Acoustic pipe condition assessment using coherent averaging

Methods, systems, and computer-readable storage media for accurate time delay estimation using coherent averaging. A plurality of out-of-bracket acoustical impulses are generated in a pipe segment of a fluid distribution system. Signal data representing the acoustical impulses sensed at two locations along the pipe segment are recorded. Precise timings for the generation of the acoustical impulses are obtained, and the acoustical impulses in the signal data recorded from the first location are averaged based on the precise timings to produce a near-sensor average impulse. Similarly, the acoustical impulses in the signal data recorded from the second location are averaged based on the same precise timings to produce a far-sensor average impulse. A time delay between arrival of the plurality of out-of-bracket acoustical impulses at the first and second locations is estimated from the timing of the near-sensor average impulse and the far-sensor average impulse.

BRIEF SUMMARY

The present disclosure relates to technologies for improving predictions of the condition of pipes of a fluid distribution system by accurate time delay estimation using coherent averaging. According to some embodiments, a method comprises generating a plurality of out-of-bracket acoustical impulses in a pipe segment of a fluid distribution system and recording signal data representing the acoustical impulses sensed at a first location and a second location along the pipe segment. Precise timings for the generation of the acoustical impulses are obtained, and the acoustical impulses in the signal data recorded from the first location are averaged based on the precise timings to produce a near-sensor average impulse. Similarly, the acoustical impulses in the signal data recorded from the second location are averaged based on the same precise timings recovered from the signal data from the first location to produce a far-sensor average impulse. A time delay between arrival of the plurality of out-of-bracket acoustical impulses at the first and second locations is then estimated from the timing of the near-sensor average impulse and the far-sensor average impulse.

According to further embodiments, a computer-readable medium comprises processor-executable instructions that cause a computer system to receive a first signal data from a first acoustic sensor and a second signal data from a second acoustic sensor, the first signal data and second signal data representing a plurality of acoustical impulses sensed at a first location and a second location, respectively, bracketing a pipe segment of a pipe in a fluid distribution system. Precise timings of the plurality of acoustical impulses in the first signal data are recovered, and the plurality of acoustical impulses in the first signal data are averaged based on the precise timings to produce a near-sensor average impulse. The plurality of acoustical impulses in the second signal data are also averaged based on the same precise timings to produce a far-sensor average impulse, and a time delay between arrival of the plurality of acoustical impulses at the first and second locations is estimated from the timing of the near-sensor average impulse and the far-sensor average impulse.

According to further embodiments, a water distribution system comprises an acoustical impulse generator, a first acoustic sensor, a second acoustic sensor, and an acoustic analysis module. The acoustical impulse generator is in acoustical communication with a pipe in the water distribution system and is configured to generate a plurality of acoustical impulses in the pipe. The first and second acoustic sensors are in acoustical communication with the pipe at a first location and second location, respectively, and are configured to sense the plurality of acoustical impulses in the pipe to produce first signal data and second signal data representing the sensed acoustical impulses. The acoustic analysis module executes in a pipe assessment system and is configured to receive the first signal data and the second signal data and select a prototypical impulse from the plurality of acoustical impulses in the first signal data. A fixed time window encompassing the prototypical impulse is slid sample-by-sample through the first signal data while a correlation coefficient between the signal data in the fixed time window and the first signal data at each sample position is calculated. Precise timing for each acoustical impulse in the plurality of acoustical impulses is determined based on corresponding local maximums in the correlation coefficient. The plurality of acoustical impulses in the first signal data are averaged based on the precise timings of the plurality of acoustical impulses to produce a near-sensor average impulse. Similarly, the plurality of acoustical impulses in the second signal data are averaged based on the same precise timings of the plurality of acoustical impulses to produce a far-sensor average impulse. Signal envelopes are computed for the near-sensor average impulse and the far-sensor average impulse, and a time delay between arrival of the plurality of acoustical impulses at the first and second locations is estimated by measuring a time between a point on a rising edge of the signal envelope computed for the near-sensor average impulse and a corresponding point on the rising edge of the signal envelope computed for the far-sensor average impulse.

These and other features and aspects of the various embodiments will become apparent upon reading the following Detailed Description and reviewing the accompanying drawings.

