transducers, such as a transducer array 152 including respective transducers 154A through 154N. The transducers array can follow a linear or curved contour or can include an array of elements extending in two axes, such as providing a matrix of transducer elements. The elements need not be square in footprint or arranged along a straight-line axis. Element size and pitch can be varied according to the inspection application.

[0029] A modular probe assembly 150 configuration can be used, such as to allow a test instrument 140 to be used with various different probe assemblies. Generally, the transducer array 152 includes piezoelectric transducers, such as can be acousticallycoupled to a target 158 (e.g., a test specimen or “object-under-test”) through a coupling medium 156. The coupling medium can include a fluid or gel or a solid membrane (e.g., an elastomer or other polymer material), or a combination of fluid, gel, or solid structures. For example, an acoustic transducer assembly can include a transducer array coupled to a wedge structure comprising a rigid thermoset polymer having known acoustic propagation characteristics (for example, Rexolite® available from C-Lec Plastics Inc.), and water can be injected between the wedge and the structure under test as a coupling medium 156 during testing, or testing can be conducted with an interface between the probe assembly 150 and the target 158 otherwise immersed in a coupling medium.

[0030] The test instrument 140 can include digital and analog circuitry, such as a front-end circuit 122 including one or more transmitter signal chains, receiver signal chains, or switching circuitry (e.g., transmit / receive switching circuitry). The transmitter signal chain can include amplifier and filter circuitry, such as to provide transmit pulses for delivery through an interconnect 130 to a probe assembly 150 for insonifying the target 158, such as to image or otherwise detect a flaw 160 on or within the target 158 structure by receiving scattered or reflected acoustic energy elicited in response to the insonification.

[0031] While FIG. 1 shows a single probe assembly 150 and a single transducer array 152, other configurations can be used, such as multiple probe assemblies connected to a single test instrument 140, or multiple transducer arrays 152 used with a single probe assembly 150 or multiple probe assemblies for pitch / catch inspection modes. Similarly, a test protocol can be performed using coordination between multiple test instruments 140, such as in response to an overall test scheme established from a master test instrument 140 or established by another remote system such as a compute facility 108 or general-purpose computing device such as a laptop 132, tablet, smartphone, desktop computer, or the like. The test scheme may be established according to a published standard or regulatory requirement and may be performed upon initial fabrication or on a recurring basis for ongoing surveillance, as illustrative examples.

[0032] The receiver signal chain of the front-end circuit 122 can include one or more filters or amplifier circuits, along with an analog-to-digital conversion facility, such as to digitize echo signals received using the probe assembly 150. Digitization can be performed coherently, such as to provide multiple channels of digitized data alignedor referenced to each other in time or phase. The front-end circuit can be coupled to and controlled by one or more processor circuits, such as a processor circuit 102 included as a portion of the test instrument 140. The processor circuit can be coupled to a memory circuit 104, such as to execute instructions that cause the test instrument 140 to perform one or more of acoustic transmission, acoustic acquisition, processing, or storage of data relating to an acoustic inspection, or to otherwise perform techniques as shown and described herein. The test instrument 140 can be communicatively coupled to other portions of the system 100, such as using a wired or wireless communication interface 120.

[0033] For example, performance of one or more techniques as shown and described herein can be accomplished on-board the test instrument 140 or using other processing or storage facilities such as using a compute facility 108 or a general- purpose computing device such as a laptop 132, tablet, smart-phone, desktop computer, or the like. For example, processing tasks that would be undesirably slow if performed on-board the test instrument 140 or beyond the capabilities of the test instrument 140 can be performed remotely (e.g., on a separate system), such as in response to a request from the test instrument 140. Similarly, storage of imaging data or intermediate data such as A-scan matrices of time series data or other representations of such data, for example, can be accomplished using remote facilities communicatively coupled to the test instrument 140. The test instrument can include a display 110, such as for presentation of configuration information or results, and an input device 112 such as including one or more of a keyboard, trackball, function keys or soft keys, mouse-interface, touchscreen, stylus, or the like, for receiving operator commands, configuration information, or responses to queries.

