Apparatus, associated systems, and methods for managing structural non-conformities and abnormalities.

Abstract

The present invention provides an apparatus, as well as associated systems and methods, for managing structural non-conformities and abnormalities. [Solution] This specification discloses an apparatus, as well as associated systems and methods for managing non-conforming anomalies within a structure. The apparatus generates a digital representation of the structure, maps anomalies, defines dynamically adjustable zones, and associates anomalies with corresponding zones. Each zone is linked to an inspection plan outlining anomaly evaluation criteria and corrective actions. The system automatically executes the inspection plan and generates instructions to guide corrective actions for the actual structure. This dynamic zone-based approach improves anomaly tracking, evaluation, and resolution, enabling structured, adaptable anomaly management for manufacturing, maintenance, and repair applications.

Description

Technical Field

[0001] The present disclosure generally relates to anomaly management systems, and more particularly to a dynamic zone-based system for managing non-conforming anomalies in a structure. Background Art

[0002] Management of non-conformance (NC) anomalies in large or complex structures such as aircraft, rockets, microchips, and other assemblies can present significant challenges due to the volume and complexity of anomalies. Conventional anomaly tracking methods rely on static location-based approaches that rigidly divide a structure into predetermined sections. However, these methods do not scale efficiently for large or complex structures where anomalies can occur at unpredictable locations across a wide area. Furthermore, static systems lack flexibility and cannot adapt to operational changes such as accessibility constraints, shifting inspection priorities, or changes in structural configuration over time.

[0003] These static systems often result in inefficient inspections, inconsistent resource allocation, and an inability to adapt anomaly management strategies to real-world conditions. As a result, conventional inspections can be time-consuming, costly, and less effective at identifying and addressing critical anomalies. Summary of the Invention Means for Solving the Problems

[0004] The subject matter of the present application was developed in accordance with the current state of the art, particularly in response to the shortcomings of anomaly management systems that have not yet been fully solved by currently available technology. Accordingly, the subject matter of the present application has been developed to provide an apparatus, and associated systems and methods, for managing non-conforming anomalies that overcome at least some of the above-described shortcomings of the prior art.

[0005] The following is a non-exhaustive list of examples of subject matter disclosed herein that may or may not be claimed.

[0006] This specification discloses a device for managing non-conformities within a structure, comprising a processor and memory for storing code executable by the processor. The device defines a plurality of zones within a digital representation of the structure. The size of each of the plurality of zones is dynamically adjustable. Each of the plurality of zones is associated with a corresponding inspection plan of a plurality of different inspection plans. The device dynamically adjusts the size of at least one of the plurality of zones based on the operating requirements of the digital representation of the structure. The device also maps a plurality of anomalies identified by an anomaly identification system to corresponding locations within the digital representation of the structure and dynamically associates each of the plurality of anomalies with a corresponding zone within the plurality of zones in the digital representation of the structure. The device further automatically executes the inspection plans associated with the zones among the plurality of zones and generates instructions to guide at least one corrective action for each of the plurality of anomalies. The instructions associated with each corresponding of the plurality of anomalies are generated according to the inspection plan of the zone associated with the corresponding anomaly of the plurality of anomalies. The aforementioned subject matter of this paragraph characterizes Example 1 of this disclosure.

[0007] The device subdivides at least one of multiple zones into multiple subzones within a digital representation of the structure. The size of each of the multiple subzones is dynamically adjustable. Each of the multiple subzones is associated with a corresponding inspection plan of multiple different inspection plans. The device also dynamically associates at least one of multiple anomalies with a corresponding subzone of the multiple subzones within a digital representation of the structure. The device further automatically executes the inspection plans associated with the subzones among the multiple subzones and generates instructions to guide at least one corrective action for each of the multiple anomalies associated within the multiple subzones. The instructions associated with each corresponding anomaly are generated according to the inspection plan of the subzone associated with the corresponding anomaly of the multiple anomalies. The subject matter described herein characterizes Example 2 of the present disclosure, which also includes the subject matter described in Example 1 above.

