The present invention relates generally to fields of Cathodic protection (CP), and more specifically, to non-contact condition monitoring (NCM) autonomous ground vehicle (AGV) for inspecting conductive protective layer and possible defect mapping and the associated method thereof.
BACKGROUND
[0002] Condition monitoring and defect inspection in the buried oil and gas pipelines, made of metals, has always been a challenge for all organizations operating in the Oil and Gas sector. Metallic surfaces of the pipelines, especially of the buried oil and gas pipelines, are adversely affected by numerous corrosive electrolytic fluids that contact these surfaces. In the natural gas and petroleum industries, e.g., corrosion occurs extensively on the outer surface of both implanted and above-ground pipelines.
[0003] In order to reduce, or entirely eliminate this undesirable metallic surface corrosion, anti-corrosion protective coatings are extensively used in the pipeline industry. These ubiquitous anti-corrosion protective coatings frequently take the form of a helically-applied tape-like protective outerwrapping. The tape-like protective component may be applied directly over an unprepared pipeline outer surface, or may, in fact, be overlaid onto a primer-coated, pretreated pipeline outer surface.
[0004] An important measurable parameter directly relating to the performance of anti-corrosion pipeline protective coatings is that of cathodic disbondment. This property is defined as the extent to which an anti-corrosion protective coating overlaying a metallic surface will disbond as a result of a cathodic reaction, around an unintentionally-induced holiday, or discontinuity, in the protective coating, in a case where the pipe has been subjected to cathodic protection potentials in the soil environment.
[0005] Cathodic protection as it is used here refers to the phenomenon of applying a small potential to a metallic pipeline that is buried in the ground. This imparted cathodic status of the buried pipeline will tend to limit or protect against corrosion attacking the metal surface.
[0006] Cathodic protection is a technique used to control the corrosion of a metal surface by making it the cathode of an electrochemical cell. A simple method of protection connects the metal to be protected to a more easily corroded "sacrificial metal" to act as the anode. The sacrificial metal then corrodes instead of the protected metal. For structures such as long pipelines, where passive galvanic cathodic protection is not adequate, an external DC electrical power source is used to provide sufficient current.
[0007] Cathodic protection systems protect a wide range of metallic structures in various environments. Common applications are: steel water or fuel pipelines and steel storage tanks such as home water heaters; steel pier piles; ship and boat hulls; offshore oil platforms and onshore oil well casings; offshore wind farm foundations and metal reinforcement bars in concrete buildings and structures. Another common application is in galvanized steel, in which a sacrificial coating of zinc on steel parts protects them from rust. Cathodic protection can, in some cases, prevent stress corrosion cracking.
[0008] Hazardous product pipelines are routinely protected by a coating supplemented with cathodic protection. An impressed current cathodic protection system (ICCP) for a pipeline consists of a DC power source, often an AC powered transformer rectifier and an anode, or array of anodes buried in the ground (the anode groundbed).
[0009] The DC power source would typically have a DC output of up to 50 amperes and 50 volts, but this depends on several factors, such as the size of the pipeline and coating quality. The positive DC output terminal would be connected via cables to the anode array, while another cable would connect the negative terminal of the rectifier to the pipeline, preferably through junction boxes to allow measurements to be taken.
[0010] Anodes can be installed in a groundbed consisting of a vertical hole backfilled with conductive coke (a material that improves the performance and life of the anodes) or laid in a prepared trench, surrounded by conductive coke and backfilled. The choice of groundbed type and size depends on the application, location and soil resistivity. The DC cathodic protection current is then adjusted to the optimum level after conducting various tests including measurements of pipe-to-soil potentials or electrode potential.
[0011] It is sometimes more economically viable to protect a pipeline using galvanic (sacrificial) anodes. This is often the case on smaller diameter pipelines of limited length. Galvanic anodes rely on the galvanic series potentials of the metals to drive cathodic protection current from the anode to the structure being protected.
