Patent Description:
The disclosure herein generally relates to leak event detection system, and, more particularly, to method and system for unobstrusive automatic leak event detection in real-time conduit by template selection.

Oil and Gas is a common source to meet ever-increasing energy demand in the world. The oil and gas transport from one location to another location via pipelines. These pipelines run inter-country to inter-continents. One of the biggest challenges faced by oil and gas companies is to monitor such long pipelines for leak events, as unattended leaks may cause a catastrophe (i.e., environmental hazards, life loss, economic loss etc.). Various techniques have been developed in the past for real time monitoring of these pipelines. A fundamental problem with most of the existing nondestructive testing (NDT) techniques (i.e., ultrasound, and thermal camera etc.) is that they can only monitor leaks locally (i.e., max ~<NUM> meters from one or more sensors) and one needs to put multiple sensors very frequently to monitor pipelines that run for hundreds of kilometers. This makes the solution not feasible both from cost and maintenance perspective. However, one sensing modality called a negative pressure wave (NPW) can monitor leaks from a very long distance (i.e., ~<NUM>) and can be detected since low frequencies have significantly less attenuation in fluids. The NPW consists of low frequency waves (i.e., ~<NUM> to <NUM>) that are generated by sudden leak events and wavefront travels at speed of sound inside fluid in both upstream and downstream directions. Most existing NPW techniques rely on invasive pressure sensors for their enhanced reliability. Accelerometer-based NPW techniques, although more attractive to industrial use-cases due to their unobtrusive, easy-to-install nature and maintainability, come with their challenges. It is very difficult to discriminate the NPW signatures from an unknown signatures.

Machine learning (ML) based approach requires multiple trials which further complicates lab-based model efforts. Further, a model generation is way more complicated, depends on multiple sensor input and correct parameter values and is computationally time consuming. Another approach which utilizes Kalman filter-based model highly depends on choosing proper process covariance matrix which requires extensive tuning efforts, and the convergence is not always guaranteed. Another challenge with the Kalman filter is linearization of the highly non-linear systems with gaussian noise approximation. As all systems are non-linear and for small data lengths both measurement and process noise are random in nature. Accordingly, the false alarm rate is a big issue in case of oil and gas pipeline leak detection. The sensors are susceptible to environmental noises and are generally likely to generate false leak event alarms during routine pipe maintenance jobs. The document <CIT> discloses that two accelerometers and/or hydrophones are deployed on a pipeline so that the straddle a suspected leak position, and acoustic signals emanating from the leak are recorded. A pressure transducer is also deployed on the pipeline in the vicinity of one or both of the two accelerometers and/or hydrophones. The pressure pattern, or a mathematically adjusted version of it, is correlated with each of the two accelerometer/hydrophone vibration patterns and the time delays maximizing the cross correlation function in each case are used to estimate the position of the leak. Measuring the pressure signals allows the information relating to leak noise to be extracted from the vibration signals even if this information is obscured by ambient noise. The document <CIT> discloses about an apparatus for the continuous monitoring of a pipeline or a pipeline network carrying flowing media that can not only detect the presence of a leak but also locate the source of the leak through the use of rarefaction wave detection and a method of using the same. The apparatus and method are specifically configured to locate the leak source within less than <NUM> inches using a calibration means and a noise cancellation means. The document <CIT> provides a method and system for continuous remote monitoring of integrity of pressurized pipelines and properties of fluids transported, the method including: installing plural measurement stations along the pipeline, connected to vibroacoustic sensors configured to simultaneously and continuously measure elastic signals propagating in walls of the pipeline, and acoustic signals propagating in the transported fluid; synchronizing the signals measured from different measurement stations, with absolute time reference; continuously transmitting the measured and synchronized signals to a central unit configured to process them in a multichannel mode; calculating, by the central unit, plural transfer functions that can define vibroacoustic propagation in sections of pipeline between consecutive measurement stations; filtering relevant acoustic and elastic signals from the different measurement stations subtracting the contribution relating to the passive sources; creating an equivalent descriptive model of the system including the fluid transported, pipeline and external medium surrounding the pipeline, using the transfer functions. <NPL> states that pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localization using different approaches. This paper discusses pipeline leakage detection technologies and summarizes the state-of-the-art achievements. Different leakage detection and localization in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. <NPL> states that Negative pressure wave is a popular method to detect the occurrence and location of leak incidents in oil/gas pipeline. Three core technical challenges and related algorithm are discussed in this paper. The first is data quality. The balance between noise level and locating precision is discussed in filter design. The second one is dynamic slope in anomaly detection, whence a bi-SPC (Static Process Control) algorithms is proposed to make the threshold be adaptive. The third one is the false alarm caused normal working condition changes. Multiple-sensor paring algorithms is presented.