[0008] The device defines multiple segments within a digital representation of a structure. Each of the multiple segments represents a predetermined portion of the digital representation of the structure. The multiple segments are configured to organize the digital representation of the structure into identifiable areas for managing multiple anomalies. The device defines at least one of multiple zones within the corresponding segments of the multiple segments in the digital representation of the structure. The subject matter described herein characterizes Example 3, which also includes the subject matter described in any of Examples 1-2 above.

[0009] Each corresponding anomaly identified by the anomaly identification system is identified by a tripoint within the digital representation of the structure. The tripoint includes a horizontal position defined with respect to a reference plane and measured along a first axis parallel to the reference plane, a vertical position measured along a second axis perpendicular to the reference plane, and a longitudinal position measured along a third axis extending along the length of the digital representation of the structure and intersecting the reference plane. The subject matter described above characterizes Example 4 of the present disclosure, which also includes the subject matter from any of Examples 1 to 3 above.

[0010] Each of the three corresponding points for multiple anomalies is used to dynamically associate each of the multiple corresponding points for multiple anomalies with the corresponding zones of multiple zones in the digital representation of the structure. The subject matter described above in this paragraph characterizes Example 5 of the present disclosure, which also includes the subject matter described in Example 4 above.

[0011] The step of defining multiple zones within a digital representation of a structure includes the step of automatically defining each of the multiple zones based on the operational requirements of the digital representation of the structure, and the step of automatically associating an inspection plan corresponding to each of the multiple zones. The subject matter described above in this paragraph characterizes Example 6 of the present disclosure, which also includes the subject matter from any of Examples 1 to 5 above.

[0012] The step of defining multiple zones within a digital representation of a structure includes enabling the user to manually define boundaries for each of the multiple zones and manually associating inspection plans corresponding to each of the multiple zones. The subject matter described herein characterizes Example 7 of the present disclosure, which also includes the subject matter described in any of Examples 1-6 above.

[0013] An anomaly characteristic is determined for each of a group of anomalies identified by the anomaly identification system. The anomaly characteristic includes at least one of the following: the location of each of the anomalies, the type of each of the anomalies, the size of each of the anomalies, the severity level of each of the anomalies, and the status of each of the anomalies. The subject matter described herein characterizes Example 8 of the present disclosure, which also includes the subject matter described in any of Examples 1-7 above.

[0014] The digital representation of the structure is updated in real time based on at least one of the following: a newly identified anomaly, a change in the anomaly status of at least one of multiple anomalies, or adjustments to multiple zones. The subject matter described herein is used to characterize Example 9 of the present disclosure, which also includes the subject matter described in any of Examples 1-8 above.

[0015] This specification further discloses a nonconformity anomaly management system including a structure and apparatus containing multiple anomalies. Each of the multiple anomalies requires at least one corrective action. The apparatus includes a processor and memory for storing code executable by the processor. The apparatus defines multiple zones within a digital representation of the structure. The size of each of the multiple zones is dynamically adjustable. Each of the multiple zones is associated with a corresponding inspection plan of several different inspection plans. The apparatus dynamically adjusts the size of at least one of the multiple zones based on the operating requirements of the digital representation of the structure. The apparatus also maps the multiple anomalies identified by the anomaly identification system to corresponding locations within the digital representation of the structure and dynamically associates each of the multiple anomalies with a corresponding zone within the multiple zones in the digital representation of the structure. The apparatus further automatically executes the inspection plans associated with the zones among the multiple zones and generates instructions to guide at least one corrective action for each of the multiple anomalies. The instructions associated with each corresponding one of the multiple anomalies are generated according to the inspection plan of the zone associated with the corresponding anomaly of the multiple anomalies. At least one corrective action is performed for each of the multiple anomalies of the structure, based on instructions generated by the device, derived from a digital representation of the structure. The aforementioned subject matter of this paragraph characterizes Example 10 of the present disclosure.

[0016] The anomaly management system subdivides at least one of multiple zones into multiple subzones within a digital representation of the structure. The size of each of the multiple subzones is dynamically adjustable. Each of the multiple subzones is associated with a corresponding inspection plan of multiple different inspection plans. The system dynamically associates at least one of the multiple anomalies with a corresponding subzone of the multiple subzones within a digital representation of the structure. The system further automatically executes the inspection plan associated with the subzones among the multiple subzones, generates instructions to guide at least one corrective action for each of the multiple anomalies associated within the multiple subzones, and the instructions associated with each corresponding anomaly are generated according to the inspection plan of the subzone associated with the corresponding anomaly of the multiple anomalies. The subject matter described herein characterizes Example 11, which also includes the subject matter described in Example 10 above.

