35 results about "Corrosion under insulation" patented technology

A method for detecting a latent environmental effect (such as a corrosive environment under insulation) or a latent structural change (such as a crack in a concealed structural member) at a known remote concealed location. The method of the instant invention includes three steps. The first step is to use a harmonic electromagnetic transponder at the known remote concealed location of the latent environmental effect or the latent structural change, the harmonic electromagnetic transponder having a reactive portion which reacts to the latent environmental effect or latent structural change to modify the harmonic emission of the transponder. The second step is to remotely interrogate the transponder by directing electromagnetic radiation at the transponder. The third step is to use the harmonic emission of the transponder to remotely determine the latent environmental effect or the latent structural change.

The present invention relates generally to thermal imaging systems and methods and uses therefor, and in a particular though non-limiting embodiment, to a method of detecting corrosion under insulation, as well as corrosion-related material data associated therewith. The system utilizes advanced infrared imaging video cameras to detect characteristic signatures of wet thermal traits on process equipment. Various embodiments of the invention integrate equipment, automation, and algorithms to form a method for identifying wet thermal insulation by scanning multiple locations along insulated piping, tanks, or other manufacturing equipment. Such scans can occur individually, sequentially, or simultaneously, with the results then being stored and comparatively analyzed. Further embodiments comprise tracking the approximate time of exposure to moisture, prediction of the corrosion rates for underlying insulated metal substrates, and measuring wall thicknesses along either predetermined random portions of the metal substrate.

A method of sensing corrosion of a pipe covered by a layer of insulation comprises positioning a CUI sensor radially adjacent an outer surface of the pipe. The CUI sensor comprises a non-conductive base having a first end and a second end opposite the first end. In addition, the CUI sensor comprises a first test circuit mounted to the base. The first test circuit includes a first conductor, a second conductor, and a first testing element extending between the first conductor and the second conductor. Further, the method comprises exposing the first testing element to the same environment as the outer surface of the pipe. Still further, the method comprises determining whether the first testing element has corroded through. Moreover, the method comprises assessing whether corrosion of the pipe has occurred based on the determination of whether the first testing element has corroded through.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

Systems and methods are disclosed for detecting the presence of water on or in pipelines, tanks, equipment and other structures, including insulated structures which may be subject to corrosion under insulation, or CUI. Two dissimilar, spaced-apart metals are coupled at least indirectly to a structure to be monitored, and apparatus for detecting a potential difference between the two dissimilar metals, thereby indicating that water is present as an electrolyte. In CUI applications, at least one of the dissimilar metals is attached to, or embedded within, a water-absorbing insulator or other material coupled to or surrounding the structure. The water-absorbing material may be provided in the form of a tape attached to the surface of a metal component fanning the structure. In some embodiments, the structure to be monitored may itself incorporate a ferrous metal component which is used as one of the dissimilar metals.

Methods and systems for inspecting insulated equipment for corrosion under insulation are provided. The system includes an autonomous unmanned vehicle having aerial and ground locomotive capabilities. The vehicle includes an infrared detector and a pulsed eddy current sensor. In the method, infrared waves emitted from the equipment are detected along the equipment with the infrared detector. Using the infrared detector, at least one image of an inner surface of the equipment is developed based on the detected infrared waves. At least one area that is susceptible to corrosion is determined based on the at least one image. The susceptible area is inspected with the pulsed eddy current sensor, which induces an eddy current in the inner wall of the equipment. Based on a rate of the decay in strength of the eddy current, it is determined whether corrosion exists at the susceptible area using a processor configured by code.

The present invention relates generally to thermal imaging systems and methods and uses therefor, and in a particular though non-limiting embodiment, to a method of detecting corrosion under insulation, as well as corrosion-related material data associated therewith. The system utilizes advanced infrared imaging video cameras to detect characteristic signatures of wet thermal traits on process equipment. Various embodiments of the invention integrate equipment, automation, and algorithms to form a method for identifying wet thermal insulation by scanning multiple locations along insulated piping, tanks, or other manufacturing equipment. Such scans can occur individually, sequentially, or simultaneously, with the results then being stored and comparatively analyzed. Further embodiments comprise tracking the approximate time of exposure to moisture, prediction of the corrosion rates for underlying insulated metal substrates, and measuring wall thicknesses along either predetermined random portions of the metal substrate.