[0034] FIG. 2A illustrates generally an illustrative example 200A of a time-of-flight determination, which can be performed using a measured duration, represented as a time duration Ai, between features in a received acoustic echo signal, such as echo peaks 205 A and 207 A, respectively. Such a duration Ai can represent a duration for an acoustic pulse to propagate from an interface corresponding to the peak 205 A and travel back to the transducer after being reflected off a backwall corresponding to the peak 207 A. Using a known or estimated acoustic propagation velocity, represented as c, the corresponding estimated thickness, d, of a structure being inspected can beestimated using the arithmetic expression:

[0035] In one approach, time gates (e.g., corresponding to time windows) can be set to capture (e.g., detect) the peaks 205 A and 207 A, such as represented by a synchronization gate 209 window and a corresponding first backwall echo gate 211, along with corresponding amplitude thresholds shown by the vertical level of the lines. The synchronization gate 209 can be referred to as a “Gate I,” and the first backwall echo gate 211 can be referred to as “Gate A.” Detection modes can include peak detection or leading-edge threshold crossing detection, as illustrative examples. The reported duration Ai can be computed as a difference in time indices corresponding to the peak locations or leading edges corresponding to the peaks 205 A and 207 A. As shown in FIG. 2A, each of the gates 209 and 211 can have a configurable duration such as defined by start time, stop time, and an amplitude threshold.

[0036] FIG. 2B illustrates generally an illustrative example 200B of inhibition of time-of-flight measurement that can occur if one or more thresholds are not met. As shown in FIG. 2B, if a received acoustic signal does not include features such as well- defined peaks that exceed both the amplitude threshold associated with the synchronization gate 209 and the corresponding first backwall echo gate 211, a time- of-flight value, A2, is not generated. For example, a first peak 205B may just barely meet the synchronization gate 209 threshold, but a second peak 207B associated with a weak backwall echo may not. To address such challenges, such as shown by the example of FIG. 2B, the present inventor has developed a technique that can include use of a correlation-based technique. The present subject matter can help to facilitate time-of-flight determination without requiring gates to be established, or otherwise reducing a risk of erroneous or inconsistent measurements due to variations in gate configuration.

[0037] FIG. 3A illustrates generally a workflow that can be used in relation to the present subject matter, and potential effects of use of such a workflow, and FIG. 3B shows, graphically, how such a workflow can function. In general, acoustic echo data can be captured from an object under inspection (e.g., received by an electroacoustictransducer and digitized), where the acoustic echo data is elicited in response to an acoustic pulse transmission coupled into the object under inspection. At (1) as shown in FIG. 3A and FIG. 3B, an autocorrelation signal can be generated that is representative of a correlation between a representation of the captured acoustic echo data and time-delayed versions of the same acoustic echo data. In this manner, as shown in FIG. 3B, the time series data is re-indexed to show an initial peak in the autocorrelation signal at the beginning of the autocorrelation signal for positive lag (e.g., “T”) values, so the time series data becomes invariant with respect to an initial echo, (e.g., an interface echo) provided that the first echo is present in the A-scan time series.

[0038] As shown at (2) in FIG. 3A and FIG. 3B, the autocorrelation operation can help to increase signal -to-noise ratio by rejecting or suppressing uncorrelated noise from the autocorrelation signal. In this manner, use of time-corrected gain (TCG) is not required, and such an approach can result in less missing data (e.g., missing thickness measurements in a thickness map) because of the enhanced dynamic range associated with performing time-of-flight measurements using the autocorrelation signal. As shown in FIG. 3B, the autocorrelation signal has a time axis defined by time lag steps (represented as T), which are set by the lag intervals used in the autocorrelation operation. A time-of-flight determination can be made by identifying a peak (as shown at (3) in FIG. 3B or corresponding to another extremum) and measuring a duration between the peak and another time index such as the zeroth lag (indicating the beginning of the autocorrelation signal) as shown by the duration, A, in FIG. 3B.

[0039] As discussed elsewhere herein, generation of the autocorrelation signal can be performed using unrectified acoustic echo data (e.g., “radio frequency” (RF) acoustic data) as an input, or using other representations of acoustic echo data such as a rectified and filtered “video” signal as the input. As shown in FIG. 3B, a videofiltered A-scan representation shows an interface echo 305 A and first backwall echo 307A, and similar to the example of the RF signal, and autocorrelation signal can have an initial peak 305B at the beginning of the autocorrelation signal, and a peak 307B from which a time-of-flight duration can be determined, such as by directly reading out the lag index corresponding to the peak 307B or as a difference between adjacent peaks, as illustrative examples. Examples of different processing operationsare shown and described below, such as in FIG. 4A, FIG. 4B, FIG. 4C, and FIG. 4D.