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. The invention is set out in appended set of claims.

There is a need for unobtrusively and automatic leak event detection in a real-time conduit. Embodiments of the present disclosure provide a template matching technique for unobtrusive accelerometer-based leak event detection to isolate negative pressure wave (NPW) signatures from other vibrational signatures on the real-time conduit.

<FIG> illustrates a block diagram of a system <NUM> for automatic detection of a leak event in a real-time conduit <NUM> by a template selection, according to an embodiment of the present disclosure. In an embodiment, the system <NUM> includes one or more processor(s) <NUM>, communication interface device(s) or input/output (I/O) interface(s) <NUM>, and one or more data storage devices or a memory <NUM> operatively coupled to the one or more processors <NUM>. The memory <NUM> includes a database (Not shown in Figure). The one or more processor(s) processor <NUM>, the memory <NUM>, and the I/O interface(s) <NUM> may be coupled by a system bus such as a system bus <NUM> or a similar mechanism. The one or more processor(s) <NUM> that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more processor(s) <NUM> is configured to fetch and execute computer-readable instructions stored in the memory <NUM>. In an embodiment, the system <NUM> can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud, and the like.

The I/O interface device(s) <NUM> can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface device(s) <NUM> may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. Further, the I/O interface device(s) <NUM> may enable the system <NUM> to communicate with other devices, such as web servers and external databases. The I/O interface device(s) <NUM> can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. In an embodiment, the I/O interface device(s) <NUM> can include one or more ports for connecting number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory <NUM> includes a plurality of modules <NUM> and a repository <NUM> for storing data processed, received, and generated by the plurality of modules <NUM>. The plurality of modules <NUM> may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.

Further, the database stores information pertaining to inputs fed to the system <NUM> and/or outputs generated by the system (e.g., data/output generated at each stage of the data processing) <NUM>, specific to the methodology described herein. More specifically, the database stores information being processed at each step of the proposed methodology.

Additionally, the plurality of modules <NUM> may include programs or coded instructions that supplement applications and functions of the system <NUM>. The repository <NUM>, amongst other things, includes a system database <NUM> and other data <NUM>. The other data <NUM> may include data generated as a result of the execution of one or more modules in the plurality of modules <NUM>. Further, the database stores information pertaining to inputs fed to the system <NUM> and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. Herein, the memory for example the memory <NUM> and the computer program code configured to, with the hardware processor for example the processor <NUM>, causes the system <NUM> to perform various functions described herein under.

<FIG> is functional block diagram of the system <NUM> of <FIG> illustrating the template selection phase, according to an embodiment of the present disclosure. The system 200A may be an example of the system <NUM> (<FIG>). In an example embodiment, the system 200A may be embodied in, or is in direct communication with the system, for example the system <NUM> (<FIG>). The system 200A includes a test environment <NUM>, a conduit <NUM>, a first sensing unit 206A, a second sensing unit 206B, a leak event instant time detection unit <NUM>, a bandpass filter <NUM>, a leak event duration detection unit <NUM>, and a temporal template signal generation unit <NUM>. The test environment <NUM> in which the conduit <NUM> (e.g., a pipeline) is constructed with a fluid running through. The conduit <NUM> at the test environment <NUM> is constructed based on (a) a pipe diameter, (b) a pipe thickness, (c) a material, (d) a flowing fluid, and (e) a pressure level at the conduit <NUM>. The first sensing unit 206A corresponds to a pressure sensor for obtaining a pressure data (Pr). The second sensing unit 206B corresponds to an accelerometer sensor for obtaining the accelerometer data (Accel).