DETAILED DESCRIPTION

The following detailed description is directed to technologies for improving predictions of the condition of pipes of a fluid distribution system by accurate time delay estimation using coherent averaging. Water distribution mains may degrade in several ways. For example, metal pipe walls may corrode and become thinner and weaker (less stiff). Asbestos cement pipes lose calcium and the wall losses strength in time. The wall of pre-stressed concrete pipes gets weaker if the steel wires break. These degradations may cause hydraulic failure of the distribution system.

As described in, e.g., U.S. patent application Ser. No. 09/570,922, filed May 15, 2000, and issued as U.S. Pat. No. 6,561,032; U.S. patent application Ser. No. 11/156,573, filed Jun. 21, 2005, and issued as U.S. Pat. No. 7,328,618; and U.S. patent application Ser. No. 11/952,582, filed Dec. 7, 2007, and issued as U.S. Pat. No. 7,475,596, the disclosures of which are incorporated herein by this reference in their entireties, methods for assessing the condition of, e.g., predicting the stiffness and/or wall thickness of, pipes of a water or other fluid distribution system may rely on measuring the speed of sound in a given pipe segment. The speed of sound may be determined by placing two acoustic or vibration sensors on the pipe or a component of the water system bracketing the pipe segment under test, and generating an out-of-bracket noise. The noise propagates along the pipe segment reaching first the near acoustic sensor, i.e. the sensor closest to the noise generator, and then the far acoustic sensor. The signals from the two sensors are recorded, and signal processing is applied to estimate the time delay between the noise reaching the near and far sensors (i.e., the time it takes for the sound to travel from one sensor to the other).

With the known distance between the two sensors and the estimated time delay, one can determine the propagation velocity of the noise down the pipe segment. The measured propagation velocity may then be compared with a reference speed of sound for that specific pipe class and material, with any differences used to determine the condition of the pipe segment under test. While these methods generally work well, there are circumstances for which a reliable estimation of the time delay is problematic, thus causing inaccurate assessment of condition of the pipes. For example, spurious signals caused by the pipe network topology, such as reflections, may introduce errors difficult to resolve. In addition, high levels of background noise in the signals due to traffic noise and/or other surface or sub-surface noise may further cause estimation problems.

According to embodiments described herein, systems and methods may be implemented utilizing an impulse noise source that allows for a time delay estimation that is relatively immune to the above mentioned spurious phenomena. An impulse excitation allows for a direct measurement of the delay using a time-of-flight approach by observing the precise time the impulse arrives at each sensor. While sound reflections affect the tail of an impulse, they may not affect the leading edge of the impulse. Therefore, measuring the time-of-flight between the leading edge of the two recorded impulses at the near and far sensors provides a more reliable time delay estimation, and thus more accurate condition assessment. In order to accurately identify the leading edge of the impulse(s) in the presence of the background noise, “coherent averaging” is employed to increase the signal-to-noise ratio by averaging multiple impulses while aligning the impulses in phase (hence, “coherent”).

FIG.1and the following description are intended to provide a general description of a suitable environment in which the embodiments described herein may be implemented. In particular,FIG.1shows an environment100for assessing the condition, e.g. predicting the stiffness or wall thickness, of a pipe of a fluid distribution system, according to embodiments described herein. The environment100includes a pipe102containing the pipe segment to be tested. According to some embodiments, the pipe102may be a main in a water or other fluid distribution system that may include many pipes of various diameters and made of various materials, all connected in a fluid network. The fluid distribution system may further include other distribution system components, such as couplings, valves, hydrants, pumps, and the like, all connected together to form the fluid network, of which the pipe102is a part.

In some embodiments, the fluid network may be partially or wholly subterraneous, or portions of the fluid network may be subterraneous, while other portions of the fluid network may be non-subterraneous (i.e., above ground). For example, the pipe102may be partially or wholly subterraneous while a hydrant or valve (not shown) connected to the pipe may be located above ground. In other embodiments, the pipe102may be partially subterraneous in that the pipe has portions exposed, such as to allow easy connection of sensor or testing devices (e.g., acoustical impulse generators and acoustic sensors described herein) to the pipe.

The environment100further includes an acoustical impulse generator104and two or more vibration or acoustic sensors, such as acoustic sensors106A and106B (referred to herein generally as acoustic sensors106), inserted into a fluid path110of the pipe102, attached to an outside of the pipe wall, or otherwise in acoustical communication with the pipe. For purposes of this disclosure, a component or device being “in acoustical communication with” the pipe102represents the component being connected directly or indirectly to the pipe in such a way that vibrations, acoustical impulses, or other variations in pressure traveling through the pipe wall and/or the fluid in the pipe can be produced or sensed by the component.