[0040] FIG. 4A shows an illustrative example of signal processing operations that can be performed in relation to the present subject matter, with FIG. 4B, FIG. 4C, and FIG. 4D showing different combinations of such processing operations to cover different implementations associated with various examples. Referring to FIGS. 4A through 4C, the following signal processing operations can be used in different combinations. Optionally, initially at (A), a filter can be applied, such as a low-pass filter having zero-phase can be applied to preserve temporal locations of interface and backwall echo features in the acquired time series data, while rejecting noise. Illustrative but non-limiting examples of filter topology, order, and parameters are shown in FIG. 4A. At (B), a full-wave rectification operation can be performed (e.g., an absolute value operation in the digital domain). At (C), another filtering operation can be performed, such as to low-pass filter the full-wave rectified waveform. At (D), an autocorrelation signal can be generated.

[0041] Various approaches can be used to perform autocorrelation on discrete time series data. A brute force approach using a loop can step a time-delayed representation of the input to the autocorrelation operation across the input waveform provided at the input at (D). Other approaches may be more computationally efficient. For example, if a cross-correlation operation is available as library call, such as the cross-correlation (xcorr) function provided by Matlab (Mathworks, Natick, MA), the cross-correlation function can be provided with the acoustic echo signal as an input waveform. If a convolution operation is available, such a function could also be used such as by reversing a sign of the time index for the input function represented by the second term in the convolution (*) operation as shown in the expression below:

[0042] In yet another approach, a Toeplitz matrix can be constructed using the time series, and matrix multiplication can be used to obtain an autocorrelation signal. In yet another approach, recognizing the Wiener-Khinchin theorem, an inverse Fourier transform can be performed on the absolute value of a Fourier transform of the input waveform, squared, as represented by the following expression, where RX(T) represents the autocorrelation signal at the output at (D) and x(t) represents the inputwaveform:

[0043] In the expression above, the Fourier transform operations can be Fast Fourier Transform (FFT) operations. At (E), a modulus of an analytic representation can be determined. An analytic signal representation can be obtained such as by applying a Hilbert transform to time series data, and then determining the modulus to obtain an envelope of the time series data. At (F), one or more extrema can be identified in the resulting processed autocorrelation signal, such as identifying a time index of the maximum value (e.g., “arg max”), corresponding to a first backwall echo. A time index at the location of the maximum value can directly indicate a time-of-flight duration, from which an estimated thickness can be determined. A series of multiple times-of-flight determined using the techniques described herein can be used to assemble a two-dimensional plot of thicknesses, such as referred to as a “corrosion map” or “thickness map.”

[0044] Referring to FIG. 4B through FIG. 4D, the solid lines indicate which operations (A) through (F) form a portion of a signal processing path. For example, as shown in FIG. 4B, an acquired A-scan time series (e.g., an “RF” signal) can be provided as an input to the autocorrelation operation at (D), and a modulus can be determined at (E), such as applied to an analytic representation of the autocorrelation signal as discussed above, and then one or more extrema can be identified at F, such as to provide a time-of-flight determination using a time index corresponding to an identified extremum such as local maximum value.

[0045] In the example of FIG. 4C, an acquired acoustic echo signal time series can be full-wave rectified at (B), and then filtered at (C), to provide what can be referred to as a “video signal” or “video” representation of an acquired acoustic A-scan echo signal time series. As mentioned elsewhere herein, the techniques described herein are also applicable to such a video representation, where at (D), the autocorrelation signal can be generated using the video representation as an input, and similar to other examples, a local maximum or other extremum can be identified, and a corresponding time index can be used to perform a time-of-flight determination and associatedthickness estimate.