[0012] It is known that the adequacy of an underground pipeline cathodic protection system is assessed by measuring potential and current of a steel probe placed near the pipeline at depth that is under the same environmental conditions. A saturated copper/copper sulphate reference electrode (CSE) is also installed in the vicinity of the steel probe. The steel probe, simulating a coating defect, is electrically connected to a pipeline. Off-potential without IR-drop is measured with respect to a CSE by interrupting the steel probe and a pipeline connection. Recently, this method termed instant-off method has been widely used to assess cathodic protection conditions.
[0013] The prior art methods also describes accelerated procedures for the determination of the cathodically disbonded area by means of exposure of the test pipe segment with its adherent anti-corrosion protective coating to a salt electrolyte solution, for a period of from 30 to 90 days, following the cutting of the protective coating in the form of an intentionally-induced holiday, and with a potential being applied to the system.
[0014] The present situation is that the buried environment of pipelines is becoming more and more aggressive and the opportunities for AC corrosion are being expanded. One of the most important drawback in above and other available techniques of accessing or inspecting condition of underground pipeline cathodic protection system is that it is very difficult to identify the precise location or point of break i.e., were exactly the disconnection or break of the current/signal has occurred. Thus, to sort this issue, prior-art shows a prominent technique of according to which an analyzer (a human being) needs to carry a pipe locator device (Hand-held inspection sensors) and detect the possible location or point of break i.e., were exactly the disconnection or break of the current/signal has occurred to perform the control or repair measures or maintainace. Such pipe locators can be a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation.
[0015] However, as visible in FIGs. 1A and 1B, there three major problems with this conventional technique. One problem is that as the pipes are buried underground it is a difficult and tedious task for an analyzer (a human being) to detect presence of pipe underground leading to become a time consuming and labor cost increasing activity. Second problem is that even if the analyzer (a human being) is able to detect presence of pipe underground, such pipe underground is normally does not always travel straight underground but are normally curved and travelling in any direction which may not be known to the analyzer (a human being) thus leading to loss of track of the pipe underground and thereby investing additional time and efforts for again detecting the pipe underground. More precisely, walk/drive along the buried pipeline structure without having any idea about the pipe track is practically impossible. Third problem is that, under broadest possible assumption, even if such analyzer (a human being) is able to track or maintain track of the pipe underground, it becomes very difficult to find the were exactly the disconnection or break of the current/signal has occurred to perform the control or repair measures or maintenance.
SUMMARY
[0016] The present invention overcomes the above-described and other problems and disadvantages in the prior art.
[0017] In response to the above-described and other problems and disadvantages in the prior art, the present invention provides A non-contact condition monitoring (NCM) autonomous ground vehicle (AGV) (102) for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground.
[0018] The present invention provides a capability of predicting the yet to happen future anomalies in the pipeline is not possible with the conventional inspection methods. So, the predictive maintenance mechanism based on Artificial Intelligence (AI) and Machine Learning (ML) for inspection and management of the buried metallic pipes are also integrated in the present invention.
[0019] The present invention provides is aimed at bringing automation and digitalization to the facility integrity management of field-based industries like oil and gas, and power. The present invention replaces and complements the inefficient and risky manual inspection with a new alternative robotic inspection which will be not only efficient in terms of resource allocation and improving safety factor but also significantly reduce spending of companies on regular inspection, maintenance and repair (IMR) tasks. As described earlier the gap between two consecutive ILI inspections can be adequately filled by this newly proposed NCM inspection method so that critical decisions regarding the repair and maintenance of pipelines can be taken before any severe incident takes place. This predictive maintenance capability can enhance the overall life of the pipeline with timely interventions minimizing the overall maintenance and repair cost.
[0020] The present invention provides a robotic inspection mechanism i.e., an autonomous ground vehicle (AGV) NCM has 4 components, 1) autonomous robotic platforms (Autonomous Ground Vehicle -AGV), 2) non-contact location and inspection sensors (a first non-contact sensor (a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation), 3) Artificial Intelligence (AI) and Machine Learning (ML) based sensor data processing and 4) cloud deployment of the data for real-time streaming and analysis.
[0021] The present invention complements and overcome the shortcomings of the conventional inspection of buried pipelines by the ILI (In-Line Inspection) tool. The present invention enables to pre-predict yet to happen macro anomalies based on historical inspection data by using cutting edge Arti?cial Intelligence (AI) and Machine Learning (ML) techniques. The capability of prediction of yet to happen future anomalies in the pipeline is not possible with conventional inspection methods.