[0017] The anomaly management system defines multiple segments within a digital representation of a structure. Each of the multiple segments corresponds to a given portion within the digital representation of the structure, and the multiple segments are configured to organize the digital representation of the structure into identifiable areas for managing multiple anomalies. The system also defines at least one of multiple zones within the corresponding segments of the multiple segments within the digital representation of the structure. The subject matter described herein characterizes Example 12, which also includes the subject matter described in any of Examples 10–11 above.

[0018] Each corresponding anomaly identified by the anomaly identification system is identified by a tripoint within the digital representation of the structure. The tripoint is defined in the digital representation with respect to a reference plane and includes a horizontal position measured along a first axis parallel to the reference plane, a vertical position measured along a second axis perpendicular to the reference plane, and a longitudinal position measured along a third axis extending along the length of the structure and intersecting the reference plane. The subject matter described above characterizes Example 13 of the present disclosure, which also includes the subject matter from any of Examples 10-12 above.

[0019] An anomaly characteristic is determined for each of a group of anomalies identified by the anomaly identification system. The anomaly characteristic includes at least one of the following: the location of each of the anomalies, the type of each of the anomalies, the size of each of the anomalies, the severity level of each of the anomalies, and the status of each of the anomalies. The subject matter described herein characterizes Example 14 of the present disclosure, which also includes the subject matter described in any of Examples 10-13 above.

[0020] The digital representation of the structure is updated in real time based on at least one of the following: a newly identified anomaly, a change in the anomaly status of at least one of multiple anomalies, or adjustments to multiple zones. The subject matter described herein characterizes Example 15 of the present disclosure, which also includes the subject matter described in any of Examples 10–14 above.

[0021] This specification further discloses a method for managing non-conforming anomalies within a structure. The method includes the step of identifying a plurality of anomalies within a structure using an anomaly identification system. The method also includes the step of mapping each of the plurality of anomalies to a corresponding location in a digital representation of the structure. The method further includes the step of defining a plurality of zones within the digital representation of the structure. The size of each of the plurality of zones is dynamically adjustable and associated with corresponding inspection plans of a plurality of different inspection plans. The method further includes the step of dynamically adjusting at least one of the plurality of zones within the digital representation of the structure based on the operational requirements of the structure, and the step of dynamically associating each of the plurality of anomalies with a corresponding zone in the plurality of zones within the digital representation of the structure. The method also includes the step of automatically executing the inspection plans associated with the corresponding zones in the plurality of zones, and the step of executing at least one corrective action for each of the plurality of anomalies within the structure according to the inspection plans of the zones associated with the corresponding anomalies of the plurality of anomalies. The aforementioned subject matter of this paragraph characterizes Example 16 of this disclosure.

[0022] The method includes the step of subdividing at least one of several zones in a digital representation of a structure into several subzones. The size of each of the several subzones is dynamically adjustable, and each of the several subzones is associated with a corresponding inspection plan of several inspection plans. The subject matter described herein characterizes Example 17, which also includes the subject matter described in Example 16 above.

[0023] The method includes the step of defining a digital representation of a structure into a plurality of segments. Each of the plurality of segments corresponds to a given portion of the digital representation of the structure, and defines at least one of a plurality of zones within the corresponding segment of the plurality of segments in the digital representation of the structure. The subject matter described herein characterizes Example 18 of the present disclosure, which also includes the subject matter described in any of Examples 16–17 above.

[0024] The method includes the step of determining three points in a digital representation of the structure for each of several anomalies with respect to a reference plane. The three points include a horizontal position, a vertical position, and a longitudinal position. The subject matter described herein characterizes Example 19 of the present disclosure, which also includes the subject matter described in any of Examples 16–18 above.

[0025] The method includes the step of generating a digital representation of the structure in real time based on at least one of the following: a newly identified anomaly, a change in the anomaly status of at least one of several anomalies, or adjustments of several zones. The subject matter described herein characterizes Example 20 of the present disclosure, which also includes the subject matter described in any of Examples 16-19 above.