A method of sensing corrosion of a pipe covered by a layer of insulation comprises positioning a CUI sensor radially adjacent an outer surface of the pipe. The CUI sensor comprises a non-conductive base having a first end and a second end opposite the first end. In addition, the CUI sensor comprises a first test circuit mounted to the base. The first test circuit includes a first conductor, a second conductor, and a first testing element extending between the first conductor and the second conductor. Further, the method comprises exposing the first testing element to the same environment as the outer surface of the pipe. Still further, the method comprises determining whether the first testing element has corroded through. Moreover, the method comprises assessing whether corrosion of the pipe has occurred based on the determination of whether the first testing element has corroded through.

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

The invention discloses a phenolic aldehyde epoxy coating with CUI (corrosion under insulation) resistance. The phenolic aldehyde epoxy coating comprises a component A and a component B, wherein the component A comprises raw materials in percentage by weight as follows: 20%-30% of phenolic aldehyde epoxy resin, 16%-19% of a solvent, 0.5%-0.6% of a thixotropic agent, 0.4%-0.6% of a defoaming agent, 15%-22% of flaky iron oxide red, 5%-8% of hollow glass microspheres, 27%-34% of barite and 0.5%-1% of a silane coupling agent; the component B is 100% modified polyamide resin; before coating, the component A and the component B are mixed in a weight ratio of A to B being (88-92): (8-12) for use. According to the phenolic aldehyde epoxy coating with CUI resistance, a high-temperature-resistant film can be combined with flaky iron oxide red and the hollow glass microspheres, and an acquired coating layer has good high-temperature resistance, high-temperature cracking resistance and good long-term corrosion resistance, and can effectively resist metal surface corrosion caused by rainwater, mist and electrolyte ion infiltration under insulation; the phenolic aldehyde epoxy coating is easy to prepare and simple, facilitates construction and can be widely applied to the fields of petrochemical industry and thermal pipeline application.

A permanently installed monitoring system for corrosion under insulation (PIMSCUI) and a method for performing the monitoring of said system are provided. The present invention attains the objective of the invention by a fibre optic cable permanently mounted between walls of a pipeline and pipeline insulation surrounding the pipeline and placement of acoustic emitters along the length of the pipeline in mechanical contact with the optical fibre. The acoustic emitters send a pulsed acoustic signal towards the pipeline which is received by the optical fibre, the acoustic signal subsequently travels through the pipeline wall, reflecting from the inner diameter of the pipeline before the reflected pulse is received at the optical fibre.

Methods and systems for inspecting insulated equipment for corrosion under insulation are provided. The system includes an autonomous unmanned vehicle having aerial and ground locomotive capabilities. The vehicle includes an infrared detector and a pulsed eddy current sensor. In the method, infrared waves emitted from the equipment are detected along the equipment with the infrared detector. Using the infrared detector, at least one image of an inner surface of the equipment is developed based on the detected infrared waves. At least one area that is susceptible to corrosion is determined based on the at least one image. The susceptible area is inspected with the pulsed eddy current sensor, which induces an eddy current in the inner wall of the equipment. Based on a rate of the decay in strength of the eddy current, it is determined whether corrosion exists at the susceptible area using a processor configured by code.

The invention provides a test device and a test method for CUI (corrosion under insulation). The test device for CUI comprises a tank body, wherein sealed chambers are formed in the tank body and comprise an immersion chamber capable of being filled with a leaching agent, a first filling chamber capable of being filled with a water immersion insulating material, a second filling chamber capable ofbeing filled with a dry insulating material as well as a first mounting chamber; the immersion chamber is communicated with the first filling chamber; the first filling chamber is formed between thesecond filling chamber and the immersion chamber to isolate the second filling chamber from the immersion chamber; the second filling chamber is formed between the first filling chamber and the firstmounting chamber to isolate the first filling chamber from the first mounting chamber; a working electrode is mounted in the first mounting chamber. An accurate basis can be provided by the test device for occurrence of CUI.