[0022] The present invention utilizes a noncontact magnetometric diagnosis (NCMD) technology which can predict anomalies even before any actual macro-level defect start appearing to be detected by ILI sensors or any other macro-defect inspection tools. The present invention can be utilized in the industries specially that has fields with high risk such as nuclear and oil and gas industry. By using cutting edge artificial intelligence and machine learning technologies new robotic platforms changes the scenario even in highly manual traditional oil and gas industry. In the onshore oil and gas industry robotic solutions are used both in upstream and downstream processes, such as site survey, drilling, production and transportation, mainly focused in the form of in-pipe inspection robots (IPIRs), tank inspection robots (TIRs), unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) etc.
[0023] In contrast to the convention available solutions for pipeline inspection mechanisms, the NCM AGV of the present invention for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground is capable of automatically identifying the pipes that are buried underground without manual intervention, navigate the vehicle over the pipe along with tracking of the same and is able ot precisely identify the defects present at any part /location precisely of the fault/damage/possible damage.
[0024] In contrast to the convention available solutions for pipeline inspection mechanisms, the Autonomous Ground Vehicle (AGV) as per the present invention utilizes Artificial Intelligence (AI) and Machine Learning (ML) for inspection and management of the buried metallic pipelines that enables predictive maintenance capability and prediction of future anomalies in the pipeline based on data collected in the past. Further, the AGV as per the present invention provides an alternative method for external non-contact inspection of pipelines as a screening tool instead of always depending on expensive, tedious and time-consuming ILI mechanisms. Furthermore, the AGV as per the present invention comprises various sensors fitted thereon to detect presence of any hazardous gas thereby enabling gas leakage detection mechanism. Also, the AGV as per the present invention comprises various sensors (including GPS) that not only detects the location of the CP layer based on data collected and share the same at remote location but also captures the exact location of the defect in the CP layer based on data collected and shares the same at remote location thereby achieve geotagging functionality. Further the the AGV as per the present invention can also detect the exact location of any tool performing the Inline Inspection and share its precise location to the remote devices. The AGV as per the present invention has a Gas-leak detection sensor is used to detect and map the gas leak density and GPS tag the gas density profile.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIGs. 1A-1B illustrates manual usage of pipe locator for inspection of CP layer anomaly.
[0026] FIGs. 2A-2B illustrates non-contact condition monitoring (NCM) autonomous ground vehicle (AGV) (102) for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, in accordance with the present invention.
[0027] FIG. 3 illustrates non-contact condition monitoring (NCM) method for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, in accordance with the present invention.
[0028] FIG. 4 illustrates detection of parameters analyzed by a neural network, in accordance with the present invention.
DETAILED DESCRIPTION
[0029] Embodiments of the present disclosure include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware or by human operators.
[0030] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0031] The term “non-contact sensor” used in the present invention indicates the sensor that uses wear-free technology, the sensor contains no friction on the moving parts, and this eliminates wear and tear and mechanical failure. TO elaborate, the sensor is using a technology that uses non-touching parts. This term non-contact or non-contacting usually refers to a position sensor that can measure movement or displacement in a rotary or linear fashion. A non-contact sensor uses technology which doesn't come into physical contact. The technology within a contacting position sensor will consist of a track and a slider. The slider will move along the track to measure position of an object. This method of measuring position often causes wear and tear within the sensor and although contacting sensors are lower cost, they can cost more long term as they require more frequent replacement. The term “non-contact sensor” used in the present invention indicates monitoring of a particular object without actually coming into physical contact.
[0032] FIGs. 2A-2B illustrates a non-contact condition monitoring (NCM) autonomous ground vehicle (AGV) (102) for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, in accordance with the present invention.
[0033] In an embodiment, the NCM AGV (102) includes a first non-contact sensor (106) configured to detect at least one metallic pipeline (104) from the one or more metallic pipelines buried underground, and detect magnetic field generated by the CP layer (104A) due to passage of current therethrough. The magnetic field indicates current leakage emitted from the CP layer (104A). The first non-contact sensor (106) is communicably coupled to one or more wheels (110) of the AGV to align said one or more wheels for navigating the AGV (102) over the at least one detected metallic pipeline (104).