According to embodiments, the acoustical impulse generator104generates a series of acoustical impulses, i.e., a vibrations or longitudinal pressure waves, within a fluid path110of the pipe102. The acoustical impulse generator104may comprise any means suitable for the creation of acoustical impulses or vibrations in the pipe102according to defined parameters, such as interval and intensity, including a mechanical device, such as a motorized hammer or piston for striking the pipe wall, an electro-mechanical device, such as a speaker or hydrophone, a manually actuated device, such as a human with a hammer, and the like. In further embodiments, a valve may be opened and closed one or more times so as to generate an acoustical impulse within fluid path110. It will be understood that many other techniques may be implemented to cause the acoustical impulses to be generated in the fluid path110. The acoustical impulse generator104may be attached to and/or act upon an outer wall of the pipe102or on a component of the fluid distribution system in fluid communication with the pipe, such as a hydrant or valve. In other embodiments, the parts of the acoustical impulse generator104may extend partially or wholly into the fluid path110, or may be located in proximity to the external surface of the pipe102and transmit acoustic energy through the soil surrounding the pipe.

The acoustic sensors106measure the sound pressure of the acoustical impulses propagating through the pipe102. In some embodiments, the acoustic sensors106may comprise hydrophones inserted into the fluid path110. In other embodiments, the acoustic sensors may comprise transducers or accelerometers attached to the outer wall of the pipe102or to a component in fluid communication with the pipe, such as a hydrant. The transducers or accelerometers may measure the instantaneous acceleration of the pipe wall from vibrations caused by the sound pressure of the acoustical impulses. The measured acceleration of the wall constitutes an indirect measurement of sound pressure in the pipe. In further embodiments, the acoustic sensors106may include hydrophones, transducers, accelerometers, or any combination of these and other sensors known in the art for measuring vibrations or acoustic signals.

In some embodiments, two acoustic sensors106A and106B are placed in acoustical communication with the pipe at a specific distance apart, bracketing the specific pipe segment112for testing, also referred to herein as the “target pipe segment112,” as shown inFIG.1. According to embodiments, the length of the pipe segment112may be hundreds of centimeters, hundreds of meters, or several kilometers apart. In some embodiments, the acoustic sensors106A and106B may be connected to the same pipe, such as pipe102, as further shown inFIG.1.

According to some embodiments, the acoustical impulse generator104is located outside of the pipe segment112between the two acoustic sensors106A and106B, referred to herein as being located “out-of-bracket.” The acoustic sensors106A and106B sense the acoustical impulses in the pipe102generated by the acoustical impulse generator104at their respective locations. The acoustic sensors106A and106B may also pickup background noise, such as road traffic or other surface or subsurface activity, and spurious acoustical events, such as a truck hitting a metal construction plate on a nearby roadway.

The acoustic sensors106A and106B each produce a signal representing the sensed sounds, and signal data representing the sensed signal from the respective acoustic sensors106A and106B are sent to a pipe assessment system120. The pipe assessment system120processes and analyzes the signal data received from the acoustic sensors106A and106B to determine a condition of the segment112of the pipe102bracketed by the sensors utilizing the methods and technologies described herein. It will be appreciated that the condition of the target pipe segment112may be representative of the condition of the pipe102or pipe network as a whole.

Generally, the pipe assessment system120represents a collection of computing resources for the processing and analysis of the signal data received from the acoustic sensors106and determination pipe condition. According to embodiments, the pipe assessment system120may comprise one or more computer devices and/or computing resources connected together utilizing any number of connection methods known in the art. For example, the pipe assessment system120may comprise a mobile computer device, such as a laptop or tablet, deployed in the field in proximity to the target pipe segment112. Alternatively or additionally, the pipe assessment system120may comprise laptop or desktop computers; tablets, smartphones or mobile devices; server computers hosting application services, web services, database services, file storage services, and the like; and virtualized, cloud-based computing resources, such as processing resources, storage resources, and the like, that receive the signal data from the acoustic sensors106through one or more intermediate communication links or networks.