A method for detecting a latent environmental effect (such as a corrosive environment under insulation) or a latent structural change (such as a crack in a concealed structural member) at a known remote concealed location. The method of the instant invention includes three steps. The first step is to use a harmonic electromagnetic transponder at the known remote concealed location of the latent environmental effect or the latent structural change, the harmonic electromagnetic transponder having a reactive portion which reacts to the latent environmental effect or latent structural change to modify the harmonic emission of the transponder. The second step is to remotely interrogate the transponder by directing electromagnetic radiation at the transponder. The third step is to use the harmonic emission of the transponder to remotely determine the latent environmental effect or the latent structural change.

The present invention realizes an inspection method for inspecting corrosion under insulation. This inspection method according to the present invention makes it possible to inspect corrosion easily and economically in piping furnished with heat insulators. The inspection method is an inspection method for inspecting corrosion under insulation, in piping to which an heat insulator is provided, and includes providing a fiber optical Doppler sensor to the piping; and inspecting the corrosion in the piping by using the fiber optical Doppler sensor.

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

A method for identifying corrosion under insulation (CUI) in a structure comprises receiving thermographs from the structure using an infrared camera, applying filters to the thermograph using a first machine learning system, initially determining a CUI classification based on output from the filters, and validating the initial CUI classification by an inspection of the structure. The first machine learning system is trained using results of the validation. Outputs of the first machine learning system and additional structural and environmental data are fed into a second machine learning system that incorporates information from earlier states into current states. The second machine learning system is trained to identify CUI according to changes in the outputs of the first machine learning system and the additional data over time until a second threshold for CUI classification accuracy is reached. CUI is thereafter identified using the first and second machine learning systems in coordination.

A method for inspecting corrosion under insulation simply and precisely at a low cost is provided. The method for inspecting CUI is characterized by obtaining signals from a fiber optical Doppler sensor attached to equipment and counting a waveform for a predetermined time before and after a point where an amplitude is over a threshold value as one acoustic emission (AE) signal, recording the AE signals and the maximum amplitudes thereof, subjecting the AE signals to a filtering process to remove noise signals, determining a frequency distribution of the AE signal hits relative to various maximum amplitude values, determining a regression line of the number of the AE hits relative to the maximum amplitudes from a scattering diagram which is obtained by double logarithmic expression of the frequency distribution, and judging existence or nonexistence of the corrosion based on a gradient of the regression line.

Methods and systems for inspecting insulated equipment for corrosion under insulation are provided. The system includes an autonomous unmanned vehicle having aerial and ground locomotive capabilities. The vehicle includes an infrared detector and a pulsed eddy current sensor. In the method, infrared waves emitted from the equipment are detected along the equipment with the infrared detector. Using the infrared detector, at least one image of an inner surface of the equipment is developed based on the detected infrared waves. At least one area that is susceptible to corrosion is determined based on the at least one image. The susceptible area is inspected with the pulsed eddy current sensor, which induces an eddy current in the inner wall of the equipment. Based on a rate of the decay in strength of the eddy current, it is determined whether corrosion exists at the susceptible area using a processor configured by code.

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

A method for inspecting corrosion under insulation simply and precisely at a low cost is provided. The method for inspecting CUI is characterized by obtaining signals from a fiber optical Doppler sensor attached to equipment and counting a waveform for a predetermined time before and after a point where an amplitude is over a threshold value as one acoustic emission (AE) signal, recording the AE signals and the maximum amplitudes thereof, subjecting the AE signals to a filtering process to remove noise signals, determining a frequency distribution of the AE signal hits relative to various maximum amplitude values, determining a regression line of the number of the AE hits relative to the maximum amplitudes from a scattering diagram which is obtained by double logarithmic expression of the frequency distribution, and judging existence or nonexistence of the corrosion based on a gradient of the regression line.