[0034] The NCM AGV (102) also includes a processor (112) coupled to the first non-contact sensor (106). The processor detects one or more parameters associated with the detected magnetic field while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline to thereby inspect condition of the CP layer (104A) based on the one or more detected parameters.
[0035] In an exemplary embodiment, the current is passed thorough one or more metallic wires (C) wound around the at least one metallic pipeline (104) causing the passage of current through the CP layer (104A).
[0036] In an exemplary embodiment, the first non-contact sensor (106) is a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation.
[0037] In an exemplary embodiment, the one or more detected parameters are transmitted, by a transmitter (114) coupled to the processor (112) of the AGV, to a remote computing device (116) for analysis of the CP layer (104A) to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0038] In an exemplary embodiment, the NCM AGV (102) further includes a camera (118) for capturing real-time visual data of the at least one detected metallic pipeline while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline. The captured real-time visual data is transmitted to a remote computing device for analysis of the CP layer to forecast a performance related information in future by utilizing past pre-stored performance value of the CP layer, the captured real-time visual data, and the one or more detected parameters.
[0039] In an exemplary embodiment, the NCM AGV (102) further includes a GPS to transmit a current location of the AGV to a remote computing device, and acoustic sensor or a surface acoustic wave sensor to sense one or more signal associated with the the at least one detected metallic pipeline and transmit the sensed signal to the remote computing device. The GPS is configured to perform geotagging based on the inspected condition of the at least one detected metallic pipeline to tag at least a location of the at least one detected metallic pipeline or a location of an defect present in the CP layer.
[0040] In an exemplary embodiment, the one or more detected parameters are analyzed by at least one of an artificial intelligence (AI) technique pre-stored in the remote computing device (116), a machine learning (ML) technique pre-stored in the remote computing device, and a signal processing technique pre-stored in the remote computing device for analysis of the CP layer to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0041] In an exemplary embodiment, the one or more detected parameters are analyzed by a neural network receiving the one or more parameters as a multimodal information input to the neural network for analysis of the of the at least one detected metallic pipeline to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0042] In an exemplary embodiment, the autonomous ground vehicle (AGV) (102) is an automated guided vehicle or an automatic guided vehicle (AGV).
[0043] In an exemplary embodiment, the one or more parameters associated with the CP layer (104A) are selected from a magnetic field intensity emanating from the CP layer (104A).
[0044] In an exemplary embodiment, the condition of the CP layer (104A) is selected from a normal condition or a defect condition due to at least one of cracks present in the CP layer (A), a wall thinning of the CP layer (A), pitting of the CP layer (A), erosion of the CP layer (A), breaking of metallic wires wound around the metallic pipe (B), and stress led corrosion of the CP layer (A).
[0045] In another embodiment, the NCM AGV (102) includes a first non-contact sensor (106) configured at a front side of the AGV that detects at least one metallic pipeline (104) from the one or more metallic pipelines buried underground, and a second non-contact sensor (108) configured at a back side of the AGV for detecting magnetic field generated by the CP layer due to passage of current therethrough. The magnetic field indicates current leakage emitted from the CP layer. The first non-contact sensor and the second non-contact sensor are communicably coupled to one or more wheels (110) of the AGV to align said one or more wheels for navigating the AGV over the at least one detected metallic pipeline (so as to travel exactly and precisely over the one or more metallic pipelines buried underground on the surface of ground (500)).
[0046] The NCM AGV (102) further includes a processor (112) coupled to the second non-contact sensor, the processor detects one or more parameters associated with the detected magnetic field while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline to thereby inspect condition of the CP layer (104A) based on the one or more detected parameters.
[0047] In an exemplary embodiment, the first non-contact sensor (106) is a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation.
[0048] In an exemplary embodiment, the second non-contact sensor (108) is a magnetic sensor, an Electromagnetic (EM) sensor, a Metal Magnetic Memory (MMM) sensor, or a combination thereof.