[0046] In the example of FIG. 4D, the input A-scan signal is full-wave rectified at (B), but the low-pass filter at (C) is not used, and the downstream processing can otherwise be similar to FIG. 4C, where an autocorrelation operation is performed at (D) on the full-wave rectified signal, and an extremum can be identified at (F) for use in a time-of-flight determination and corresponding thickness estimate. As shown generally in FIG. 4B through FIG. 4D, the correlation-based approach works on various different types of representations of acquired acoustic echo signal data, including unrectified representations (e.g., “RF” data), rectified, and rectified plus filtered (e.g., “video”) representations.

[0047] FIG. 5 A shows an illustrative example of a pitted test piece and a corresponding three-dimensional scan of the test piece to provide a “ground truth” map of the surface topology of the test piece for comparison with other measurement techniques. FIG. 5B shows an illustrative example of a thickness map corresponding to the same test piece as shown in FIG. 5 A, but where the measurements are performed using a correlation-based approach as shown and described herein. In particular, the results shown in FIG. 5B correspond to the technique shown and described above with respect to FIG. 4C, where an autocorrelation operation is performed using a video signal (e.g., a full-wave rectified and filtered A-scan signal) as an input to the autocorrelation operation. The illustrative example of FIG. 5B shows good agreement with the three-dimensional scan of FIG. 5 A, with very little missing data (as indicated by white unshaded portions of the map of FIG. 5B).

[0048] By contrast, FIG. 5C shows an illustrative example of a thickness map corresponding to the same test piece as shown in FIG. 5 A, but where measurements have been obtained using a gate-based approach (e.g., with manually -configured gates A and I, and the plot generated using a time delay calculated as Gate A / minus Gate I / , corresponding to the time indices associated with rising edges of each detected peak within the corresponding Gate A and Gate I time windows). In FIG. 5C, there are a greater number and a larger area of white areas indicative of missing data.

[0049] Looking more closely at regions showing missing data, FIG. 6 shows an illustrative example comprising a representative cross-sectional profile corresponding to the horizontal line drawn through each of FIG. 5 A, FIG. 5B, and FIG. 5C, for purposes of comparison between the ground truth three-dimensional scan of FIG. 5 A,the correlation-based approach of FIG. 5B, and the gates-based approach of FIG. 5C. The correlation-based approach (labeled “x-corr based method) performs similarly to the gates-based approach and shows good agreement with the three-dimensional scan and is even able to capture some data in regions where the data would otherwise be missing if the gates-based approach were used (where missing data is indicated by missing segments of the lines).

[0050] The present inventor has recognized that certain types of pitting or other features can create more complex echo waveforms that may confound a thickness measurement or may result in ambiguous time-of-flight determinations. For example, FIG. 7A, FIG. 7B, and FIG. 7C illustrate generally different acoustic probe assembly 150 positions in relation to a deep pit or channel in the object under test 158, and FIG. 8A, FIG. 8B, and FIG. 8C show that depending on the probe 150 position relative to the deep pit or channel, a doublet waveform can be generated showing two or more peaks instead of a distinct single echo corresponding to the backwall (labeled “BW). In an intermediate probe 150 position, as shown in FIG. 7B and the corresponding waveform of FIG. 8B, ambiguity may exist to the operator as to whether the multiple peaks indicate pitting or merely noise. It may be useful to an operator to know whether the detected peak is associated with pitting or a backwall echo.

[0051] To help inform such detection, FIG. 9 illustrates a technique that can be used, optionally, to suppress triggering or detection of peaks in an acoustic echo signal falling below a specified amplitude threshold. An A-scan representation 970 can be compared to an adjustable amplitude threshold. For example, as shown in FIG. 9, only peaks 907 A and 907B preserved in an output 927 because they exceed the amplitude threshold. But, if the amplitude threshold were adjusted upward, detection of the first peak 907A could be suppressed, with time-of-flight measurement performed on the second peak 907B corresponding to the backwall echo. Time-of-flight measurements and associated thickness determinations could be performed using each of the first peak 907A and the second peak 907B, and a difference such determinations (and associated thickness measurements) could provide an indication of pitting depth, or the detection of the peak 907A associated with the pitting could be suppressed.

