Coordinated acoustic leak detection sensor sampling

A leak detection sensor may be capable of dynamically adjusting a sampling time for acoustic data based on monitored fluid flow. The leak detection sensor monitors leak detection information associated with a fluid in a fluid transporting medium over a predetermined time interval, where the leak detection information comprises flow and pressure of the fluid in the fluid transporting medium, and processing the leak detection data monitored at the sampling time in the predetermined time interval, where the sampling time is determined based at least in part on the monitored flow of the fluid.

TECHNICAL FIELD

The present disclosure generally relates to the field of leak detection systems, and more specifically to methods and apparatus for coordinating leak data sampling for improving leak detection accuracy.

BACKGROUND

Leaks in water pipes create noise as water leaves the pipe. Some known leak detection systems used in utility distribution systems (e.g. fluid utility distribution systems such as for gas and water) employ leak detection sensors with acoustic sensors that detect noise and/or characteristic sounds, which may be indicative of a potential leak. In particular, these known leak detection sensors are usually coupled to a pipe and/or portion of a fluid delivery system, and utilize parameters, such as amplitude and/or a time-history of acoustic signals or samples to determine a presence of a potential leak. Such leak detection sensors may take acoustic samples which are then sent to a headend application to be combined with samples from other leak detection sensors and analyzed to determine if there is a sound, or an acoustic signature, that is indicative of a leak.

However, use of these parameters can be inaccurate, and/or the sampled data can be masked, if the acoustic data were sampled during a period of water usage and/or localized noise.

DETAILED DESCRIPTION

A method and apparatus for coordinating leak data sampling for improving leak detection accuracy are disclosed. Some leak detection systems employ leak detectors with acoustic sensors that detect noise and/or characteristic sounds, which may be indicative of a potential leak. However, with these systems, determination of leaks and/or leak patterns can be difficult due to noise present in the system such as external noise, slow drift of leak patterns and/or irregular use (e.g., irregular or highly variable utility system usage).

These systems may be statically programmed to sample acoustic data at night when water usage and external noise are typically low. However, depending on locations of leak detection sensors of the leak detection system, there may be nearby noise sources with irregular cycles. That is, the noise may not be the lowest at night, such as a sprinkler system watering at night, a construction site active at night, a 24-hour business still operating at night, and the like.

A leak detection sensor, or an endpoint device, that supports both leak sampling capabilities and consumption recording may monitor presence of a fluid, such as water or gas, in a fluid transporting medium, such as a pipe, hose, flange, faucet, valve, and the like, herein forward referred to as a pipe or pipes, and may monitor or detect acoustic noise when the fluid consumption at a service point it is attached to, i.e., the location where the water or gas is being provided, is at a minimum over a predetermined time interval such as a day, i.e., a 24-hour period. The leak detection sensor may be a single device or comprise a plurality of devices coupled together. The plurality of devices may include, for example, a fluid consumption measuring device, a timer or timing device, an acoustic sensor, a fluid pressure measuring device, and the like, and may be coupled wirelessly or physically via communication wires and/or shared housing(s). Because when the fluid consumption is low, a noise from the fluid flow is reduced, thus making it an ideal time, which may be referred to as a quiet time or a sampling time, to sample acoustic data in the pipe for detecting a potential leak. The sampling time may also be optimized and/or predicted based on a historical flow pattern recorded by the leak detection sensor or by a meter, headend unit, or other devices coupled to the leak detection sensor over a longer period of time such as a week or a month which may be a rolling/running week or month. A diurnal flow pattern may be derived based on the historical flow pattern and utilized to potentially forecast a day of the week for the optimal recording period. Accordingly, the leak detection sensor may determine, and revise as needed, the quiet time, that is when to sample the acoustic data on a daily basis, based on the monitored fluid flow. The monitored flow includes a monitored quantity of the fluid and a flow rate of the fluid, which may be used interchangeably. In addition, the leak detection sensor may also monitor fluid pressure in the pipe at the service point. The sampling time may include one or more sampling times within a time interval if appropriate. Because the fluid pressure is generally known to be at the highest when the fluid flow is at its lowest, the fluid pressure data may also be used in determining the quite time.