[0026] The described features, structures, advantages, and / or characteristics of the subject matter of the present disclosure may be combined in any suitable manner in one or more examples and / or implementations. In the following description, numerous specific details are provided to give a thorough understanding of examples of the subject matter of the present disclosure. Those skilled in the art will recognize that the subject matter of the present disclosure may be practiced without one or more of the specific features, details, components, materials, and / or methods of a particular example or implementation. In other examples, additional features and advantages may be recognized in particular examples and / or implementations that may not be present in all examples or implementations. Furthermore, in some instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the subject matter of the present disclosure. The features and advantages of the subject matter of the present disclosure will become more fully apparent from the following description and the appended claims, or may be learned by practice of the subject matter set forth below.

[0027] In order that the advantages of the subject matter may be more readily understood, a more specific description of the subject matter briefly described above is provided by reference to specific examples illustrated in the accompanying drawings. Understanding that these drawings, which are not necessarily drawn to scale, depict only certain examples of the subject matter and therefore should not be considered as limiting its scope, the subject matter will be described and explained with additional specificity and detail through the use of the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] [Figure 1] FIG. 1 is a schematic diagram of an apparatus for managing non-conforming anomalies in a structure, in accordance with one or more examples of the present disclosure. [Figure 2A] FIG. 2A is a schematic table illustrating segmented partitioning of a digital representation of a structure that defines segments into a plurality of zones, in accordance with one or more examples of the present disclosure. [Figure 2B] FIG. 2B is a schematic table illustrating segmented partitioning of the digital representation of the structure of FIG. 2A that further defines at least one of the plurality of zones into a plurality of subzones, in accordance with one or more examples of the present disclosure. [Figure 3A] A graphical representation of one example of a structure, in which multiple segments and multiple zones are defined within the structure, as shown in one or more examples of the present disclosure. [Figure 3B] Figure 3A is a graphical representation of the structure, in which multiple segments, multiple zones, and multiple subzones are defined relative to the external portion of the structure, according to one or more examples of the present disclosure. [Figure 3C] Figure 3A is a graphical representation of the structure, in which multiple segments and multiple zones are defined for the internal parts of the structure, according to one or more examples of the present disclosure. [Figure 4A] This is a schematic table showing multiple locations of abnormalities, based on one or more examples of the present disclosure. [Figure 4B] This is a schematic side view of a typical structure showing multiple locations of abnormalities, including the multiple abnormalities shown in Figure 4A, according to one or more examples of the present disclosure. [Figure 5] This is a schematic graphical representation of anomaly occurrences within corresponding zones and subzones, as illustrated by one or more examples of the present disclosure. [Figure 6] One or more examples of this disclosure include a schematic graphical representation showing the association between inspection plans and corresponding zones and subzones, as well as multiple anomalies identified within each zone or subzone. [Figure 7] This is a schematic perspective view of a non-conformity anomaly management system based on one or more examples of the present disclosure. [Figure 8] This is a schematic flowchart illustrating a method for managing non-conformities within a structure, based on one or more examples of the present disclosure. [Modes for carrying out the invention]

[0029] Throughout this specification, any reference to “one example,” “an example,” or similar language means that the particular features, structure, or characteristic described in relation to the example is included in at least one example of this disclosure. Throughout this specification, any occurrence of the phrases “in one example,” “in one example,” and similar wording may, though not necessarily, all refer to the same example. Similarly, the use of the term “implementation” means an implementation having a particular feature, structure, or characteristic described in relation to one or more examples of this disclosure, but an implementation may be associated with one or more examples if there is no clear correlation indicating otherwise.

[0030] This specification discloses apparatus, as well as associated systems and methods for managing nonconformities within a structure. As used herein, a nonconformity (NC) abnormality refers to a deviation from a given design, manufacture, or operating specification, including, but not limited to, defects and other abnormalities that affect the performance, integrity, reliability, or appearance of the structure. NC abnormalities, hereafter referred to as “abnormalities,” may arise from material mismatches, assembly errors, environmental damage, fatigue, or other factors that cause a component or system to fall outside acceptable tolerances. These abnormalities may require evaluation, tracking, and corrective action to ensure compliance with safety and performance standards.

