A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

A pipeline reconditioning system and process is provided for reconditioning corroded or damaged pipelines. The reconditioning system includes a mobile topside unit storing and providing reservoirs and supplies of substances used in the pipeline reconditioning and the equipment for the reconditioning system, a management and towing units that are securely anchored in the pipeline at remote access points, and a towed delivery system array that includes a deployment sled that processes the substances and dispenses the reconditioning material, such as a composite epoxy resin with chopped glass or basalt filament fibers, between access points to the pipeline interior.

In various embodiments, the present disclosure relates to robot systems configured to operate on a cell tower to inspect, install, reconfigure, and repair cellular equipment. The present disclosure provides a robot for performing tasks on cell towers. The robot includes a body portion configured to hold various electronic components of the robot including monitoring equipment disposed thereon, one or more arms extending from the body portion adapted to manipulate components of a cell tower and to facilitate movement of the robot on the cell tower, a tethering system adapted to prevent the robot from falling off of the cell tower, and wireless interfaces adapted to allow wireless control of the robot. The robot is configured to be controlled by one of a user in a remote location, a user at the cell tower site, and autonomously via direct programing.

There are provided a defect inspection apparatus, method, and program for, in a case of using an image of an inspection-target industrial product (test object) to conduct an inspection to check whether defects are present, allowing an image interpreter to precisely and efficiently detect defects. A possible-defect image D3 indicating a crack-like defect and a simulation result image P3 indicating the predicted growth of the crack-like defect are displayed. Sliders L1 and L2 and checkboxes CB1 are used to enable selection of possible defects and simulation results to be displayed. An image interpreter can use the checkboxes CB1 to select a type of possible defect to be displayed, and can use the sliders L1 and L2 to select possible defects to be displayed on the basis of the wall thickness of portions in which possible defects are detected and the size of possible defects.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation.

In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

Processes and systems for preparing sections of pipeline pipe for machine processing to form pin and box ends for assembly into pipelines using mechanical press-fit pipe joints. The prepared pipe sections are prepared to be fully measured and documented during both manufacturing and assembly. Installed pipelines include pipeline data monitoring systems coupled to a pipeline data management center for receiving, archiving, and analysis of the data records to aid in administration, operation and management, and troubleshooting of pipelines.

Processes and systems for preparing pipe for machine processing to form pin and box ends for assembly into pipelines using mechanical press-fit pipe joints that are fully measured and documented during both manufacturing and assembly. Installed pipelines include pipeline data monitoring systems coupled to a pipeline data management center for receiving, archiving, and analysis of the data records to aid in administration, operation and management, and troubleshooting of pipelines.

A process for assembling a pipeline from pipe sections connected together using mechanical press-fit joints using mechanical press-fit pipe joints that are fully measured and documented during both manufacturing and assembly. Installed pipelines include pipeline data monitoring systems coupled to a pipeline data management center for receiving, archiving, and analysis of the data records to aid in administration operation and management, and troubleshooting of pipelines.

Processes and systems for preparing pipe for machine processing to form pin and box ends for assembly into pipelines or to assemble pipe sections in pipelines using mechanical press-fit pipe joints in which hydraulic pressure and linear displacement parameters, respectively P and L, are fully measured and documented during both manufacturing and assembly. Installed pipelines include pipeline data monitoring systems coupled to a pipeline data management center for receiving, archiving, and analysis of the data records to aid in administration, operation and management, and troubleshooting of pipelines.

Processes and systems for preparing pipe for machine processing to form pin and box ends for assembly into pipelines using mechanical press-fit pipe joints that are fully measured and documented during both manufacturing and assembly. Installed pipelines include pipeline data monitoring systems coupled to a pipeline data management center for receiving, archiving, and analysis of the data records to aid in administration, operation and management, and troubleshooting of pipelines.