While an externally powered leak detection sensor, such as one powered by an external power supply connected to an AC outlet, may continuously monitor the fluid flow, the fluid pressure, and the acoustic data, a battery-powered leak detection sensor may, instead, monitor the fluid flow, the fluid pressure, and the acoustic data periodically, for example, every five minutes for a monitoring period of five seconds, over the 24-hour period to conserve the battery power. However, for an urgent situation, such as a newly suspected leak, running diagnostics, or inspecting local pipes, the battery-powered leak detection sensor may also be controlled to continuously monitor the fluid flow, the fluid pressure, and the acoustic data for a certain period of time.

For example, a group of leak detection sensors for water may be attached to a network of water pipes in a given area, and each leak detection sensor may monitor the water flow, the water pressure, and the acoustic data over a day to determine a respective sampling time based on when a respective monitored water flow is at the lowest and/or a respective monitored pressure is at the highest during the day. Topology of the network of water pipes and a location of each leak detection sensor on the water pipes may be recorded and used in determining a leak location. Each leak detection sensor may then determine its own daily sampling time individually. Alternatively, monitored water flow and pressure data may be communicated among the group of leak detection sensors and analyzed via distributed intelligence or edge computing at one or more of the leak detection sensors to determine, or coordinate, an individual sampling time for each leak detection sensor, a common sampling time for the group of leak detection sensors, or one or more sampling times for one or more subgroups of the group of leak detection sensors based on the topology and leak detection sensor locations.

Alternatively, a controller unit, or a headend device, may receive and analyze monitored water flow and pressure data from the group of leak detection sensors and determine, or coordinate, the individual sampling time for each leak detection sensor, the common sampling time for the group of leak detection sensors, or one or more sampling times for one or more subgroups of the group of leak detection sensors based on the topology and leak detection sensor locations.

In addition to sampling the acoustic data at the sampling time, the leak detection sensor may also sample acoustic data at a time offset by a predetermined offset time interval from the sampling time comparison to avoid unexpected noise. The sampling time may also be re-determined and adjusted periodically to account for changes in water, or gas, usage patterns based on the day of the week, ex., a weekday vs. weekend, local changes, such as residential turn-over, new constructions, and seasonal changes, such as a sprinkler usage. Because acoustic information collected by the leak detection sensor may be affected by its environment and conditions, the leak detection sensor may be calibrated based on a material of the pipe, such as copper and PVC, a location, such as a distance from other leak detection sensors of the group and/or a distance from a consumption point, and an expected fluid pressure. Additionally, if a leak detection sensor detects a noise in a given time period outside of the sampling time, that leak detection sensor may signal the other leak detection sensors to sample the acoustic data to determine if the noise is local or regional.

At the sampling time, the leak detection sensor may record acoustic data present in the pipe, analyze the acoustic data by performing spectral analysis on the acoustic data, and identify a noise and/or leak signature. Examples of the spectral analysis include methods described in U.S. Pat. Nos. 10,359,335, 10,551,274, 10,704,982, and US Patent Application Publication Number 2019/0154535, entireties of which are herein incorporated by reference.

Alternatively, the acoustic data may be communicated among the group of leak detection sensors and analyzed via distributed intelligence or edge computing at one or more of the leak detection sensors to determine a leak location, or be transmitted from each leak detection sensor to the controller unit for analysis to determine the leak location based on the topology of the pipes and leak detection sensor locations. The leak location includes a pre-localized leak location. For example, if a leak detection sensor records a leak, i.e., a loud acoustic signal indicative of a leak, then a location of the leak may be estimated to be near the location of the leak detection sensor, that is, the pre-localized leak location. Based on the acoustic signal indicative of the same leak by nearby leak detection sensors, the leak location may be determined by triangulating based on the locations of the leak detection location and the topology of the pipe network. The leak detection sensors may be calibrated such that if a signal, from a location having the same distance to two leak detection sensors, was captured by the two leak detection sensors, both leak detection sensors would record the signal with equal amplitude. Cross correlation may also be performed based on a time synchronized data, i.e., captured acoustic signal.