[0052] The present inventor has also recognized that a correlation-based approach may produce a result even if applied to noise rather than a valid acoustic echo signal as an input. If input data provided to a correlation-based technique is noisy or notrepresentative of an actual acquired acoustic echo signal, a technique can be used to suppress generation of a time-of-flight determination or thickness result. For example, FIG. 10A, FIG. 10B, and FIG. IOC illustrate respective examples showing a technique that can be used to detect whether a valid time series has been acquired, such as to avoid reporting false thickness values when a correlation-based technique is executed on noise or noisy data that is not representative of a valid measurement.

[0053] As an illustration, a metric can be established that can represent a degree of chaotic activity or unexpected dispersion in the input signal provided to the correlation-based technique. A detection criterion can be established based on such a metric. For example, ratios of values (e.g., magnitudes) of respective samples (e.g., lag values) in the autocorrelation signal can be computed relative to a maximum value of the autocorrelation signal, such as within a specified range of lag values corresponding to a range between a minimal expected thickness and a nominal thickness. A central tendency of such ratios can be determined, such as a median value, or another value such as a mean value. If the median value is greater than a specified threshold, then the time-of-flight determination can be deemed invalid, or computation of the time-of-flight can be suppressed. The metric mentioned above can be expressed analytically as follows, where A represents respective ratio determinations for different lag values in a truncated autocorrelation signal, and AT represents a metric corresponding to a determined median value of the different respective ratio determinations:M = median(X) EQN. 5

[0054] The truncated autocorrelation record can be represented as Rx', as shown in FIG. 10C. In FIG. 10C, a valid time-of-flight (TOF) determination can be made based on a distinct peak, such as corresponding to an input signal as shown in FIG. 10A, where the metric, M, is below the specified threshold (“hasSignal” flag). By contrast, in FIG. 10B, noise is shown for the input signal, and the metric, M, is above the specified threshold (“noDetection” flag, so time-of-flight determination can be suppressed. The metric Mean also be implemented using other techniques, asmentioned above, such as a mean value, skewness, or kurtosis, as illustrative examples.

[0055] FIG. 11 illustrates generally a technique 1100, such as a machine-implemented method that can be used to perform a time-of-flight determination. At 1105, acoustic echo data can be captured from an object under inspection, such as elicited in response to an acoustic pulse transmission coupled into the object under inspection using an acoustic transducer. Capturing the acoustic echo data can generally includes digitizing an acoustic echo signal received by the acoustic transducer. At 1110, an autocorrelation signal can be generated, such as using various techniques shown and described herein. Generally, the autocorrelation signal represents a correlation between a representation of the acoustic echo data (e.g., “RF,” rectified, or video A- scan data according to various examples), and time-delayed versions of the same acoustic echo data. At 1115, an extremum can be identified in the autocorrelation signal, such as a first peak or maximum value.

[0056] At 1120, a time-of-flight can be determined corresponding to a duration between the identified extremum and an index. The index of the lag corresponding to the detected extremum can represent the time-of-flight duration. At 1125, a thickness can be determined corresponding to the time-of-flight duration determined at 1120. For thickness mapping or corrosion mapping, times-of-flight and corresponding thicknesses can be determined for different probe positions across an object under inspection. Optionally, such as shown and described elsewhere herein, at 1130, detection can be suppressed of extrema below a specified amplitude threshold, such as shown in FIG. 9. Optionally, such as shown and described elsewhere here, at 1135, determination or reporting of time-of-flight can be suppressed if a criterion is not met, such as a noise metric as shown and described in relation to FIG. 10A, FIG. 10B, and FIG. 10C.

[0057] FIG. 12 illustrates a block diagram of an example comprising a machine 1200 upon which any one or more of the techniques (e.g., methodologies) discussed herein may be performed. Machine 1200 (e.g., computer system) may include a hardware processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1204 and a static memory 1206, connected via an interlink 1230 (e.g., link or bus), as some or all of these components may constitute hardware for systems or relatedimplementations discussed above.

[0058] Generally, the hardware processor 1202 may, for example, include at least one of a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Tensor Processing Unit (TPU), a Neural Processing Unit (NPU), a Vision Processing Unit (VPU), a Machine Learning Accelerator, an Artificial Intelligence Accelerator, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Radio- Frequency Integrated Circuit (RFIC), aNeuromorphic Processor, a Quantum Processor, or any combination thereof. A processor circuit may further be a multi -core processor having two or more independent processors (sometimes referred to as "cores") that may execute instructions contemporaneously. Multi-core processors contain multiple computational cores on a single integrated circuit die, each of which can independently execute program instructions in parallel. Parallel processing on multi-core processors may be implemented via architectures like superscalar, VLIW, vector processing, or SIMD that allow each core to run separate instruction streams concurrently. A processor circuit may be emulated in software, running on a physical processor, as a virtual processor or virtual circuit. The virtual processor may behave like an independent processor but is implemented in software rather than hardware.