[0031] The disclosed device provides a dynamic, zone-based anomaly management approach that improves anomaly tracking, evaluation, and resolution. The device converts anomaly data from various sources into a structured format within a digital representation of the structure. Thus, the device maps anomalies onto the digital representation of the structure, defines adjustable inspection zones, and associates anomalies with corresponding zones. Each zone is not statically defined; rather, zones can be reconfigured based on operational requirements, including structural changes or inspection priorities, while maintaining anomaly associations. This ensures that anomaly tracking remains accurate even if zones are resized, merged, or split in response to changing conditions.

[0032] The device is particularly beneficial for large and / or complex structures and assemblies such as aircraft, ships, industrial equipment, or large infrastructure components. The dynamic nature of the zones allows the system to adapt across different stages of the structure's lifecycle, including manufacturing, quality control, maintenance, and repair operations. The disclosed device enables real-time updates of the digital representation of the structure when an anomaly is identified, the anomaly status changes, or the zone is adjusted. The ability to dynamically manage anomalies within a changing structural framework enhances the efficiency of anomaly tracking and improves the overall anomaly resolution workflow.

[0033] In some examples, the disclosed devices and systems may be configured to interface with external anomaly management tools, quality management systems (QMS), or inspection databases. This interoperability allows for the import of anomaly and mapping data from external sources such as CAD models, digital twin platforms, or manual inspection records, ensuring seamless integration with existing workflows.

[0034] Figure 1 shows one example of a device 100 for managing anomalies within a structure. As used herein, a structure refers to a physical object or assembly that is subject to anomaly monitoring and corrective action. This includes, but is not limited to, large or complex structures such as aircraft fuselages, aircraft wings, ship hulls, automobile chassis, industrial pipelines, and composite assemblies. Throughout this description, an aircraft fuselage is used as a non-limiting example to illustrate the function of the disclosed device. However, the device 100 and associated systems and methods may be applied to a variety of other structures without departing from the scope of this disclosure.

[0035] The device 100 also includes a processor 102 and a memory 104. The memory 104 stores code that can be executed by the processor 102 to perform anomaly management functions. The processor 102 and the memory 104 are communicably linked to an anomaly management interface 105, which facilitates various operations related to anomaly identification, tracking, and planning of corrective actions. The anomaly management interface 105 includes, but is not limited to, several modules, including a zone definition module 106, a mapping module 108, anomaly association module 110, and an inspection planning module 112.

[0036] The zone definition module 106 is configured to define multiple zones within a digital representation of a structure. As used herein, the digital representation of a structure refers to a virtual model or mapped dataset corresponding to a physical structure. The digital representation may be generated from a CAD model, sensor data, inspection records, or other data source that reflects the physical properties, anomalies, and zoning configuration of the physical structure. Each zone represents a designated inspection area within the digital representation of the structure, enabling systematic anomaly tracking and evaluation.

[0037] Multiple zones may initially be defined based on at least one of the structural characteristics, functions, or assembly stages of the digital representation of the structure. In some examples, defining zones based on structural characteristics involves dividing the digital representation into areas corresponding to physical components of an aircraft, such as the fuselage, wings, or tail. This approach helps align inspections with the structural layout and makes it easier to track anomalies within specific structural elements. Zones may also be defined based on function, with each zone corresponding to a specific operational role within the structure. For example, in an aircraft, functional zones may be created for areas related to pressurization systems, avionics, or structural reinforcement, thereby enabling targeted inspections based on critical functions. Additionally or alternatively, zones may be determined according to the assembly stage, thereby allowing inspections to remain aligned with changing manufacturing or maintenance needs. For example, during initial assembly, zones may focus on areas where structural joining is performed, and in later stages, zones may be adjusted within the digital representation to take into account accessibility constraints when specific parts are fully integrated.

[0038] Multiple zones are dynamically adjustable, meaning their size, shape, and / or scope (e.g., corresponding boundaries) can be changed according to the operational requirements of the digital representation of the structure. Since the need for a digital representation of a structure can change over time, multiple zones are dynamically adjustable according to operational requirements. As used herein, operational requirements refer to factors that influence the configuration, adjustment, or execution of inspection and anomaly management processes. These factors may include, but are not limited to, changes in accessibility, shifts in inspection priorities, structural changes during manufacturing, maintenance, or repair, environmental conditions, regulatory compliance considerations, and system performance constraints. For example, zones may be expanded to cover a wider inspection area when a broader anomaly assessment is required, such as after a major structural change. Conversely, zone sizes may be reduced to focus on specific smaller sections. The ability to dynamically adjust zones ensures that inspections remain efficient and responsive to changing conditions.