FIG.1illustrates an example schematic diagram of a leak detection system100comprising a controller unit102and a plurality of leak detection sensors104distributed at multiple locations106over a network of pipes108. In this example, seven leak detection sensors,104A-104G, are shown to be attached to various locations,106A-106G, of the network of pipes108. In general, a leak detection sensor104may refer to a single leak detection sensor or one or more leak detection sensors.

A leak detection sensor104may monitor leak detection information, such as the flow and the pressure of the fluid and leak detection data, in the pipes108at its corresponding location106over a time interval, which may be predetermined such as a 24-hour period, and manage the leak detection data monitored at a sampling time where the sampling time may be determined based at least in part on the monitored flow and/or pressure. For example, each leak detection sensor104may collect and store the leak detection information locally and analyze the leak detection data. As shown with dotted lines, the leak detection sensors104may also communicate with each other and coordinate to manage the leak detection information and the sampling time. In addition, or alternatively, the leak detection sensors104may communicate with the controller unit102to have the controller unit102manage the leak detection information and the sampling time.

FIG.2illustrates an example graph200of the monitored flow202and the monitored pressure204by the leak detection sensors104over a portion, from time 0:00 to 6:00 AM, of the predetermined time interval.

The graphs of monitored flow202(A-G) show that the monitored flow of each leak detection sensor104is the lowest at 3:00 AM, as indicated as a solid-line circle206, and the graphs of monitored pressure204(α-γ) show that the monitored pressure of each leak detection sensor104is the highest at 3:00 AM, as indicated by a dotted-line circle208. As discussed above, the sampling time may be set to when the fluid consumption is the lowest because the noise from the fluid flow is reduced and/or minimized, in this example 3:00 AM as indicated by the solid-line circle206. Additionally, the monitored pressure204of the fluid may also be utilized in determining and setting the sampling time because the fluid pressure is generally known to be at the highest when the fluid flow is at the lowest. In this example, the highest pressure for the leak detection sensors104also occurs at 3:00 AM as indicated by the dotted-line circle208. However, the sampling time may not be the same time for all of the leak detection sensors104based on the occurrence of the minimum flow at different times for different leak detection sensors.

FIG.3illustrates an example process300for processing leak detection data monitored by the leak detection sensors104at the sampling time.

At block302, a leak detection sensor104may be monitoring leak detection information associated with a fluid, such as water or gas, in the pipes108over a time interval, which may be predetermined or dynamically determined, at a corresponding location106of the leak detection sensor104as shown inFIG.1. In this example, water as the fluid is used to describe the process. The leak detection information may comprise a flow rate of the water and leak detection data associated with the water. In addition, a pressure of the water may also be monitored and included in the leak detection information. For example, when the leak detection sensors104are initially activated, the leak detection information from each leak detection sensor104may be collected over a time interval, such as a day, several days, a week, or other desired and appropriate period of time. The leak detection information may be collected and stored individually by the leak detection sensors104, collected, stored, and distributed among the leak detection sensors104, or collected by the controller unit102as described with reference toFIG.1.

While an externally powered leak detection sensor, such as one powered by an external power supply connected to an AC outlet, may continuously monitor and collect the leak detection information, a battery-powered leak detection sensor may, instead, monitor and collect the leak detection information periodically, for example, every five minutes for a monitoring period of five seconds, over the 24-hour period to conserve the battery power.

At block304, a sampling time may be determined based on the monitored flow rate and/or the monitored pressure of the water. For example, as discussed above with reference toFIG.2, the sampling time may be determined to be when the monitored flow rate is at a minimum during the time interval because individual leak detection sensors will always have a respective minimum flow rate. For a group of a plurality of leak detection sensors, the sampling time may also be selected based on a threshold flow rate below which the flow rate is considered to be sufficiently low. For a large group of a plurality of leak detection sensors, it would be unlikely that all leak detection sensors of the group individually measure the minimum flow rate at the same time. Therefore, when or if an average flow rate associated with the group of the plurality of leak detection sensors were below a threshold flow rate, it could be determined as an appropriate time to sample. By using the threshold flow rate, it would allow determining a coordinated sample time even when a leak detection sensor of the group were measuring the maximum amount of flow rate at that moment.

