A fastener health monitoring system includes a structure including a first component and a second component, one or more fasteners securing the first component to the second component, and an optical fiber arranged adjacent to the one or more fasteners. The fiber is configured to detect strain on the structure from the one or more fasteners. Strain patterns are derivable from sensed data from the optical fiber, and any deviation in strain value greater than a threshold value at a particular fiber position along the optical fiber is indicative of a missing, damaged, or loosened fastener amongst the one or more of fasteners corresponding to that particular fiber position.

A method for assessing fatigue damage and a fatigue life based on a crystal plastic welding process model. According to the new method, consideration is given to the effects of the crystal slip system and the polycrystal plastic strain on the welding process performance of the material. A welding process damage and fatigue life assessment model is established on the mesoscopic scale. The effect of microscopic characterizations of materials on the welding process performance, as well as on the fatigue damage and life of welded joints, can be studied from the mesoscopic point of view. The relationship between the welding process and the evolution of the material performance can be determined by the macro-mesoscopic coupling calculation model to further determine the effect and degree of welding processes on the fatigue damage and life of materials.

A pipeline pressure test that accounts for measurement uncertainties includes a method for performing a pressure test of a pipe section of a pipeline including receiving a desired pressure to be applied to the pipe section and a duration of time the desired pressure is to be applied to the pipe section, receiving a pressure measurement of a fluid, a temperature measurement of the fluid, a volume measurement of the fluid, and a pipe section strain measurement, determining a change in fluid pressure and a volume change, determining a pressure change uncertainty and a volume change uncertainty, checking that the pressure change uncertainty is within a pressure uncertainty threshold and that the volume change uncertainty is within a volume uncertainty threshold, determining whether the desired pressure has been applied to the inner surface of the pipe section for the duration of time; and outputting a result of the testing.

Certain embodiments may generally relate to structural damage detection. An embodiment may be directed to method for identifying a presence and a location of structural damage. Such method may include training a convolutional neural network (CNN) for a joint of a structure, sending instructions to a modal shaker to induce an input to the structure, receiving, as a result of the induced input, a raw acceleration signal at the joint, computing, based on the trained CNN and the raw acceleration signal, an index value of the joint, and identifying, according to the index value, a presence of a location of structural damage of the structure. In a further embodiment, the index value represents a likelihood of damage at the joint.

Embodiments described herein utilize Non-Destructive Inspection (NDI) scan data obtained during a process performed on a surface of a structure to update a location of an NDI scanner on the surface. A subsurface feature within the structure is detected based on the NDI scan data, which are correlated with pre-defined position data for the subsurface feature. A measured location of the NDI scanner on the surface is corrected based on the pre-defined position data for the subsurface feature.

Methods, systems, and apparatuses are provided for detecting and determining conditions of and conditions within a fluid conduit.

Robotic devices that can be utilized on pipes of any material and of a variety of pipe diameters are provided. The robotic device utilizes a ducted fan to create the normal forces needed to adhere to any part of a pipe. The chassis of the device can be segmented to allow for application on various diameter pipes.

Methods for performing maintenance operations using unmanned aerial vehicles (UAVs). The methods are enabled by equipping a UAV with a maintenance tool capable of performing a desired maintenance operation (e.g., nondestructive inspection) on a limited-access surface of a large structure or object (e.g., a wind turbine blade). The UAV uses re-orientation of lifting means (e.g., vertical rotors) to move the maintenance tool continuously or intermittently across the surface of the structure while maintaining contact with the surface of the structure undergoing maintenance.

A damage detection system includes one or more emitters, one or more receivers, and a controller. Emitters are arranged to transmit continuous beam or intermittent light pulses toward rotor blades during operation of a turbomachine. Light returns collected at the receivers define a light return amplitude profile. The controller analyzes the light return amplitude profile to identify light amplitude changes indicative of one or more damaged blades. When the light return profile satisfies one or more damage criteria, the controller outputs an indication of blade damage to another turbomachine controller, system, or display.

An apparatus and method to be used for the automated selection, inspection, analysis, and disposition of tools that are installed on milling drums used by an asphalt milling machine to remove a layer of asphalt from a road surface that is being resurfaced is disclosed. Asphalt milling machines are also known as cold planer, pavement planer, pavement recycler, or roto-mill machines. The disclosed apparatus and method comprises a robot, one or more 3-dimensional scanners, one or more digital 2-dimensional cameras, and a controller. A work surface is provided for where the robot may select a tool to be inspected, place the tool on a rotating pedestal, inspect the tool while rotating the tool to capture all aspects of the tool, perform an analysis on the images captured, and depending on the analysis, classify the tool as reusable, degraded, or scrap.