30 results about "Corrosion under insulation" patented technology

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.

Methods and systems for using survival modeling methods with pipeline inspection data to determine causal factors for corrosion under insulation comprise determining a first corrosion condition of a pipeline joint at a first time; determining a second corrosion condition of the pipeline joint at a second, subsequent time; determining joint attributes, pipeline attributes, and location attributes associated with the pipeline joint; and repeating the process for a plurality of pipeline joints in one or more pipelines. This information is fed into a multiple regression and survival analysis process that determines regression coefficients reflecting the estimated degrees to which various factors contribute to corrosion under insulation. The survival analysis also determines one or more survival models capable of predicting when a given pipeline joint is likely to transition from a first corrosion state to a different second corrosion state, given values for its various attributes.

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 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.

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.

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.

A permanently installed monitoring system for corrosion under insulation (PIMSCUI) and a method for performing the monitoring of said system are provided. The objective of the invention by a fiber 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 fiber. The acoustic emitters send a pulsed acoustic signal towards the pipeline which is received by the optical fiber, 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 fiber.

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 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.