According to embodiments, the pipe assessment system120includes one or more processor(s)122. The processor(s)122may comprise microprocessors, microcontrollers, cloud-based processing resources, or other processing resources capable executing instructions and routines stored in a connected memory124. The memory124may comprise a variety non-transitory computer-readable storage media for storing processor-executable instructions, data structures and other information within the pipe assessment system120, including volatile and non-volatile, removable and non-removable storage media implemented in any method or technology, such as RAM; ROM; FLASH memory, solid-state disk (“SSD”) drives, or other solid-state memory technology; compact disc ROM (“CD-ROM”), digital versatile disk (“DVD”), or other optical storage; magnetic hard disk drives (“HDD”), hybrid solid-state and magnetic disk (“SSHD”) drives, magnetic tape, magnetic cassette, or other magnetic storage devices; and the like.

In some embodiments, the memory124may include an acoustic analysis module126for performing the acoustic analysis of the signal data from the two acoustic sensors106A and106B to accurately estimate the time delay between the two sensors using coherent averaging, as described herein. The acoustic analysis module126may include one or more software programs, components, and/or modules executing on the processor(s) of the pipe assessment system120. The acoustic analysis module126may further include hardware components specifically designed to perform one or more steps of the routines described herein. According to further embodiments, the memory124may store processor-executable instructions that, when executed by the processor(s)122, perform some or all of the steps of the routine400described herein for accurately estimating a time delay between sensing acoustical impulses in two sensors bracketing a pipe segment using coherent averaging, as described in regard toFIGS.4A and4B.

The pipe assessment system120may be in direct communication with the acoustic sensors106over a wired connection, or may be indirectly connected to the sensors and impulse generator through one or more intermediate communication links and/or computing devices. For example, a laptop may be connected to the acoustic sensors106A and106B via one or more radio-frequency (“RF”) links to receive signal data from the sensors. In other embodiments, the signal data from each acoustic sensor106may be received by individual computing device and sent to a central analysis computer for processing and analysis. In such embodiments, it may be necessary to ensure that the clocks of the individual computing devices are synchronized or share a highly-accurate time source in order to ensure accurate timing accompanies the signal data from the respective acoustic sensors106.

According to some embodiments, the processor(s)122are operatively connected to acoustic sensors106through a sensor interface128. The sensor interface128allows the processor(s)122to receive the signals from the sensors representative of the sensed acoustical impulses in the pipe102. For example, the sensor interface128may utilize one or more analog-to-digital converters (“ADCs”) to convert an analog voltage output of the acoustic sensors106to a digital value that is sampled by the processor(s)122at a specific sampling rate sufficient to represent the acoustical impulses in the signal data. According to some embodiments, a sampling rate around 10 kHz may be utilized to capture data representing the frequencies of interest in the acoustical impulses. In further embodiments, the sound processing unit, or “sound card” of the laptop computer may be utilized to provide the sampling functionality.

In further embodiments, the pipe assessment system120may also be connected directly or indirectly to the acoustical impulse generator104through an excitation interface202, as shown inFIG.2. The excitation interface202may allow the processor(s)122to control the acoustical impulse generator104to generate acoustical impulses in the pipe102with a specific interval (period) and/or a specific intensity. The excitation interface202may further allow the processor(s)122to receive precise timing information for the generation of the acoustical impulses by the acoustical impulse generator104in the pipe102.

It will be appreciated that the structure and/or functionality of the pipe assessment system may be different that that illustrated inFIGS.1and2and described herein. For example, one or more of the processor(s)122, memory124, sensor interfaces128, excitation interfaces202, and/or other components and circuitry described may be integrated within a common integrated circuit package or distributed among multiple integrated circuit packages in one or more computing devices. In some embodiments, some or all of the processing and analysis described herein may be implemented as software applications on mobile computing platforms, such as a smartphone or laptop with cellular networking capability. Similarly, the illustrated connection pathways are provided for purposes of illustration and not of limitation, and some components and/or interconnections may be omitted for purposes of clarity. It will be further appreciated that pipe assessment system120may not include all of the components shown inFIGS.1and2, may include other components that are not explicitly shown inFIGS.1and2, or may utilize architectures completely different than those shown inFIGS.1and2.

FIG.3shows additional details of a pipe102of a fluid distribution system and the traversal of acoustical impulses through the fluid contained therein. Acoustical impulses generated in the fluid path110of a pipe102, such as acoustical impulses114A-114C (referred to herein generally as acoustical impulses114), will travel longitudinally down the pipe at a certain speed. By accurately measuring the time that it takes the impulses to travel between the two acoustic sensors106A and106B (referred to herein generally as the “time delay”) at known distance d apart, and accurate speed of sound in the fluid path110of the pipe segment112may be computed. By comparing the computed speed of sound to models of speeds of sound in pipes of known characteristics and conditions, the condition of the pipe segment112, e.g., the thickness and/or stiffness of the pipe wall302, may be determined. However, accurate measurement of the time delay is hampered by background noise in the recorded signals, reflections from buildup on the inside of the pipe wall, spurious signal data from external noise occurrences during the recordings, inaccurate clocks and time measurements at individual computing devices in the pipe assessment system120, and the like.