[0049] In an exemplary embodiment, the one or more detected parameters are transmitted by a transmitter (114) coupled to the processor (112) of the AGV to a remote computing device (116) for analysis of the of the at least one detected metallic pipeline (104) to forecast a performance related information, in future by utilizing past pre-stored performance value of the at least one detected metallic pipeline (104) and the one or more detected parameters.
[0050] In an exemplary embodiment, the NCM AGV (102) further includes a camera (118) for capturing real-time visual data of the at least one detected metallic pipeline while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline, and wherein the captured real-time visual data is transmitted to a remote computing device for analysis of the of the at least one detected metallic pipeline to forecast a performance related information in future by utilizing past pre-stored performance value of the at least one detected metallic pipeline, the captured real-time visual data, and the one or more detected parameters.
[0051] In an exemplary embodiment, the one or more detected parameters are analyzed by at least one of an artificial intelligence (AI) technique pre-stored in the remote computing device (116), a machine learning (ML) technique pre-stored in the remote computing device, and a signal processing technique pre-stored in the remote computing device for analysis of the of the at least one detected metallic pipeline to forecast a performance related information, in future by utilizing past pre-stored performance value of the at least one detected metallic pipeline and the one or more detected parameters.
[0052] In an exemplary embodiment, the one or more detected parameters are analyzed by a neural network receiving the one or more parameters as a multimodal information input to the neural network for analysis of the of the at least one detected metallic pipeline to forecast a performance related information, in future by utilizing past pre-stored performance value of the at least one detected metallic pipeline and the one or more detected parameters.
[0053] In an exemplary embodiment, the autonomous ground vehicle (AGV) (102) is an automated guided vehicle or an automatic guided vehicle (AGV).
[0054] In an exemplary embodiment, the second non-contact sensor (108) is selected from laser micrometers, vision measuring machines, profile projectors and microscopes, Vision Systems, CT Scanners, Laser Scanners, Photogrammetry, Articulating Laser Scanning Arms, Structured Light Scanners, and CFS Sensors (Confocal White Light Sensor) devices, and Charge-Coupled Device sensors (CCD) CMOS sensors (Complementary Metal-Oxide Semiconductor).
[0055] In an exemplary embodiment, the one or more parameters associated with the at least one detected metallic pipeline (104) are selected from a magnetic field intensity emanating from the at least one detected metallic pipeline and a magnetic field intensity emanating from the underground environment.
[0056] In an exemplary embodiment, the condition of the at least one detected metallic pipeline (104) is selected from a normal condition or a defect condition due to at least one of cracks present in the at least one detected metallic pipeline, a wall thinning of the at least one detected metallic pipeline, pitting of the at least one detected metallic pipeline, erosion of the at least one detected metallic pipeline, and stress led corrosion of the at least one detected metallic pipeline.
[0057] In an exemplary embodiment, the AGV (102) comprises of a GPS to transmit a current location of the AGV to a remote computing device, and acoustic sensor or a surface acoustic wave sensor to sense one or more signal associated with the the at least one detected metallic pipeline and transmit the sensed signal to the remote computing device.
[0058] In an exemplary embodiment, the first non-contact sensor upon locating the exact location of the pipes underground may send a signal (information) to the processor which then processes the information to identify the current position of the wheels and then directs the electronic control unit (ECU) of the vehicle to move the one or more wheels (110) and align said one or more wheels for navigating the AGV over the at least one detected metallic pipeline.
[0059] In an exemplary embodiment, the AGV comprises a navigation sensor coupled to the first non-contact sensor and the navigation sensor is used for navigation of the vehicle.
[0060] In an exemplary embodiment, the AGV of the present invention uses a non-contact MMM sensor type 11-6W probe, which is a specialized highly sensitive six-channel scanning device with an analog-digital analyzer for non-contact inspecting the self-magnetic leakage field (SMLF) of oil and gas pipelines buried at the depth as well as the CP layer (104A). This probe can be used with a measuring wheel or independently. The wheel is installed with an encoder on it so that it can transfer the wheel's displacement into electrical impulse signal. When working with the measuring wheel (wheel mode), the probe records the data at the same time as the encoder transmits an impulse signal to it. The wheel not only works as an odometer but also a generator. Also, the probes can work in time mode with the analyzer from where the probes get impulse signal at a specified time interval. Besides, the probe contains two transducers — upper transducer and lower transducer. The lower transducer is on the downside to inspect the SMLF of the pipeline while the upper one is to inspect the environmental magnetic field for reference.