In an embodiment, the pressure sensor 206A is placed inside the conduit <NUM> of the test environment <NUM>. In an embodiment, the accelerometer sensor 206B is placed on outer surface of the conduit <NUM> of the test environment <NUM>. An air compressor is utilized to create leaks in a pressurized pipe and exploiting an accelerometer sensor to pick up the negative pressure wave (NPW) signatures on surface of the pipeline. The leak event instant time detection unit <NUM> is configured to process the pressure data (Pr) obtained from the pressure sensor 206A to obtain an instant timing information (T<NUM>) of a leak event in the conduit <NUM> at the test environment <NUM>. The instant timing information (T<NUM>) is obtained by detecting a variation in a steady state of a pressure value of the fluid in the conduit <NUM> at the test environment <NUM>. The accelerometer data (Accel) obtained from the accelerometer sensor 206B is processed to obtain a transient signal associated with the leak event at a specific band by applying a continuous wavelet transformation (CWT) (i.e., joint time-frequency analysis). The transient signal corresponds to a pressure wavefront travelling through a fluid inside the conduit <NUM> at the test environment <NUM>.

The bandpass filter <NUM> is configured to filter the accelerometer data (Accel) to obtain a band passed filtered accelerometer signal (Accelbpf) with a low pass cut-off frequency and a high pass cut-off frequency. In an embodiment, the Accel is band passed filtered (Accelbpf) between <NUM> to <NUM> and the duration of the leak event from the CWT plot is noted (Td). The leak event duration detection unit <NUM> is configured to process the band passed filtered accelerometer signal (Accelbpf) to obtain the duration (Td) of the leak event in the conduit <NUM> at the test environment <NUM>. The temporal template signal generation unit <NUM> is configured to truncate the band passed filtered accelerometer signal (Accelbpf) in a time domain from the instant timing information (T<NUM>) to the duration (Td) of the leak event to obtain a temporal template signal (Acceltemplate). In an embodiment, in time domain the Accelbpf is truncated from T<NUM> to T<NUM> + Td.

<FIG> is functional block diagram of the system <NUM> of <FIG> illustrating the leak event detection phase based on the template selection, according to an embodiment of the present disclosure. The system 200B further includes a real-time conduit <NUM> at a physical environment <NUM>, a sensing unit 206B, a bandpass filter <NUM>, a leak event detection unit <NUM>, and an alarm unit <NUM>. The second sensing unit 206B corresponds to an accelerometer sensor for obtaining the accelerometer data (Accel). In an embodiment, the accelerometer sensor 206B is unobtrusively placed on outer surface of the real-time conduit <NUM> of the physical environment <NUM> and data is recorded continuously. A band passed filtered accelerometer signal (Accelbpf) obtained from the bandpass filter <NUM> of the real-time conduit <NUM> at the physical environment <NUM> is cross correlated with the temporal template signal (Acceltemplate) for every successive window of T<NUM> to Td length to obtain a cross correlation value. The leak event detection unit <NUM> is configured to dynamically detect a leak event of a real-time conduit <NUM> at a physical environment <NUM> when the cross-correlation value is greater than a threshold value (oc) and subsequently an alarm notification is communicated from the alarm unit <NUM>.

In an embodiment, threshold value (∝) may vary from one setup to another i.e., depending upon the pipe length, the pipe diameter etc. In an embodiment, cross-correlation of non-leak vibrational event data with Acceltemplate data, an ∝ value of <NUM> separates most leak events from other vibrational generating events. The cross correlation which is a measurement of similarity of two signals. Consider two signals X<NUM>(t) and X<NUM>(t). The cross correlation of the two signals R<NUM> (τ) is given by: <MAT> where X<NUM>(t) is the Accel data collected from the real-time conduit and X<NUM>(t) is the Acceltemplate data generated from the test conduit.