[0039] In some examples, the zone definition module 106 is configured to automatically define one of several zones based on the operational requirements of the digital representation of the structure. By leveraging these operational requirements, the zone definition module 106 can dynamically establish zones in a way that can optimize inspection efficiency. In other examples, the zone definition module 106 is configured to allow the user to manually define one boundary for each of several zones 124. Manual zone definition allows the user to incorporate specific knowledge of structural nuances, accessibility constraints, or inherent inspection priorities that may not be fully captured by given operational requirements. Through the user interface, the user can contour zone boundaries based on practical considerations such as ease of access, visibility, or the complexity of the required inspection procedure. In yet another example, the definition of zones in the digital representation of the structure may be a combination of both automated and manual processes to allow the user to refine or adjust the automatically generated zones based on real-world inspection needs.

[0040] Each zone is associated with a corresponding inspection plan. The inspection plan defines the scope, criteria, and procedures for evaluating the zone, including, where applicable, general inspection and assessment of specific anomalies within the zone. Each of multiple zones is associated with a corresponding inspection plan among several different inspection plans. That is, each inspection plan is tailored specifically to its respective zone, meaning that the inspection plan is based on the specific characteristics and requirements of that zone. Therefore, an inspection plan applies only to the zone to which it is associated.

[0041] In some examples, inspection plans corresponding to each zone are automatically associated based on predetermined criteria such as zone characteristics, inspection history, or structural importance. This automation enables efficient, data-driven inspection planning, ensuring that appropriate inspection plans are assigned to each zone based on specific attributes. In other examples, users may manually associate inspection plans with zones, allowing for the flexibility to adapt inspection requirements based on specific knowledge, priorities, or real-time considerations. In yet another example, the association of inspection plans may be a combination of automated and manual processes, in which case the user can review or modify the automatically assigned inspection plans as needed.

[0042] An inspection plan may specify general inspection criteria for the entire zone and / or include targeted inspection instructions for specific anomalies. For example, if a zone contains 200 rivets, the inspection plan may require a general evaluation of all rivets to ensure that each rivet meets quality and safety standards. Additionally or alternatively, the inspection plan may include specific instructions for inspecting a portion of the rivets that have pre-identified anomalies, and specific instructions for inspecting and verifying those rivets individually. This approach allows for flexible anomaly management, as each inspection plan can specify general inspection procedures for zones without pre-identified anomalies, while inspection plans for zones with known anomalies can incorporate more specific inspection procedures.

[0043] In some examples, before defining multiple zones, the zone definition module 106 may first divide the digital representation of the structure into segments. Segments function as broader structural divisions than multiple zones, helping to organize large or complex structures into manageable sections before zone definitions are made. Thus, segmentation is applied to reflect the physical organization of the structure.

[0044] Segments are predefined based on structural or operational factors and remain fixed throughout the anomaly management process, while zones within each segment are dynamically adjustable to accommodate real-time inspection and maintenance needs. Segmentation is particularly useful in digital representations when a structure is too large to be effectively managed as a single entity, or when its components are naturally divided based on assembly, accessibility, or operational function. For example, a digital representation of an aircraft fuselage may be segmented up to segment A, segment B, segment C, segment D, segment E, and segment N, each segment representing a distinct part of the fuselage used in manufacturing and maintenance planning. Segment N represents the Nth segment in the sequence, corresponding to the final section of the digital representation of the structure.

[0045] While segmentation alone does not determine the inspection plan, it provides a structured framework that facilitates efficient zone definition within the digital representation. Because the segments remain fixed, zones are defined within them to enable flexible, real-time anomaly management and inspection coordination. Conversely, for smaller or less complex structures, segmentation may not be necessary, and zones can be defined directly across the entire digital representation of the structure.