[0061] FIG. 2 illustrates a non-contact condition monitoring (NCM) method for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, in accordance with the present invention.
[0062] In an embodiment, the method is performed by an autonomous ground vehicle (AGV) as disclosed in FIGs. 2A or 2B.
[0063] At step 202, at least one metallic pipeline (104) from the one or more metallic pipelines buried underground is detected. The first non-contact sensor (106) is a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation.
[0064] In an exemplary embodiment, Pipe locator, uses radar signal or electromagnetic wave to detect buried objects in the subsurface. The pipe locator used in this work has two components: transmitter and receiver. By connecting the transmitter to the Cathodic Protection (CP) pole or uncovered portion of the pipeline, an Alternating Current (AC) with optional frequency would be injected into the pipeline and generates an electromagnetic signal along the pipeline. This electromagnetic signal radiates from the pipe and is known as the signal that received by the receiver. Based on the received signal strength, the receiver sends a digitalized output to the onboard computer, which indicates:
[0065] Angle: Angle between the pipe locator and the pipeline direction. The negative sign indicates the clockwise direction and positive sign indicates the anti-clockwise deviation
[0066] Position: Indicates the position of pipe locator with respect to the pipeline. -1 indicates the pipe locator is on the right side, +1 indicates the pipe locator is on the left side and 0 indicates the pipe locator is directly above the pipeline.
[0067] Relative distance: This number represents the relative distance between the pipe locator and the pipeline. The relationship between relative distance and actual lateral distance.
[0068] Depth: Indicates the distance between the pipe locator and the pipeline structure. When the transmitter is connected properly, this value indicates the actual distance or else it gives some random numbers.
[0069] In the direct connection mode, the transmitter connects to the pipe using cable and inputs an AC with an optional frequency. The receiver, setting the same frequency, will receive the electromagnetic signal from the pipe with information shows pipe located on the left or right, pipe's depth and pipe's direction. The pipe locator can send out a digitized message per second indicating the pipeline location preferably via Bluetooth.
[0070] Since the pipe locator cannot explicitly express the true lateral distance of the pipe locator to the pipe and the relationship between relative-distance and lateral distance is still unknown to us, a trajectory controller is designed to steer the AGV following a trajectory to approach the pipeline and then to track the pipeline based on the relative-distance value. In the trajectory design, it has three level controls. The first level is when the AGV is far the relative-distance is large, and then AGV will follow a 60-degree line to approach the pipe. The second level is when the AGV 1s close enough or the relative-distance is small enough, the AGV would follow a 15-degree line to approach the pipe, which could avoid the AGV running to the pipe too fast in a short distance and oscillating around the pipe. After that, the third level control 1s to keep the AGV running on the top of the pipe. This controller aims at making the AGV can quickly and smoothly track the pipe and at the same time can avoid the AGV purely doing rotating that would damage the cover soil of the pipe and affects the MMM sensor working.
[0071] The working principle and other details about pipe locator are explained in “V. SUDEVAN, A. SHUKLA, and H. KARKI et al Magnetic Memory Method, Prague, CzechRepublic, June 2019.” & X. Zhang, A. Shukla, A. A. Ali, and H. Karki, "A Smart Robotic System for Non-ContactCondition Monitoring and Fault Detection in Buried Pipelines," in Abu Dhabi InternationalPetroleum Exhibition & Conference, 2018: Society of Petroleum Engineers. Pipe locator is attached on the front side of AGV and the digitalized output of Pipe locator such as side indicator, angle, relative distance and depth of Pipe locator with respect to the buried pipeline is then fed to the onboard computer of the platform.