<FIG> and <FIG> are exemplary flow diagrams illustrating a method <NUM> of detecting the leak event in the real-time conduit <NUM> based on the template selection, according to an embodiment of the present disclosure. In an embodiment, the system <NUM> comprises one or more data storage devices or the memory <NUM> operatively coupled to the one or more hardware processors <NUM> and is configured to store instructions for execution of steps of the method by the one or more processors <NUM>. The flow diagram depicted is better understood by way of following explanation/description. The steps of the method of the present disclosure will now be explained with reference to the components of the system as depicted in <FIG>, <FIG>, and <FIG>.

At step <NUM>, data associated with the first sensing unit 206A and the second sensing unit 206Bare received. The first sensing unit and the second sensing unit are placed in a proximity of the conduit <NUM> at the test environment <NUM>. In an embodiment, the first sensing unit 206A corresponds to the pressure sensor for obtaining the pressure data (Pr). In an embodiment, the second sensing unit 206B corresponds to the accelerometer sensor for obtaining the accelerometer data (Accel). In an embodiment, the conduit <NUM> at the test environment <NUM> is constructed based on (a) a pipe diameter, (b) a pipe thickness, (c) a material, (d) a flowing fluid, and (e) a pressure level at the real-time conduit <NUM>. At step <NUM>, the data associated with the first sensing unit 206A is processed to obtain an instant timing information (T<NUM>) of a leak event in the conduit <NUM> at the test environment <NUM>. In an embodiment, the instant timing information (T<NUM>) is obtained by detecting a variation in a steady state of a pressure value of a fluid at the conduit <NUM> at the test environment <NUM>. At step <NUM>, the data associated with the second sensing unit 206B to obtain a transient signal associated with the leak event at a specific band by applying a continuous wavelet transformation (CWT). In an embodiment, the transient signal corresponds to a pressure wavefront travelling through a fluid inside the conduit <NUM> at the test environment <NUM>. At step <NUM>, the accelerometer data (Accel) is filtered by the bandpass filter <NUM> to obtain the band passed filtered accelerometer signal (Accelbpf) with the low pass cut-off frequency and the high pass cut-off frequency. At step <NUM>, the band passed filtered accelerometer signal (Accelbpf) is processed to obtain the duration (Td) of the leak event. At step <NUM>, the band passed filtered accelerometer signal (Accelbpf) is truncated in the time domain from the instant timing information (T<NUM>) to the duration (Td) of the leak event to obtain the temporal template signal (Acceltemplate). At step <NUM>, the band passed filtered accelerometer signal (Accelbpf) obtained from the bandpass filter <NUM> of the real-time conduit <NUM> at the physical environment <NUM> is cross correlated with the temporal template signal (Acceltemplate) for every successive window of T<NUM> and Td length to obtain a cross correlation value. At step <NUM>, the leak event of the real-time conduit <NUM> at the physical environment <NUM> is dynamically detected when the cross-correlation value is greater than a threshold value (∝) and subsequently an alarm notification is communicated from the alarm unit <NUM>.

For example, a study is conducted to detect the leak event in the real-time conduit <NUM> based on the template selection. The air compressor is attached to the inlet of the pipe to pressurize the pipeline. The parameters for constructing a conduit at a test environment is depicted below in table <NUM>.

The compressor can generate pressure up to <NUM>-<NUM> PSI. Safety valves are present in this pipeline setup to avoid any unprecedented event. The sensors used for the experiment is accelerometer sensor and are completely non-invasive. The sensors are placed at the two ends of the pipeline. One pressure sensor is also used in this experimental setup. A manual leak valve is placed around middle of the pipe. The distance between two sensors is <NUM>. Leak point consist of a leak value that can be manually opened through a pulley system. When leak happens NPW is generated from a leak orifice and the NPW has low frequency components which can travel very long distance in both upstream and downstream.

