Patent ID: 12211008

DETAILED DESCRIPTION

As noted above, outdoor assets of a system require inspection and maintenance to ensure their proper condition and, in turn, the proper operation of the overall system. However, the number of these assets can make regular inspections challenging. Further, while some known attributes, such as installation date and material type, may suggest a given inspection schedule, environmental conditions (e.g., sunlight exposure, soil moisture level, ground relative temperature) can dramatically change the frequency with which inspection and maintenance must be performed. Embodiments of the systems and methods detailed herein relate to scheduling inspection of outdoor assets based on mapping variables, at least some of which are obtained via image processing, to failure probability.

FIG.1is a process flow of a method of scheduling inspections of assets according to embodiments of the invention. At block110, risk factors are obtained based on asset inspections and quantified to obtain a risk vector R for each asset of a set of inspected assets. Exemplary risk factors include mechanical breakdown, rotting progress (of a wooden pole, for example), animal invasion, treatment aging, vine crawling, angle (e.g., whether the asset is tilted or straight), and the risk of a tree falling. To obtain the risk vector R for each asset, a numerical score is assigned to a risk factor according to a predetermined range for each factor. Each risk factor is a continuous value contributing to a final risk score ranging 0 to 1 with 1 being the highest risk. Each risk factor may be calculated based on a risk feature in the image. For example, the total number of pixels from a forest area divided by the total number of pixels in a single image may be one risk factor, a forest risk factor. An average distance (in linear length) to the intersection areas in the map may be another risk factor. The final risk score is a weighted linear combination of all the risk factors. At block120, parameters are obtained for the inspected assets. An exemplary parameter includes the ratio, within a specified area that includes the asset, among water, barren land, grassy area, wooded area, roads and buildings. Other exemplary parameters include the relative location of an asset to the location of trees or high-rise buildings, distance between an asset and the closest road and intersection, size and traffic conditions of the nearest road, and population of proxy for road congestion in a specified region around the asset. Each of these exemplary indications is parameterized, as further discussed below, to provide a parameter vector P associated with each asset. These parameters need not be obtained via inspection. For inspected assets and other assets (per block150), the parameters may be obtained through geotagged image data (e.g., satellite images), for example. The process of obtaining the parameter vector P via image data is further detailed below.

At block130, the risk factors and parameters associated with inspected assets are used to determine a failure mapping. In machine learning, the process described below of determining the failure mapping matrix F may be referred to as learning. Based on:
Rk=FPk+Nk[EQ. 1]
For each inspected asset k, the risk factors discussed above are expressed as risk vector Rk, the parameters discussed above are expressed as the parameter vector Pk, and error factors are expressed as error vector Nk. Exemplary error factors are associated with variability, parameterization error, and clerical error. EQ. 1 may be used to solve for the linear failure mapping matrix F by using known techniques to minimize the error vector N such that each F vector (associated with each inspected asset) is solved by minimizing:
|R−FP|p[EQ. 2]
The norm of the matrix is indicated by p. The risk vector is subject to:
0≤R≤1   [EQ. 3]
R=[R1, R2, . . . Rh]T[EQ. 4]
The number of observations is given by h. Also,
P=[P1, P2, . . . Ph]T[EQ. 5]
The result provides
F=[F, F, . . . F]T[EQ. 6]

At block140, the failure mapping matrix (F) solved using EQ. 2 on the inspected assets is employed to determine risk factor vector R for each of the uninspected assets that are not inspected. At block150, parameters are ascertained for uninspected assets from geotagged images (e.g., satellite images) as further described below. The parameter vectors P of each of the uninspected assets are used in EQ. 1, along with the failure mapping matrix F, which was obtained by solving EQ. 2 at block130, to determine the risk vectors R. Parameter values (obtained from the images at block150) are assigned risk points at block160. The risk vector R obtained for each uninspected asset (at block140), in addition to the risk points assigned to the parameters (at block160) are combined to determine risk scores for each of the uninspected assets at block170. At block180, scheduling inspections is based on the risk scores determined at block170. For example, a wooden electric pole may have a list of risk factors associated with the normal distance to cross-section, the electric pole being in wooded area, and near vines, which will have a risk vector of [0.1 0.8 0.9]. This indicates a low risk (0.1) due to the cross-section, because the cross-section is far, but a high risk (0.8, 0.9) associated with rot and vine invasion. Another example could be a distribution transformer in a crowded region where each distribution transformer needs to supply many more customers resulting in high risk score due to the population score.