[0072] At step 204, magnetic field generated by the CP layer due to passage of current therethrough is detected by the first non-contact sensor. The magnetic field indicates current leakage emitted from the CP layer. The first non-contact sensor is communicably coupled to one or more wheels (110) of the AGV to align said one or more wheels for navigating the AGV over the at least one detected metallic pipeline. The current is passed thorough one or more metallic wires wound around the at least one metallic pipeline (104) causing the passage of current through the CP layer
[0073] The present invention uses a non-contact MMM sensor type 11-6W probe, which is a specialized highly sensitive six-channel scanning device with an analog-digital analyzer. The scanning device is manufactured as a telescopic bar with a changeable length. It is designed for non-contact inspecting the self-magnetic leakage field (SMLF) of oil and gas pipelines buried at the depth up to 3m. This probe can be used with a measuring wheel or independently. The wheel is installed with an encoder on it so that it can transfer the wheel's displacement into electrical impulse signal. When working with the measuring wheel (wheel mode), the probe records the data at the same time as the encoder transmits an impulse signal to it. The wheel not only works as an odometer but also a generator. Also, the probes can work in time mode with the analyzer from where the probes get impulse signal at a specified time interval. Besides, the probe contains two transducers — upper transducer and lower transducer. The lower transducer is on the downside to inspect the SMLF of the pipeline while the upper one is to inspect the environmental magnetic field for reference.
[0074] At step 206, a processor of the AGV detects one or more parameters associated with the detected magnetic field while the AGV is aligned and navigated over the at least one detected metallic pipeline to thereby inspect condition of the CP layer based on the one or more detected parameters.
[0075] At step 208, a transmitter coupled to the processor of the AGV transmits one or more detected parameters to a remote computing device for analysis of the CP layer to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0076] FIG. 4 illustrates detection of parameters analyzed by a neural network, in accordance with the present invention.
[0077] As shown in FIG. 4, the one or more detected parameters detected by the first non-contact sensor and shared with processor are analyzed by at least one of an artificial intelligence (AI) technique pre-stored in the remote computing device (116), a machine learning (ML) technique pre-stored in the remote computing device, and a signal processing technique pre-stored in the remote computing device for analysis of the CP layer to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0078] In an exemplary embodiment, the one or more detected parameters detected by the first non-contact sensor and shared with processor are analyzed by a neural network receiving the one or more parameters as a multimodal information input to the neural network for analysis of the of the at least one detected metallic pipeline to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
[0079] The present invention complements and overcome the shortcomings of the conventional inspection of buried pipelines by the ILI (In-Line Inspection) tool. The present invention enables to pre-predict yet to happen macro anomalies based on historical inspection data by using cutting edge Arti?cial Intelligence (AI) and Machine Learning (ML) techniques. The capability of prediction of yet to happen future anomalies in the pipeline is not possible with conventional inspection methods.
[0080] As shown in FIG. 4, the results may be displayed on the displayed of an electronic device in a graphical form or in any user understandable format. In case if the results are displayed in the graphical form, the variation in the spikes of the waveform indicates the CP layer defects as clearly shown in the FIG. 4.
[0081] While some embodiments of the present disclosure have been illustrated and described, those are completely exemplary in nature. The disclosure is not limited to the embodiments as elaborated herein only and it would be apparent to those skilled in the art that numerous modifications besides those already described are possible without departing from the inventive concepts herein. All such modifications, changes, variations, substitutions, and equivalents are completely within the scope of the present disclosure. The inventive subject matter, therefore, is not to be restricted except in the protection scope of the appended claims.

WE CLAIM:

1. A non-contact condition monitoring (NCM) autonomous ground vehicle (AGV) (102) for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, the NCM AGV (102) comprising:
a first non-contact sensor (106) configured to detect at least one metallic pipeline (104) from the one or more metallic pipelines buried underground, and detect magnetic field generated by the CP layer (104A) due to passage of current therethrough,
wherein the magnetic field indicates current leakage emitted from the CP layer (104A); and
wherein the first non-contact sensor (106) is communicably coupled to one or more wheels (110) of the AGV to align said one or more wheels for navigating the AGV (102) over the at least one detected metallic pipeline (104); and
a processor (112) coupled to the first non-contact sensor (106), the processor detects one or more parameters associated with the detected magnetic field while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline to thereby inspect condition of the CP layer (104A) based on the one or more detected parameters.