Alternatively, the sampling time may be determined to be when the monitored pressure is at a maximum during the time interval or when the monitored pressure is higher than a threshold pressure, which may or may not coincide with the monitored minimum flow rate. The pressure at specific locations may be determined based on hydraulic modeling solution. With the hydraulic model over a specific geographical area of the pipe network calibrated, the pressure throughout the specific geographical area of the pipe network at multiple locations over the 24 hour period may be predicted.

In some examples, a sampling time may be selected based on some combination of flow rate and pressure. For instance, leak detection data may be sampled at a time when the flow rate is relatively low, but not at the minimum, and the pressure is relatively high, but not at the maximum. While the sampling time may be ideally when the flow rate is at the minimum and the pressure is at the maximum, the sampling time may also be determined based on the population of the leak detection sensors and their consumption patterns. For example, if some leak detection sensors were known to hear at a certain period a consumption noise that likely is a leak, the results may be weighed. For a given area, if one leak detection sensor (local leak detection sensor) makes its recordings at the quietest time and other surrounding leak detection sensors make their recordings at some other quiet time, the spectral signatures of all the leak detection sensors may analyzed to determine whether a stationary signal, or a common spectral pattern, exists that is detected by the local and surrounding leak detection sensors. This would enhance the confidence that a leak is present.

For example, when the leak detection sensors104are initially activated, the leak detection information from each leak detection sensor104may be collected over the time interval, such as a day, several days, a week, or other desired and appropriate period of time. The leak detection information may be collected and stored individually by the leak detection sensors104, collected, stored, and distributed among the leak detection sensors104, or collected and stored by the controller unit102as described with reference toFIG.1. Additionally, the time interval, for example, a 24-hour period, may be repeated periodically to revise the sampling time as needed or desired, and the sampling time may include one or more sampling times within the time interval if appropriate. The leak detection sensor104may also sample the leak detection data at a time offset by a predetermined offset time interval from the sampling time for comparing the leak detection data. As discussed with reference toFIG.1, the sampling time may be determined by each leak detection sensor104individually based on respective leak detection information, by coordinating among the leak detection sensors104based on leak detection information from some or all of the leak detection sensors104, or by the controller unit102based on leak detection information from some or all of the leak detection sensors104. The sampling time may be determined to be a common sampling time for the leak detection sensors104.

Once the sampling time is determined, the leak detection sensors104may manage the leak detection data corresponding to the sampling time at block306. For example, an individual leak detection sensor104may analyze the leak detection data, such as acoustic data collected at the sampling time, by converting the acoustic data in the time domain to frequency domain data, via Fourier transform for example, to produce corresponding spectral representation and performing spectral analysis of the spectral representation to identify a noise and/or leak signature. Alternatively, the acoustic data may be communicated among the leak detection sensors104and analyzed via distributed intelligence or edge computing at one or more of the leak detection sensors104or be transmitted from each leak detection sensor104to the controller unit102for the analysis. At block308, the leak detection sensors104, via distributed intelligence or edge computing at one or more of the leak detection sensors104, or the controller unit102, may determine the leak location based on the spectral analysis, the topology of the pipes108, and leak detection sensor locations106.

FIG.4illustrates an example spectral graph400representing the acoustic data collected by the leak detection sensor104at the sampling time. As discussed above, the spectral graph400may be generated by transforming the acoustic data from the time domain to the frequency domain via Fourier transform, and analyzed by the leak detection sensor104or by the controller unit102. The spectral graph400shows an example comparison of a baseline spectral pattern402with no leak (solid line) to a current spectral pattern with a leak signal where the difference indicates a leak signal404(dotted line). The baseline spectral pattern402may be averaged over multiple sampling times or over multiple samples from the leak detection sensors104at the same sampling time. The spectral graph400includes a legend406, a vertical axis408indicating an acoustic amplitude, and a horizontal axis410indicating an acoustic frequency in Hertz (Hz).