<FIG> is an exemplary graphical representation which illustrates the pressure sensor data (Pr) and the accelerometer data (Accel) at the time domain received from the pressure sensor and the accelerometer sensor respectively, according to an embodiment of the present disclosure. <FIG> is an exemplary graphical representation which illustrates the bandpass filtered accelerometer signal (Accelbpf) and the pressure sensor data at <NUM> PSI, according to an embodiment of the present disclosure. <FIG> is an exemplary graphical representation which illustrates the temporal template signal (Acceltemplate) of the bandpass filtered accelerometer signal (Accelbpf), according to an embodiment of the present disclosure. The bandpass filtered accelerometer data i.e., Accelbpf and Pr data as depicted in <FIG>. The T<NUM> is measured from Pr offset, whereas Td is calculated from the CWT (shown in inset) of Accelbpf. The T<NUM> is <NUM> sec and Td is <NUM> sec. Next, Acceltemplate is obtained by truncating Accelbpf from T<NUM> to (T<NUM> + Td) i.e., from <NUM> sec to <NUM> sec and is depicted in <FIG>. In <FIG>, the Acceltemplate is cross-correlated with a new Accelbpf data collected at <NUM> PSI where the leak was done at <NUM> sec and a max cross-correlation was found exactly at <NUM> sec. The same process is repeated at different PSI levels and the max cross-correlation occurred at the exact leak instant as detected by the Pr data with a max cross-correlation value greater than ∝.

Embodiment of the present disclosure provides a template matching based approach for unobtrusive accelerometer-based leak event detection to isolate negative pressure wave (NPW) signatures from other vibrational signatures on the real-time conduit. The embodiment of the present disclosure offers complete non-invasive yet robust solution for capturing very low frequency NPW waves. The selected template is exactly tuned to the actual world pipeline at hand there is no need for a leak model/simulation. The proposed approach provides a choice of the BPF range is specific for one pipeline under test. The proposed approach reduces the false alarm rate. The template selection approach is parameter agnostic. The proposed approach treats the entire pipeline system as a nonlinear system and treats leak signature/template as a weighted sum of sinusoids of different frequencies. The frequencies are obtained from a small-scale lab setup of the actual pipeline with all parameters (e.g., pipe diameter, material, flowing fluid, pressure level) same. Hence, immune to environmental noises and/or other vibrations generated through non-leak events. The proposed approach is non-invasive in nature, suitable for larger distances and computationally much simpler as, during the template generation phase and actual field deployment, band restricting the accel signal which eliminates unnecessary computations, and the template guarantees proper match with the leak event.

Thus, the means can include both hardware means, and software means.

also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

Claim 1:
A processor implemented method (<NUM>), comprising:
receiving, via one or more hardware processors, data associated with a first sensing unit and a second sensing unit, wherein the first sensing unit and the second sensing unit are placed in a proximity of a conduit at a test environment (<NUM>);processing, via the one or more hardware processors, the data associated with the first sensing unit to obtain an instant timing information (T<NUM>) of a leak event in the conduit at the test environment (<NUM>), wherein the first sensing unit corresponds to a pressure sensor for obtaining a pressure data (Pr), wherein the instant timing information (T<NUM>) is obtained by detecting a variation in a steady state of a pressure value of a fluid at the conduit at the test environment;
processing, via the one or more hardware processors, the data associated with the second sensing unit to obtain a transient signal associated with the leak event at a specific band by applying a continuous wavelet transformation (CWT) (<NUM>), wherein the second sensing unit corresponds to an accelerometer sensor for obtaining the accelerometer data (Accel), wherein the transient signal corresponds to a pressure wavefront travelling through a fluid inside the conduit at the test environment;
filtering, by a bandpass filter, the accelerometer data (Accel) to obtain a band passed filtered accelerometer signal (Accelbpf) with a low pass cut-off frequency and a high pass cut-off frequency (<NUM>);
processing, via the one or more hardware processors, the band passed filtered accelerometer signal (Accelbpf) to obtain a duration (Td) of the leak event (<NUM>);
truncating, via the one or more hardware processors, the band passed filtered accelerometer signal (Accelbpf) in a time domain from the instant timing information (T<NUM>) to the duration (Td) of the leak event to obtain a temporal template signal (Acceltemplate) (<NUM>);
cross-correlating, via the one or more hardware processors, a band passed filtered accelerometer signal (Accelbpf) associated with a real-time conduit and the temporal template signal (Acceltemplate) for every successive window of the instant timing information (T<NUM>) to the duration (Td) length to obtain a cross correlation value (<NUM>); and
dynamically detecting, via the one or more hardware processors, a leak event of the real-time conduit when the cross-correlation value is greater than a threshold value (∝) (<NUM>).