FIG.2is a process flow of a method obtaining and using parameter values according to embodiments of the invention. The parameter values may be obtained for blocks120and150(FIG.1). In alternate embodiments, the parameters used at block120may be obtained via physical inspection of the area in which an asset is located. The description below is exemplary and does not represent an exhaustive list of parameters. Additional parameter or risk factors (discussed with reference to blocks110and140atFIG.1) may be added based on the specific asset (e.g., windmill versus utility poll), for example. At block210, selecting a set of assets may include selecting uninspected assets for which inspections must be scheduled (this pertains to block150,FIG.1). Selecting the set of asserts may instead include selecting inspected assets that will be used to determine the failure mapping matrix F (this pertains to block120,FIG.1). At block220, retrieving location attributes of each asset selected at block210may include retrieving location information that was stored at the time of installation of the asset. This information may be alternately or additionally obtained from a global positioning system (GPS) associated with each asset, for example. Traditional triangulation techniques may also be used to obtain location information periodically and separate from inspections. The location attributes of each asset are used to retrieve terrestrial geotagged images around each asset location at block230. The images may be obtained from a geographic information system (GIS) application, for example. The images may be satellite images or other images (e.g., obtained with a drone) that illustrate the landscape around one or more assets. Exemplary images are discussed below and show that parameters for more than one asset may be determined based on the same image. Information obtained from the images for each of the assets is parameterized as also discussed below.

FIG.3illustrates an image300from which a set of parameter values are obtained according to embodiments of the invention. In the image shown inFIG.3, the assets are utility poles310. Areas with trees are indicated as310, and grassy areas are indicated as320. Other types of exemplary areas that may be identified based on the contents of an image are areas with water and areas with buildings or residences. The areas may be discerned from the images in any known way. For example, each pixel of the image may be assigned a grayscale value, and each of the types of areas (e.g., trees, barren, grass) may be identified based on a grayscale range. Once the different areas within the image300are identified, the information may be parameterized as a ratio of the number of pixels associated with each type of area to the total number of pixels. For example:

Pbarren=number_of⁢_pixels⁢_identified⁢_as⁢_barren⁢_landtotal_number⁢_of⁢_pixels[EQ.⁢7]Pwooded=number_of⁢_pixels⁢_identified⁢_as⁢_wooded⁢_landtotal_number⁢_of⁢_pixels[EQ.⁢8]
The parameters (used at block150,FIG.1) also have risk points associated with them (block160,FIG.1). These assignments of risk points may be based on a lookup table, for example, or another predetermined association between some or all of the parameterized information and a set of risk points. For example, weather exposure (e.g., relative direct sun exposure) of an asset indicated by the image300may be associated with risk points. As discussed with reference toFIG.4below, risk points may also be computed.

FIG.4illustrates an image400from which another set of parameter values are obtained according to embodiments of the invention. Again, a utility pole310is indicated as an exemplary asset in the image400. The distance410to the closest road and the distance420to the closest intersection are indicated. These distances410,420may be parameterized as Euclidean distances using latitude and longitude. Given a Euclidean distance Pdistance, the associated risk points may be determined as follows:

Pdistance⁢⁢_⁢⁢risk=∑i=1k⁢⁢αi⁢1Pdistancei[EQ.⁢9]
The number of road segments is k, and the associated risk factors for each type of the road segments is α, and Pdistanceis associated with a combination of all k of the road segments.

FIG.5is a block diagram of an exemplary system500to schedule inspections of assets according to embodiments of the invention. The system500includes one or more processors510to process the information needed to assign a risk score to each asset and thereby determine a schedule of inspections. The processor510executes instructions stored in one or more memory devices520. The system500receives information via an input interface530. For example, location attributes of the assets and the images needed to determine parameters for each asset may be obtained by the system500via the input interface530. Location information may be stored in one or more memory devices520in alternate embodiments. The input interface530may include a keyboard or other user input device as well as an interface to other processors. The input interface530may facilitate selection of the set of assets whose inspection schedule is to be determined. Information produced by the system500, such as the inspection schedule, for example, is output via an output interface540. Communication at the input interface530and output interface540may be wireless or through other known methods.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one more other features, integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

The flow diagrams depicted herein are just one example. There may be many variations to this diagram or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order or steps may be added, deleted or modified. All of these variations are considered a part of the claimed invention.

While the preferred embodiment to the invention had been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow. These claims should be construed to maintain the proper protection for the invention first described.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.