In some examples, the difference between the current spectral pattern and the baseline spectral pattern402is determined and/or calculated by taking an integral of a difference between the current spectral pattern and the baseline spectral pattern402. In other words, sum areas of the current spectral pattern and the baseline spectral pattern402may be subtracted from one another to determine the difference. Additionally, or alternatively, overall shapes of waveforms corresponding to the current spectral pattern and the baseline spectral pattern402may be compared to one another to determine this difference, which may be referred to as a correlation.

In some examples, the difference between the current spectral pattern and the baseline spectral pattern402may be determined by quantifying a maximum numerical difference in amplitude. In particular, the maximum difference at a certain frequency between the current spectral pattern and the baseline spectral pattern402may be selected to determine a leak condition, for example.

Additionally, or alternatively, at least one peak and/or characteristic shape of the current spectral pattern, for example, the leak signal404, may be tracked as it shifts over time (e.g., while generally retaining aspects of its characteristic waveform) and this shift may also be considered when comparing the current spectral pattern to the baseline spectral pattern402. In such examples, spectral tracking can be effective at tracking peak and/or waveform shifts based on edges of the current spectral pattern that are clearly defined in the frequency domain.

FIG.5illustrates an example block diagram of a leak detection sensor104A as an example of the leak detection sensors104.

The leak detection sensor104A may be attached to the pipes108as discussed with reference toFIG.1. The leak detection sensor104A may comprise one or more processors (processors)502communicatively coupled to memory504. The processors502may include one or more central processing units (CPUs), graphics processing units (GPUs), both CPUs and GPUs, or other processing units or components known in the art. The processors502may execute computer-executable instructions stored in the memory504to perform functions or operations with one or more of components communicatively coupled to the one or more processors502and the memory504as described above with reference toFIGS.1-4. Depending on the exact configuration of the leak detection sensor104A, the memory504may be volatile, such as RAM, non-volatile, such as ROM, flash memory, miniature hard drive, memory card, and the like, or some combination thereof. The memory504may store computer-executable instructions that are executable by the processors502.

A fluid flow monitor506for monitoring flow of the fluid in the pipes108, an acoustic sensor508for monitoring leak detection data associated with the fluid in the pipes108, a fluid pressure monitor510for monitoring pressure of the fluid in the pipes108, and a leak detection data manager512for processing the leak detection data monitored at a sampling time.

As discussed above, with reference toFIGS.1-3, the leak detection sensor104A may monitor leak detection information in the pipes108, such as the flow of the fluid via the fluid flow monitor506, the pressure of the fluid via the fluid pressure monitor510, and the leak detection data via the acoustic sensor508, at its corresponding location106over a time interval, such as a 24-hour period. The leak detection data manager512may determine the sampling time based at least in part on the monitored flow of the fluid by the fluid flow monitor506and/or the monitored pressure of the fluid by the fluid pressure monitor510. For example, the leak detection sensor104A may collect and store the leak detection information locally in the memory504and analyze the leak detection data. The leak detection data manager512may then determine the sampling time to be when the monitored flow is at a minimum during the time interval, or when the monitored pressure is at a maximum during the time interval. Additionally, the leak detection data manager512may perform spectral analysis of the leak detection data and generate a spectral analysis result.

The leak detection sensor104A may further comprise a transceiver514for establishing a communication with one or more other leak detection sensors or with the controller unit102. The transceiver514may transmit the leak detection data, which may include the acoustic data, spectral representation of the acoustic data, and the spectral analysis result, to a controller unit102or to one or more other leak detection sensors104(the leak detection sensors104B-104G are shown in this example). The leak detection data may be communicated among the leak detection sensors104as shown inFIG.1and analyzed via distributed intelligence or edge computing at one or more of the leak detection sensors104. Alternatively, the leak detection data may be transmitted from each leak detection sensor104to the controller unit102for the analysis. Additionally, the transceiver may communicate with the one or more other leak detection sensors104, exchange the respective monitored flow of the fluid in the pipes108, and the leak detection data manager512may determine a common sampling time for all or a sub-group of the leak detection sensors104.