FIG.4illustrates one routine400for accurately estimating a time delay between sensing acoustical impulses in two sensors bracketing a pipe segment using coherent averaging, according to some embodiments. In some embodiments, the routine400may be performed by the acoustic analysis module126executing on a laptop computer in direct connection with the acoustic sensors106A,106B associated with the target pipe segment112. In other embodiments, the routine400may be performed by some combination of the processor(s)122, computing devices, components, and modules of the pipe assessment system102.

The routine400begins at step402, where a series of acoustical impulses114are generated in the pipe wall302and/or fluid path110of the target pipe segment112. This may be performed the acoustical impulse generator104. For example, the acoustical impulses114may be generated by manual operation of the acoustical impulse generator104by a human operator. In further embodiments, the pipe assessment system120or acoustic analysis module126may schedule a time to perform the data collection and analysis, and may control the acoustical impulse generator104at the scheduled time to produce the acoustical impulses114in the pipe segment112to facilitate the analysis. In some embodiments, the acoustical impulses114may be generated “out-of-bracket,” i.e., in the same pipe102as the target pipe segment112but outside of the segment bracketed by the acoustic sensors106A and106B.

According to embodiments, generation of the acoustical impulses114may comprise excitation of the pipe and/or fluid using a pulsating source, such as a manual or mechanical impact (referred to herein as “tapping”) of the pipe wall or a component in fluid connection with the pipe, such as a hydrant. In further embodiments, the excitation of the pipe/fluid may be produced by a speaker or other acoustic device attached to the pipe wall302or inserted into the fluid path110and driven to produce high amplitude impulses within the pipe wall and/or fluid path. The acoustical impulses114may be generated by the acoustical impulse generator104at a specific interval (period) and/or a specific intensity (amplitude) according to the requirements of the remaining acoustic analysis algorithm. For example, the interval between acoustical impulses114and may depend upon the length of the target pipe segment112, the size or type of the pipe102, the material of the pipe wall302, the speed of sound in the pipe and/or fluid within the pipe, and the like.

Next, the routine400proceeds from step402to step404, where the acoustic analysis module126receives signal data from the first and second acoustic sensors106A and106B representing the measurement of the acoustical impulses114in the pipe102at either end of the target pipe segment112. For example, as shown inFIG.5, signal data502A (referred to herein generally as signal data502) recorded at acoustic sensor106A and signal data502B recorded at acoustic sensor106B may be collected in the pipe assessment system120. The processor(s)122of the pipe assessment system120may sample the signals from the sensors through the ADC(s) of the sensor interface128at a rate sufficient to represent the frequency and amplitude of the selected excitation frequencies of the acoustical impulses114produced by the acoustical impulse generator104. According to some embodiments, sampling of the lower frequency ranges, e.g., from 10-1000 Hz for metal pipes, may produce the most useable signal data, and low-pass filters may be employed with the acoustic sensors106A and106B. Accordingly, sampling rates in the range of 10 kHz to 12 kHz may be utilized, according to some embodiments. In one embodiment, a sampling rate of 11,025 Hz may be used.

According to further embodiments, the signal data502may be recorded and stored in a buffer in the memory124for later analysis by the acoustic analysis module126or for transmission to a central analysis computer in the pipe assessment system120. In further embodiments, the acoustic analysis module126may also receive data from the acoustical impulse generator104(or a sensor in close proximity to the acoustical impulse generator) containing precise timing information regarding the generation of the acoustical impulses114(the “tapping”) in the pipe102that can be used in the analysis of the signal data from the acoustic sensors in lieu of the recovered precise timings of the impulses described below, as indicated by line405ofFIG.4A.

Once the signal data502from the two acoustic sensors106A and106B has been received, the routine400proceeds from step404to step406, where the acoustic analysis module126identifies a course timing of the acoustical impulses114in the signal data recorded from the acoustic sensor106having the strongest signal-to-noise ratio (“SNR”), typically the sensor nearest to acoustical impulse generator104, such as acoustic sensor106A fromFIGS.1and3B. The acoustic sensor having the strongest SNR value may be referred to herein as the “near acoustic sensor106A” or “near sensor.”

