Abstract
There is an increasing need for underwater condition
control for offshore steel platforms and wind and fish farming
facilities. Localized diagnostic techniques, such as magnetic field
non-destructive testing (NDT) methodologies for the structural
monitoring of such facilities, are very important for detecting early
signs of deterioration and damage, thus preventing fatal accidents.
The visualization of such magnetic fields can define the parts
that the diagnostic process will cover and lead to the detection of
structural flaws. A proper visualization is of the essence for the
better interpretation of data, informed decision making, and safety.
The InVizAR project (accessible at: www.hcilab.no/invizar2022) is
formulated to explore, design, and present a suitable visualization
of NDT data from inspections of jacket platforms. Tiny cracks
on the surface of the metal will generate invisible magnetic
anomalies, and the objective of InVizAR is to visualize these
signals. InVizAR utilizes augmented reality (AR) technology.
AR can visualize invisible signals and their spatial and temporal
qualities (4D), overlaying them atop the real-world view as layers
while facilitating team collaboration in metaverse spaces. InVizAR
utilizes a real-life dataset recorded in the ANDWIS project for the
client OceanTech Innovation AS. The dataset contains geospatial
and temporal values from an NDT probe on a jacket platform. The
Unity game engine is used for AR development. Therefore, a new
API structure is applied to the dataset based on the GeoJSON
format for Unity-importing purposes. In the initial stages of concept
design, potential visualization modes are identified based on the
visualization’s spatial elements (location) and the data feed’s
timing. Hence, it becomes clear that InVizAR facilitates a use
case in which an administrator wants to communicate the probing
results and their 4D qualities remotely to a client or co-worker.
Simultaneously, AR is chosen as a long-term strategy so the work
can be extended in the future and cover "on-location," contextual
AR visualizations of such datasets. Based on a literature search
and searches for commercial devices visualizing NDT results in
2D, a visualization heatmap is chosen. A heatmap is a powerful
tool for visualizing multidimensional data, with which individual
values can be expressed as colors. Subsequently, an AR heatmap
visualization of the ANDWIS dataset is developed in Unity and
is presented through a video recording. The heatmap visualizes
frequency deviations, which signify cracks, in red. A slider is
implemented to adjust the transparency of the AR visualization
and another to navigate between visualizations of past datasets.
Users can also tap on a point of the AR visualization and obtain
information about measurements in this area. Through an internal
peer-review process by the teams’ experts, the InVizAR AR
heatmap is considered a suitable and user-friendly visualization
that can serve current NDT use cases and the communication
of their results in AR/metaverse spaces. Future work will create
collaborative metaverse spaces for communicating and working on
visualization, as well as address additional visualization modes.
control for offshore steel platforms and wind and fish farming
facilities. Localized diagnostic techniques, such as magnetic field
non-destructive testing (NDT) methodologies for the structural
monitoring of such facilities, are very important for detecting early
signs of deterioration and damage, thus preventing fatal accidents.
The visualization of such magnetic fields can define the parts
that the diagnostic process will cover and lead to the detection of
structural flaws. A proper visualization is of the essence for the
better interpretation of data, informed decision making, and safety.
The InVizAR project (accessible at: www.hcilab.no/invizar2022) is
formulated to explore, design, and present a suitable visualization
of NDT data from inspections of jacket platforms. Tiny cracks
on the surface of the metal will generate invisible magnetic
anomalies, and the objective of InVizAR is to visualize these
signals. InVizAR utilizes augmented reality (AR) technology.
AR can visualize invisible signals and their spatial and temporal
qualities (4D), overlaying them atop the real-world view as layers
while facilitating team collaboration in metaverse spaces. InVizAR
utilizes a real-life dataset recorded in the ANDWIS project for the
client OceanTech Innovation AS. The dataset contains geospatial
and temporal values from an NDT probe on a jacket platform. The
Unity game engine is used for AR development. Therefore, a new
API structure is applied to the dataset based on the GeoJSON
format for Unity-importing purposes. In the initial stages of concept
design, potential visualization modes are identified based on the
visualization’s spatial elements (location) and the data feed’s
timing. Hence, it becomes clear that InVizAR facilitates a use
case in which an administrator wants to communicate the probing
results and their 4D qualities remotely to a client or co-worker.
Simultaneously, AR is chosen as a long-term strategy so the work
can be extended in the future and cover "on-location," contextual
AR visualizations of such datasets. Based on a literature search
and searches for commercial devices visualizing NDT results in
2D, a visualization heatmap is chosen. A heatmap is a powerful
tool for visualizing multidimensional data, with which individual
values can be expressed as colors. Subsequently, an AR heatmap
visualization of the ANDWIS dataset is developed in Unity and
is presented through a video recording. The heatmap visualizes
frequency deviations, which signify cracks, in red. A slider is
implemented to adjust the transparency of the AR visualization
and another to navigate between visualizations of past datasets.
Users can also tap on a point of the AR visualization and obtain
information about measurements in this area. Through an internal
peer-review process by the teams’ experts, the InVizAR AR
heatmap is considered a suitable and user-friendly visualization
that can serve current NDT use cases and the communication
of their results in AR/metaverse spaces. Future work will create
collaborative metaverse spaces for communicating and working on
visualization, as well as address additional visualization modes.