The present disclosure is directed to methods and systems for inspecting insulated equipment for any corrosion under insulation (CUI). The system includes a device comprising transmitter and receiverhorn antennas, a vector network analyzer operatively connected to the antennas, and an infrared detector. In the method, a location for inspection of the equipment is identified. A metal jacket surrounding the location is removed without stripping the insulation. Microwaves are transmitted by the transmitted horn antenna at the location, and provide heating at the location. The microwaves are received by the receiver horn antenna after reflection off the equipment. The vector network analyzer analyzes the microwaves. The infrared detector detects infrared waves emitted from the location and develops an infrared image of the inner surface of the equipment. Based on the analysis of the microwaves and the developed image, a programmed processor determines whether CUI exists at the location.

A system for predicting corrosion under insulation for a network of insulated pipes, vessels, and tanks includes a corrosion modeling engine operable to receive sensor data and generate predicted rates of corrosion. The corrosion modeling engine includes a corrosion rate calculation module operable to compare an operating temperature of an insulated component to a dew point temperature of a surrounding environment to generate a predicted rate of corrosion, and a remaining life prediction module operable to generate a predicted remaining lifetime of the insulated component using the predicted rates of corrosion and on-stream inspection data. The system further includes an alarm communicatively coupled to the corrosion modeling engine and operable to alert an operator that the predicted rate of corrosion, the predicted remaining lifetime, or a combination thereof is within a pre-determined threshold.

The invention relates to a process for protecting a metal substrate from corrosion under insulation comprising the steps of - applying a layer of a coating composition to a metal substrate, - curing of the coating composition, and - application of a layer of thermal insulation wherein the coating composition comprises - metal particles with an average diameter in the range of 20-120 microns, in an amount of 50-95 wt.%, calculated on the total weight of the coating composition, - curable resin, in an amount of 5-50 wt.%, calculated on the total weight of the coating composition, wherein the curable resin is selected from the group of epoxy resins, acrylic resins, and polyester resins, - catalyst suitable for accelerating curing of the curable resin, - and the coating layer after curing has a total layer thickness of 50-2000 microns. The invention also relates to a metal substrate provided with thermal insulation wherein a specific coating layer is provided between the metal substrate and the thermal insulation.

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

The invention discloses a phenolic aldehyde epoxy coating with CUI (corrosion under insulation) resistance. The phenolic aldehyde epoxy coating comprises a component A and a component B, wherein the component A comprises raw materials in percentage by weight as follows: 20%-30% of phenolic aldehyde epoxy resin, 16%-19% of a solvent, 0.5%-0.6% of a thixotropic agent, 0.4%-0.6% of a defoaming agent, 15%-22% of flaky iron oxide red, 5%-8% of hollow glass microspheres, 27%-34% of barite and 0.5%-1% of a silane coupling agent; the component B is 100% modified polyamide resin; before coating, the component A and the component B are mixed in a weight ratio of A to B being (88-92): (8-12) for use. According to the phenolic aldehyde epoxy coating with CUI resistance, a high-temperature-resistant film can be combined with flaky iron oxide red and the hollow glass microspheres, and an acquired coating layer has good high-temperature resistance, high-temperature cracking resistance and good long-term corrosion resistance, and can effectively resist metal surface corrosion caused by rainwater, mist and electrolyte ion infiltration under insulation; the phenolic aldehyde epoxy coating is easy to prepare and simple, facilitates construction and can be widely applied to the fields of petrochemical industry and thermal pipeline application.

A method of determining corrosion rate in a pipe surrounded using contactless thermography includes heating or cooling the structure and capturing a first, second and third thermal images of the structure at a respective initial time, after installation and when the pipe is entirely corroded. Corresponding heat flux values are derived from each thermal image. A ratio of the metal loss is determined as a ratio of the difference between the first heat flux and the second heat flux to a difference between the first heat flux and the third heat flux. A machine learning algorithm is trained to determine a level of corrosion in a metallic pipe structure, and a second machine learning algorithm is trained to a determine a level of deposits in a pipe structure. A corrected metal loss is determined by applying the algorithm output to the ratio of the metal loss.