In some embodiments, a min/max algorithm may be employed to locate the acoustic impulses114in the signal data502. The signal data502A from the near acoustic sensor106A is first divided into small time-division frames, such as frames604A-604N (referred to herein generally as small frames604), as shown in the signal graph602ofFIG.6. The size of the small frames604may be selected such that the frames are smaller than the interval of the acoustical impulses, such as acoustical impulses114A and114B. For example, if the acoustical impulse generator104is driven to deliver one acoustical impulse114approximately every second, the signal data502A from the near acoustic sensor106A may be divided into small frames604representing 0.1 seconds. A peak amplitude of the signal in each small frame604is then determined, and the frames are sorted according to their respective peak amplitude values. A value that is indicative of a background noise threshold606may then be determined from a subset of the small frames604of the signal data from the near acoustic sensor106A having the lowest peak amplitude values. For example, the background noise threshold606value may be selected such to include the peak amplitude values from the lowest 10% of the small frames604, such as frames604C and604D from the signal graph602inFIG.6.

Next the signal data502A from the near acoustic sensor106A is divided into large time-division frames, such as frames704A-704N (referred to herein generally as large frames704), as shown in the signal graph702ofFIG.7. The size of the large frames704may be selected such that the frames are larger than the interval of the acoustical impulses114A-114N. For example, if the acoustical impulse generator104is driven to deliver one acoustical impulse114approximately every second, the signal data502A from the near acoustic sensor106A may be divided into large frames704representing 3.0 seconds, such that each frame is guaranteed to contain at least one acoustical impulse. Again, a peak amplitude is determined in each of the large frames704, and a median value of the peak amplitude of the large frames is calculated. While some large frames704may contain spurious signal data from traffic noise, construction, etc., the median of the maximum values from the large frames should be representative of a typical acoustical impulse114.

A value for an impulse threshold706is then selected between the background noise threshold606and the median value of the peak amplitude of the large frames704. For example, the impulse threshold706value may be computed to be ¼ of the interval between the background noise threshold and the median peak amplitude. The acoustic analysis module126may then determine the coarse timing of each acoustical impulse114A-114N in the signal data502A using the impulse threshold value, e.g., by detecting where the power in the signal first exceeds the impulse threshold706after a relative quiescent period.

From step406, the routine400proceeds to step408, where the acoustic analysis module126selects the signal data relevant to each acoustical impulse114from the signal data502A by choosing a time window of fixed duration that encompasses the entire impulse. For example, the acoustic analysis module126may create time windows of ⅛ second before the coarse timing of each acoustical impulse114A-114N to ½ second after the coarse timing of the impulse, such as time windows804A-804N (referred to herein generally as time windows804), as shown in the signal graph802ofFIG.8. The position and width of the encompassing fixed time windows804may be parametric, and may vary based on frequency and/or period of acoustical impulse generation, type of pipe or fluid therein, length of the target pipe segment112, and the like.

The routine400proceeds from step408to step410, where the acoustic analysis module126selects one of the acoustical impulses from the signal data502A as a prototypical impulse. The selection of a prototypical impulse allows the acoustic analysis module126to more likely identify acoustical impulses114in the signal data502A than other impulses from spurious noise, such that may occur when a car travels over a pipe or a steel road plate. According to some embodiments, one method for selecting a prototypical impulse from the acoustical impulses114in the signal data502A comprises correlating the signal data inside each fixed time window804with the signal data inside all other of the fixed time windows and selecting the maximum correlation coefficient for each unique pair. For each acoustical impulse114in a fixed time window804, the acoustic analysis module126may then sum or average the maximum correlation coefficients with all of the other impulses, and select the acoustical impulse with the highest sum or average as the prototypical impulse.

For large signal data502A containing numerous acoustical impulses114, this may represent a very resource intensive process. According to further embodiments, the acoustic analysis module126may first align the acoustic impulses114to their maxim local peaks. With the signals pre-aligned, it is only necessary to compute one correlation coefficient for each unique pair of acoustical impulses806as a measure of their similarity. This may be a faster and less resource-intensive computing task than performing correlation between the entire signal data502in the corresponding fixed time windows804for each impulse pair.

