Abstract: A system for determining corrosion under insulation of an industrial asset is provided. The system includes an infrared camera configured to acquire one or more time-series infrared images of an industrial asset. The system further includes a computing device configured to receive data characterizing the one or more time-series infrared images, and to identify an area of interest of the industrial asset within the one or more time-series infrared images. The computing device further configured to identify, by a machine learning algorithm, a plurality of defects within the area of interest based on pixel-wise assignment of at least one defect category selected from a plurality of defect categories associated with corrosion under insulation of the industrial asset, and to provide the plurality of defects within the area of interest of the industrial asset. Related methods, apparatuses, and computer-readable mediums are also provided.

Abstract: A method of analyzing an asset is provided. The method can include receiving first data characterizing an industrial asset. The first data can be acquired via a sensor. The method can also include determining a condition of the industrial asset based on a 3D digital twin of the industrial asset. The 3D digital twin can be generated using second data acquired prior to the first data. The digital twin can include at least one 3D digital instance of at least one component of the industrial asset and a list of components of the industrial asset described according to a pre-defined taxonomy associated with the industrial asset. The method can also include providing the condition of the industrial asset. Related apparatuses, systems, and computer-readable mediums are also provided.

Abstract: A method includes receiving data characterizing a two-dimensional target site image including an image of a first asset acquired by a camera. The camera has a first location during the acquisition of the two-dimensional target site image. The method also includes receiving data characterizing a three-dimensional model of a target site that includes a plurality of assets including the first asset. The three-dimensional model is annotated to at least identify the first asset. The method further includes generating a projected annotation of the first asset on the two-dimensional target site image by at least projecting the three-dimensional model based on the first location and orientation of the camera relative to the first asset during the acquisition of the target site image.

Abstract: Computer vision based systems, and methods are provided for generating plume analysis data from a plurality of input image frames as captured by a camera. Image data associated with a plume of gas is received and provided as inputs to a predictive model used to determine plume analysis data including a plume prediction segmentation mask associated with the plume of gas. Other attributes of the plume are also estimated from the prediction segmentation mask such as cross-sectional area, cross-sectional velocity, leak source pixel and volumetric leak rates. The plume analysis data can be provided as an overlay atop the image data. The plume analysis data overlaid atop the image data can be transmitted by and/or to one or more computing devices.