[0059] Specific examples of main memory 1204 include Random Access Memory (RAM), and semiconductor memory devices, which may include storage locations in semiconductors such as registers. Specific examples of static memory 1206 include non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; RAM; or optical media such as CD-ROM and DVD-ROM disks.

[0060] The machine 1200 may further include a display device 1210, an input device 1212 (e.g., a keyboard), and a user interface (UI) navigation device 1214 (e.g., a mouse). In an example, the display device 1210, input device 1212, and UI navigation device 1214 may be a touch-screen display. The machine 1200 may include a mass storage device 1208 (e.g., drive unit), a signal generation device 1218 (e.g., a speaker), a network interface device 1220, and one or more sensors 1216, such as aglobal positioning system (GPS) sensor, compass, accelerometer, or some other sensor. The machine 1200 may include an output controller 1228, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

[0061] The mass storage device 1208 may comprise a machine-readable medium 1222 on which is stored one or more sets of data structures or instructions 1224 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 1224 may also reside, completely or at least partially, within the main memory 1204, within static memory 1206, or within the hardware processor 1202 during execution thereof by the machine 1200. In an example, one or any combination of the hardware processor 1202, the main memory 1204, the static memory 1206, or the mass storage device 1208 comprises a machine readable medium.

[0062] Specific examples of machine-readable media include, one or more of nonvolatile memory, such as semiconductor memory devices (e.g., EPROM or EEPROM) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; RAM; or optical media such as CD-ROM and DVD-ROM disks. While the machine-readable medium is illustrated as a single medium, the term "machine readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) configured to store the one or more instructions 1224.

[0063] An apparatus of the machine 1200 includes one or more of a hardware processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1204 and a static memory 1206, sensors 1216, network interface device 1220, antennas, a display device 1210, an input device 1212, a UI navigation device 1214, a mass storage device 1208, instructions 1224, a signal generation device 1218, or an output controller 1228. The apparatus may be configured to perform one or more of the methods or operations disclosed herein.

[0064] The term “machine readable medium” includes, for example, any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 1200 and that cause the machine 1200 to perform any one or more of the techniquesof the present disclosure or causes another apparatus or system to perform any one or more of the techniques, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine- readable medium examples include solid-state memories, optical media, or magnetic media. Specific examples of machine-readable media include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); or optical media such as CD-ROM and DVD-ROM disks. In some examples, machine readable media includes non-transitory machine-readable media. In some examples, machine readable media includes machine readable media that is not a transitory propagating signal.

[0065] The instructions 1224 may be transmitted or received, for example, over a communications network 1226 using a transmission medium via the network interface device 1220 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as WiFi®), IEEE 802.15.4 family of standards, a Long Term Evolution (LTE) 4G or 5G family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, satellite communication networks, among others.

[0066] In an example, the network interface device 1220 includes one or more physical jacks (e.g., Ethernet, coaxial, or other interconnection) or one or more antennas to access the communications network 1226. In an example, the network interface device 1220 includes one or more antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. In some examples, the network interface device 1220 wirelessly communicates using Multiple User MIMOtechniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 1200, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.Various Notes

[0067] Each of the non-limiting aspects in this document can stand on its own or can be combined in various permutations or combinations with one or more of the other aspects or other subject matter described in this document.

[0068] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to generally as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventor also contemplates examples in which only those elements shown or described are provided. Moreover, the present inventor also contemplates examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0069] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0070] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc., are used merely aslabels, and are not intended to impose numerical requirements on their objects.

[0071] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine- readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Such instructions can be read and executed by one or more processors to enable performance of operations comprising a method, for example. The instructions are in any suitable form, such as but not limited to source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like.Further, in an example, the code can be tangibly stored on one or more volatile, non- transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0072] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may he in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with thefull scope of equivalents to which such claims are entitled.