Next, at step412, the acoustic analysis module126utilizes the selected prototypical impulse, such as the prototypical impulse902shown inFIG.9, to recover the precise timing of each of the acoustical impulses114in the signal data502A. According to some embodiments, this may be accomplished by cross-correlating the prototypical impulse902with the entire signal data502A—essentially sliding the signal data in the fixed time window804B encompassing the prototypical impulse902sample-by-sample through the signal data502A while calculating a correlation coefficient between the data in the fixed time window804B and the signal data at that sample position, as shown at1104. The timing of each acoustical impulse114in signal data502A may be obtained from local maximums in the correlation coefficient, i.e., the times of the sample where the correlation coefficient peaks, as further shown at906A-906N inFIG.9, allowing a more precise time for each impulse to be determined than the coarse timing of each acoustical impulse determined in step406.

For illustrative purposes,FIGS.10A and10Bshow composite signal graphs1002A and1002B showing the acoustical impulses114A-N from the near signal data502A aligned utilizing the coarse timings from step406and the precise timings recovered in step412, respectively. As may be seen inFIG.10B, the acoustical impulses114A-N could be aligned utilizing the recovered precise timings. Averaging these aligned impulses will result in improved SNR since the peaks in the impulses are aligned but the background noise is not. Thus the averaging process cancels out the noise.

Recovering a precise timing of each acoustical impulse114in the near signal data502A may be desirable because, despite the initiation of impulse generation by the acoustical impulse generator104at a known time and at a known period of impulses, there may be no precise measurement of the exact timing of the impact or “tapping” causing the impulses. This may be due to mechanical or electrical variations in the, e.g., motorized hammer or piston that is utilized by the acoustical impulse generator104to strike the pipe or component, or variations in timing that may naturally occur in manual excitation of the pipe, e.g., by a human with a conventional hammer. In alternative embodiments, if precise timing of the impacts or tapping can be obtained from the acoustical impulse generator104(or from a sensor in very close proximity to the acoustical impulse generator, the need for steps406-412of the routine400to recover the precise timing of the impulses114from the signal data502A can be eliminated.

From step412, the routine400proceeds to step414, where the acoustic analysis module126utilizes the precise timings of the acoustical impulses114to average the impulses in the signal data502A for the near acoustic sensor106A into a single average pulse. For example, an average amplitude may be calculated over the samples in the fixed time windows804encompassing each acoustical impulse114aligned using the precise timings recovered in step412. This results in a near-sensor average impulse1104A as shown in the signal graph1102ofFIG.11.

The routine400proceeds from step414to step416, where the acoustic analysis module126utilizes the same precise timings recovered in step412to select the acoustic impulses114in the signal data502B from the far acoustic sensor106B. According to some embodiments, the width of the fixed time windows804determined from the signal data502A from the near acoustic sensor106A may be set to encompass the same impulse in the signal data502B from the far acoustic sensor106B, based on knowledge of the length of the target pipe segment1121and an estimated speed of sound in the pipe102. Utilizing these same fixed time windows804aligned with the precise timings recovered in step412, the acoustic analysis module126may identify the acoustical impulses114in the far signal data502B. At step418, as shown inFIG.4B, the acoustic analysis module126may then average the identified acoustical impulses114in the signal data502B from the far acoustic sensor106B into a far-sensor average impulse1104B, as further shown inFIG.11, using a same method as utilized in step414.

From step418, the routine400proceeds to step420, where the acoustic analysis module126estimates an impulse envelope for each of the near-sensor average impulse1104A and the far-sensor average impulse1104B. For example, the acoustic analysis module126may apply a Hilbert transform over the two average impulses1104A and1104B within the encompassing fixed time windows804to produce the respective near-sensor estimated impulse envelope1204A and far-sensor estimated impulse envelope1204B, as shown inFIG.12. According to embodiments, the estimated impulse envelopes1204A and1204B retain their position along the time axis from the respective average impulses1104A and1104B. In further embodiments, the acoustic analysis module126may simply apply an absolute value function to the amplitude of the signal data502A and502B from the respective average impulses1104A and1104B and calculate a bounding function from the resulting positive signal to produce the estimated impulse envelopes1204A and1204B. In addition, the acoustic analysis module126may normalize the estimated impulse envelopes1204A and1204B to a consistent maximum value (e.g., 1.0), as shown at step422. In further embodiments, the acoustic analysis module126may first normalize the signal data for average impulses1104A and1104B to the consistent maximum value before the corresponding estimated impulse envelopes1204A and1204B are computed, as further illustrated inFIG.12.