[0046] In some examples, the zone definition module 106 may subdivide at least one of several zones into several subzones within the digital representation of the structure. Each subzone represents a smaller, finer division within the zone, enabling improved inspection planning and anomaly management within the digital representation. Thus, subzones allow for a more detailed focus on specific areas that may require separate inspection procedures or anomaly assessments compared to a broader zone. Subdividing zones into subzones within the digital representation can be beneficial when specific areas within a zone have different inspection requirements, accessibility constraints, or anomaly concentrations. For example, different parts of a component within the digital representation may experience varying stress levels, environmental exposures, or manufacturing tolerances that require different inspection techniques or criteria.

[0047] Each subzone is dynamically adjustable within its digital representation, meaning its size (e.g., boundaries) can be changed based on operational requirements, similar to zones. The dynamic adjustability of subzones ensures that inspections continue to respond to changes in accessibility, inspection priorities, or structural modifications reflected in the digital representation. Furthermore, each subzone is associated with a corresponding inspection plan that defines the scope, criteria, and procedures for evaluating anomalies within that subzone. Each subzone's inspection plan is unique in that it allows for inspection requirements tailored to the specific characteristics of the subzone within its digital representation. In some examples, the subzones themselves may be further subdivided to allow for a hierarchical structuring of inspection areas that can be iteratively refined to accommodate changing operational needs.

[0048] The zone definition module 106 is further configured to dynamically adjust the size of at least one of multiple zones based on the operational requirements of the digital representation of the structure. Unlike simply defining zones, dynamic adjustment actively changes zone boundaries in real time based on changing factors. For example, if a section of the digital representation of the structure becomes temporarily inaccessible due to the progress of assembly, the associated zone may be redefined to exclude that section until access is restored. Conversely, if the emergence of a new anomaly trend indicates the need for a wider inspection range, the zone may be expanded to encompass a wider area.

[0049] Referring to Figure 2A, a table is shown illustrating one example of a digital representation 120 of a structure defined in multiple zones 124. As described above, in some examples, the digital representation 120 of the structure is segmented into multiple segments 122 before the multiple zones 124 are defined. For example, the multiple segments 122 may include a first segment 122A, a second segment 122B, a third segment 122C, a fourth segment 122D, a fifth segment 122E, and an Nth segment 122N, where N represents the last segment of the digital representation based on the given segmentation. Segmentation provides an initial structural framework for improving anomaly management and inspection planning. In other examples, the digital representation 120 of the structure is not segmented into multiple segments and includes only multiple zones 124.

[0050] Multiple zones 124 are defined within a digital representation 120 of the structure, and when segmentation is used, at least one zone 124 is established within a corresponding segment of multiple segments 122. For example, in the digital representation, the first segment 122A includes the first zone 124A, the second zone 124B, the third zone 124C, and the fourth zone 124D, and the second segment 122B includes the fifth zone 124E, the sixth zone 124F, the seventh zone 124G, and the eighth zone 124H. Thus, both the first segment 122A and the second segment 122B contain multiple zones 124. In other examples, the number of zones 124 per segment 122 may vary based on structural complexity, anomaly distribution, or other inspection requirements. For example, a segment may define only a single zone, while other segments may contain multiple zones to accommodate more detailed inspections. This segmentation and zoning structure within the digital representation helps ensure that each defined zone remains a manageable unit for inspection and anomaly tracking. For simplicity, not all zones are labeled in Figures 2A–6, and additional zones may exist within the digital representation 120 of the structure.

[0051] Each of the multiple zones 124 is associated with a corresponding inspection plan among the multiple inspection plans 134. For example, the first zone 124A may be associated with the first inspection plan 134A, the second zone 124B with the second inspection plan 134B, the third zone 124C with the third inspection plan 134C, the fourth zone 124D with the fourth inspection plan 134D, the fifth zone with the fifth inspection plan 134E, the sixth zone 124F with the sixth inspection plan 134F, the seventh zone 124G with the seventh inspection plan 134G, and the eighth zone 124H with the eighth inspection plan 134H. Each of the multiple inspection plans 134 is different from one another, as each inspection plan is tailored to address specific inspection requirements, anomaly types, or structural considerations within the corresponding zone 124.