The processors502, via distributed intelligence or edge computing at one or more of the leak detection sensors104, or the controller unit102, may then determine a leak location based on the spectral analysis, the topology of the pipes108, and leak detection sensor locations106.

FIG.6illustrates an example block diagram of a controller unit102.

The controller unit102may comprise one or more processors (processors)602communicatively coupled to memory604. The processors602may include one or more central processing units (CPUs), graphics processing units (GPUs), both CPUs and GPUs, or other processing units or components known in the art. The processors602may execute computer-executable instructions stored in the memory604to perform functions or operations with one or more of components communicatively coupled to the one or more processors602and the memory604as described above with reference toFIGS.1-4. Depending on the exact configuration of the controller unit102, the memory604may be volatile, such as RAM, non-volatile, such as ROM, flash memory, miniature hard drive, memory card, and the like, or some combination thereof. The memory604may store computer-executable instructions that are executable by the processors602.

The controller102may comprise a transceiver606for receiving the leak detection information from one or more leak detection sensors104and a sampling time module for determining the sampling time based on the leak detection data. As described above with reference toFIGS.1-3and5, the controller unit102, that is a sampling time processor608of the controller unit102, may determine, or coordinate, the individual sampling time for each leak detection sensor, the common sampling time for a group of leak detection sensors, or one or more sampling times for one or more subgroups of the group of leak detection sensors based on the topology and leak detection sensor locations. The received leak detection information and the sampling time(s) may be in the memory604. The sampling time(s) may then be communicated with, or transmitted to, via the transceiver606, to the leak detection sensors104.

The controller unit102may further comprise a leak detection data analyzer610for analyzing the leak detection data received from the leak detection sensors104. The leak detection data analyzer610may analyze the leak detection data, such as acoustic data collected at the sampling time, by converting the acoustic data in the time domain to frequency domain data, via Fourier transform for example, to produce corresponding spectral representation and performing spectral analysis of the spectral representation to identify a noise and/or leak signature. The leak detection data analyzer may then determine a leak location based on the spectral analysis, the topology of the pipes108, and leak detection sensor locations106.

The controller unit102may further comprise a communication module612allowing the controller unit102to communicate with other device(s), system(s), a central office, and/or a user working with the controller unit102, collectively shown as an entity614over a network616. For example, the controller unit102, via the communication module612, may communicate the leak location to a utility worker trouble-shooting the pipes108for a leak. The network616may include the Internet, wired media such as a wired network or direct-wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, cellular networks, and other wireless media.

Some or all operations of the methods described above can be performed by execution of computer-readable instructions stored on a computer-readable storage medium, as defined below. The term “computer-readable instructions” as used in the description and claims, include routines, applications, application modules, program modules, programs, components, data structures, algorithms, and the like. Computer-readable instructions can be implemented on various system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, personal computers, hand-held computing devices, microprocessor-based, programmable consumer electronics, combinations thereof, and the like.

The computer-readable storage media may include volatile memory (such as random-access memory (RAM)) and/or non-volatile memory (such as read-only memory (ROM), flash memory, etc.). The computer-readable storage media may also include additional removable storage and/or non-removable storage including, but not limited to, flash memory, magnetic storage, optical storage, and/or tape storage that may provide non-volatile storage of computer-readable instructions, data structures, program modules, and the like.

A non-transitory computer-readable storage medium is an example of computer-readable media. Computer-readable media includes at least two types of computer-readable media, namely computer-readable storage media and communications media. Computer-readable storage media includes volatile and non-volatile, removable and non-removable media implemented in any process or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, phase change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism. As defined herein, computer-readable storage media do not include communication media.

The computer-readable instructions stored on one or more non-transitory computer-readable storage media, when executed by one or more processors, may perform operations described above with reference toFIGS.1-6. Generally, computer-readable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes.

CONCLUSION