The routine400proceeds from step422to step424, where the acoustic analysis module126estimates a time delay between the arrival of the acoustical impulses114at the near and far acoustic sensors106A and106B by measuring a time difference between the rising (leading) edge of the two impulse envelopes1204A and1204B estimated from the average impulses1104A and1104B in step420. As further shown inFIG.12, a point along the rising edge of the normalized near-sensor estimated impulse envelope1204A may be determined that represents a starting point of the acoustical impulse114. For example, where the leading edge of the near-sensor estimated impulse envelope1204A crosses a threshold1206representing 20% of the interval from the background noise threshold606to the normalized peak amplitude (1.0) noise (referred to herein as the T20 threshold1206), as shown at1208inFIG.12. A same point1210is determined for the leading edge of the normalized far-sensor impulse envelope1204B, and the time difference1212between the two points1208and1210provides the value for the estimated time delay between the arrival of the acoustical impulses at the near and far acoustic sensors106A and106B, respectively. This time delay value may then be utilized to calculate a speed of sound in the target pipe segment112over the known distance d to be utilized in pipe condition assessment or other algorithms requiring an accurate measurement of the speed of sound in the pipe(s).

In further embodiments, the routine400proceeds from step424to step426, where the acoustic analysis module126may refine the time delay estimate utilizing correlation of the near-sensor average impulse1104A and the far-sensor average impulse1104B. Simply correlating the signal data from the average impulses1104A and1104B may not produce an accurate time delay estimate, however, since the average impulses still contain peaks from both the initial impact of the tapping on the pipe or component as wells as from reverberation, reflections, harmonics, and the like. Instead, the acoustic analysis module126may utilize a mask to limit the data utilized for correlation to the initial portion of each average impulse1104A and1104B.

For example, as shown inFIG.13, a mask1304may be applied to the near-sensor average impulse1104A producing a masked signal1306representing the beginning of the acoustical impulses114in the near signal data502A, i.e., the arrival of the impulses from the initial impact at the near acoustic sensor106A. Peaks in the tail of the acoustic impulse signals representing reflections and other distortions are removed from the masked signal1306. According to some embodiments, the mask1304may be centered at the times corresponding to the point1208in the near-sensor estimated impulse envelope1204A utilized to determine the estimated time delay in step424. In some embodiments, the width of the mask1304may be determined from wavelength of the acoustical impulses114and may be set to include two or three periods from the leading edge. In further embodiments, the width and position of the mask1304may be parametric with optimal values determined experimentally. The same mask1304is applied to both the near-sensor average impulse1104A and the far-sensor average impulse1104B to limit the signal data for correlation.

Alternatively, the signal data502for the average impulses1104A and1104B may be limited to a certain number of peaks in the signal before and after the times corresponding to the points1208and1210in the respective estimated impulse envelopes1204A and1024B. Similarly to the properties of the mask1304, the number of peaks to utilize in the pre-point and post-point signal data502may be parametric with optimal values determined experimentally. A cross-correlation may then be performed between the respective masked signals1306or the otherwise limited signal data from the near and far average impulses1104A and1104B to produce a more precise estimate of the time delay. For example, as may be seen inFIG.14, the graph1402shows a cross-correlation between the near-sensor average impulse1104A and the far-sensor average impulse1104B without masking or other limitations, while the graph1404shows the cross-correlation between the masked signals1306corresponding to the near and far average impulses1104A and1104B. From step426, the routine400ends.

Based on the foregoing, it will be appreciated that technologies for improving predictions of the condition of pipes of a fluid distribution system by accurate time delay estimation using coherent averaging are presented herein. The above-described embodiments are merely possible examples of implementations set forth for a clear understanding of the principles of the present disclosure. Many variations and modifications may be made to the above-described embodiments without departing substantially from the spirit and principles of the present disclosure. All such modifications and variations are intended to be included within the scope of the present disclosure, and all possible claims to individual aspects or combinations and sub-combinations of elements or steps are intended to be supported by the present disclosure.

The logical steps, functions or operations described herein as part of a routine, method or process may be implemented (1) as a sequence of processor-implemented acts, software modules or portions of code running on a controller or computing system and/or (2) as interconnected machine logic circuits or circuit modules within the controller or other computing system. The implementation is a matter of choice dependent on the performance and other requirements of the system. Alternate implementations are included in which steps, operations or functions may not be included or executed at all, may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present disclosure.