2. The NCM AGV as claimed in claim 1, wherein the current is passed thorough one or more metallic wires (C) wound around the at least one metallic pipeline (104) causing the passage of current through the CP layer (104A).
3. The NCM AGV as claimed in claim 1, wherein the first non-contact sensor (106) is a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation.
4. The NCM AGV as claimed in claim 1, wherein the one or more detected parameters are transmitted, by a transmitter (114) coupled to the processor (112) of the AGV, to a remote computing device (116) for analysis of the CP layer (104A) to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
5. The NCM AGV as claimed in claim 1, wherein the NCM AGV (102) further includes:
a camera (118) for capturing real-time visual data of the at least one detected metallic pipeline while the AGV (102) is aligned and navigated over the at least one detected metallic pipeline, and wherein the captured real-time visual data is transmitted to a remote computing device for analysis of the CP layer to forecast a performance related information in future by utilizing past pre-stored performance value of the CP layer, the captured real-time visual data, and the one or more detected parameters; and
a GPS to transmit a current location of the AGV to a remote computing device, and acoustic sensor or a surface acoustic wave sensor to sense one or more signal associated with the the at least one detected metallic pipeline and transmit the sensed signal to the remote computing device, wherein GPS is configured to perform geotagging based on the inspected condition of the at least one detected metallic pipeline to tag at least a location of the at least one detected metallic pipeline or a location of an defect present in the CP layer.
6. The NCM AGV as claimed in claim 1, wherein the one or more detected parameters are analyzed:
by at least one of an artificial intelligence (AI) technique pre-stored in the remote computing device (116), a machine learning (ML) technique pre-stored in the remote computing device, and a signal processing technique pre-stored in the remote computing device for analysis of the CP layer to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters; or
by a neural network receiving the one or more parameters as a multimodal information input to the neural network for analysis of the of the at least one detected metallic pipeline to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.
7. The NCM AGV as claimed in claim 1, wherein the autonomous ground vehicle (AGV) (102) is an automated guided vehicle or an automatic guided vehicle (AGV).
8. The NCM AGV as claimed in claim 1, wherein the one or more parameters associated with the CP layer (104A) are selected from a magnetic field intensity emanating from the CP layer (104A).
9. The NCM AGV as claimed in claim 1, wherein the condition of the CP layer (104A) is selected from a normal condition or a defect condition due to at least one of cracks present in the CP layer (A), a wall thinning of the CP layer (A), pitting of the CP layer (A), erosion of the CP layer (A), breaking of metallic wires wound around the metallic pipe (B), and stress led corrosion of the CP layer (A).
10. A non-contact condition monitoring (NCM) method for inspecting condition of a conductive protective (CP) layer (104A) associated with one or more metallic pipelines buried underground, the method comprising:
detecting (202), by a first non-contact sensor of an autonomous ground vehicle (AGV), at least one metallic pipeline (104) from the one or more metallic pipelines buried underground, wherein the first non-contact sensor (106) is a general-purpose pipe locator or an underground pipe detector manufactured by VIVAX-METROTECH Corporation;
detecting (204), by the first non-contact sensor, magnetic field generated by the CP layer due to passage of current therethrough,
wherein the magnetic field indicates current leakage emitted from the CP layer,
wherein the first non-contact sensor is communicably coupled to one or more wheels (110) of the AGV to align said one or more wheels for navigating the AGV over the at least one detected metallic pipeline, and
wherein the current is passed thorough one or more metallic wires wound around the at least one metallic pipeline (104) causing the passage of current through the CP layer;
detecting (206), by a processor (112) of the AGV, one or more parameters associated with the detected magnetic field while the AGV is aligned and navigated over the at least one detected metallic pipeline to thereby inspect condition of the CP layer based on the one or more detected parameters; and
transmitting (208), by a transmitter coupled to the processor of the AGV, the one or more detected parameters to a remote computing device for analysis of the CP layer to forecast a performance related information, in future by utilizing past pre-stored performance value of the CP layer and the one or more detected parameters.