Claims

WHAT IS CLAIMED IS:

1. A machine-implemented method for performing non-destructive acoustic inspection, the method comprising: receiving acoustic echo data captured from an object under inspection, the acoustic echo data elicited in response to an acoustic pulse transmission coupled into the object under inspection; generating an autocorrelation signal representative of a correlation between a representation of the acoustic echo data and time-delayed versions of the representation of the acoustic echo data; identifying at least one extremum in the autocorrelation signal; and determining a time-of-flight corresponding to a duration between the at least one extremum and an index.

2. The machine-implemented method of claim 1, comprising determining a thickness value using the determined time-of-flight and at least one of storing or presenting the determined thickness value.

3. The machine-implemented method of claim 2, comprising assembling a map of respective thickness values using respective determined times-of-flight.

4. The machine-implemented method of any of claims 1 through 3, wherein the index comprises a beginning of the autocorrelation signal.

5. The machine-implemented method of any of claims 1 through 4, wherein the index comprises another extremum.

6. The machine-implemented method of any of claims 1 through 5, wherein the representation of the acoustic echo data comprises an unrectified time series; and wherein the method comprises establishing an envelope of the unrectified time series for use in identifying the at least one extremum.

7. The machine-implemented method of claim 6, wherein the establishing the envelope comprises:applying a Hilbert transform to the unrectified time series to provide an analytic representation; and determining a modulus of the analytic representation.

8. The machine-implemented method of any of claims 1 through 5, wherein the representation of the acoustic echo data comprises a full-wave rectified time series; and wherein the method uses the full-wave rectified time series for the identifying the at least one extremum.

9. The machine-implemented method of claim 8, wherein the full-wave rectified time series is low-pass filtered.

10. The machine-implemented method, of any of claims 1 through 9, comprising suppressing the determining a time-of-flight corresponding to the duration between the at least one extremum and the index in response to a detection criterion.

11. The machine-implemented method of claim 10, wherein the detection criterion comprises: determining a metric, the metric comprising a central tendency of a series of determinations of a ratio of a magnitude of a respective sample in the autocorrelation signal to a value of an identified extremum in the autocorrelation signal; and comparing the metric to a threshold; wherein the determining the time-of-flight is suppressed in response to the metric being below the threshold as indicated by the comparison.

12. The machine-implemented method of claim 11, wherein the central tendency comprises a median.

13. The machine-implemented method of any of claims 1 through 12, wherein the identifying at least one extremum in the autocorrelation signal comprises suppressing extrema falling below a specified amplitude threshold.

14. The machine-implemented method of claim 13, wherein an extremum falling below the specified amplitude threshold comprises a peak associated with a feature other than a backwall echo.

15. The machine-implemented method of any of claims 1 through 14, wherein the generating the autocorrelation signal uses a Fourier transform or a Toeplitz matrix.

16. A system for performing non-destructive acoustic inspection, the system comprising: an analog front-end circuit that can be coupled to a probe assembly comprising at least one acoustic transducer; at least one processor circuit communicatively coupled with the analog frontend circuit; and at least one memory circuit communicatively coupled with the at least one processor circuit, the at least one memory circuit comprising instructions that, when executed by the at least one processor circuit, cause the system to perform the method of any of claims 1 through 15 to store or present data indicative of a time-of-flight determination.

17. The system of claim 16, further comprising the probe assembly.

18. The system of claim 16, further comprising a display configured to present the data indicative of the time-of-flight determination; and wherein the instructions comprise instructions to present the data indicative of the time-of-flight determination.

19. The system of any of claims 16 through 18, wherein the data indicative of the time-of-flight determination comprises a determined thickness or a map of respective determined thicknesses.

20. A system for performing non-destructive acoustic inspection, the system comprising: a means for receiving acoustic echo data captured from an object underinspection, the acoustic echo data elicited in response to an acoustic pulse transmission coupled into the object under inspection; a means for generating an autocorrelation signal representative of a correlation between a representation of the acoustic echo data and time-delayed versions of the representation of the acoustic echo data; a means for identifying at least one extremum in the autocorrelation signal; and a means for determining a time-of-flight corresponding to a duration between the at least one extremum and an index.

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