FAILURE MODELING BY INCORPORATION OF TERRESTRIAL CONDITIONS
A system and method to schedule inspections for a plurality of assets at a corresponding plurality of outdoor locations are described. The method includes retrieving location attributes of each of the plurality of assets, retrieving terrestrial geotagged images that include one or more of the plurality of assets, obtaining parameters associated with each of the plurality of assets based on information from the images, and determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping. The method also includes determining a risk score for each of the plurality of assets based on the corresponding risk factors, and scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.
1 . A method of scheduling inspections for a plurality of assets at a corresponding plurality of outdoor locations, the method comprising:
retrieving location attributes of each of the plurality of assets;
retrieving terrestrial geotagged images that include one or more of the plurality of assets;
obtaining parameters associated with each of the plurality of assets based on information from the images;
determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping;
determining a risk score for each of the plurality of assets based on the corresponding risk factors; and
scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.
2 . The method according to claim 1 , wherein the retrieving the terrestrial geotagged images includes retrieving satellite images.
3 . The method according to claim 1 , wherein the retrieving the terrestrial geotagged images includes retrieving images from a geographic information system (GIS) application.
4 . The method according to claim 1 , wherein the obtaining parameters based on the information from the images includes parameterizing a distance from each of the plurality of assets to a corresponding nearest road and a distance from each of the plurality of assets to a set of intersections.
5 . The method according to claim 4 , wherein the parameterizing the distance from each of the plurality of assets to the corresponding nearest road includes using a Euclidean distance based on latitude and longitude.
6 . The method according to claim 1 , wherein the obtaining parameters based on the information from the images includes parameterizing a ratio of a size of an area comprising woods, water, or grass to a total size of an area of the corresponding image.
7 . The method according to claim 6 , wherein the parameterizing the ratio for each area type includes solving:
P
area
_
type
=
number_of
_pixels
_identified
_as
_area
_type
total_number
_of
_pixels
,
the area type being the woods, the water, or the grass.
8 . The method according to claim 1 , further comprising determining risk points associated with each of the plurality of assets based on the associated parameters.
9 . The method according to claim 8 , wherein the determining the risk score of each of the plurality of assets is based on the corresponding risk factors and the corresponding risk points of each of the plurality of assets.
10 . The method according to claim 1 , further comprising determining the failure mapping (F) based on inspecting and analyzing a set of assets.
11 . The method according to claim 10 , wherein the inspecting and analyzing the set of assets includes determining risk factors of the set of assets (R) and parameters of the set of assets (P) and solving for the failure mapping F by minimizing
| R−FP| p
wherein p indicates the norm.
12 - 20 . (canceled)