[0052] Referring to Figure 2B, at least one of the multiple zones 124 in the digital representation can be further subdivided into multiple subzones 126. As described above, each zone 124 provides a separate inspection area within the digital representation 120. However, in some examples, at least one of the multiple zones 124 may be further subdivided into subzones 126 to provide a more granular level of anomaly management and inspection planning. For example, within the first segment 122A, the first zone 124A may be subdivided into the first subzone 126A and the second subzone 126B, and the fifth zone 124E may be subdivided into the third subzone 126C and the fourth subzone 126D. Similarly, other zones in the multiple zones 124 may be subdivided into multiple subzones. The number and size of subzones 126 within each zone 124 may be dynamically adjusted based on factors such as anomaly density, inspection requirements, or accessibility constraints. Similar to segmentation and zoning structures, subdivision into subzones helps ensure that inspections can be performed at an appropriate level of detail without compromising efficiency. For simplicity, not all subzones 126 are labeled in Figures 2B–6, and additional subzones may exist within the digital representation 120 of the structure.

[0053] When a pre-divided zone (i.e., a parent zone) is subdivided into multiple subzones 126, the inspection plan pre-associated with the parent zone is no longer applied at the zone level, and instead, each subzone 126 is assigned a corresponding inspection plan from among multiple inspection plans. Each subzone inspection plan is tailored to the specific characteristics and inspection requirements of its respective subzone and differs from both the original inspection plan of the parent zone and the inspection plans of the other subzones. For example, the first subzone 126A is associated with the first subzone inspection plan 134A-1, and the second subzone 126B is associated with the second subzone inspection plan 134A-2, where 134A-1 and 134A-2 are different inspection plans. Similarly, additional subzones are each associated with a corresponding inspection plan tailored to the specific characteristics and inspection requirements of their respective subzones. For simplicity, not all inspection plans 134 are labeled in Figures 2B to 6, and additional inspection plans may exist within the digital representation 120 of the structure.

[0054] As shown in Figures 3A to 3C, the multiple zones 124 are visually represented on the graphic visualization of the digital representation 120 of the structure. In this example, the digital representation 120 of the structure corresponds to a fuselage segmented into multiple segments 122 along its length. Within each of the multiple segments 122, multiple zones 124 are defined to organize inspection areas. Figure 3A shows an example where multiple zones 124 are defined across the digital representation of the fuselage, with each zone visually depicted by a distinct boundary. The digital representation of the fuselage is divided into both an external and an internal part along its length. For example, the first segment 122A includes the first zone 124A, the second zone 124B, the third zone 124C, and the fourth zone 124D, and the second segment 122B includes the fifth zone 124E, the sixth zone 124F, the seventh zone 124G, and the eighth zone 124H.

[0055] Figure 3B focuses on the external zone of the digital representation of the fuselage, which is divided into an upper external section and a lower external section. Additionally, the upper external section is further subdivided into subzones, including an upper crown section and an upper window section, to allow for more detailed inspection of specific structural areas. For example, the first segment 122A includes the first zone 124A, which is subdivided into a first subzone 126A and a second subzone 126B, as well as a fourth zone 124D. Figure 3C focuses on the internal zone of the digital representation of the fuselage, which is similarly divided into an upper internal section and a lower internal section. For example, the first segment 122A includes a second zone 124B and a third zone 124C. To address accessibility constraints, optimize inspection scope, and ensure that appropriate attention is paid to structural areas during anomaly evaluation, the digital representation may be defined in this way, in terms of zones and subzones.

[0056] Referring back to Figure 1, the mapping module 108 is configured to map multiple anomalies identified by the anomaly identification system to corresponding locations in the digital representation of the structure. The anomaly identification system refers to a combination of systems, devices, or technologies configured to detect, record, and classify anomalies within the structure. The anomaly identification system may include automated inspection tools such as non-destructive testing (NDT) equipment (e.g., ultrasonic, eddy current, or radiation inspection systems), optical or infrared imaging devices, laser scanning systems, or other sensor-based detection mechanisms. Additionally, the anomaly identification system may incorporate manual inspections, including inspection reports, maintenance logs, and NC reports generated by inspectors.

[0057] The mapping module 108 processes anomaly data obtained from the anomaly identification system and determines the specific location of each anomaly within the digital representation of the structure. The locations may be mapped using coordinate-based criteria, structural identifiers, or predetermined reference points to establish the precise positional relationship between the anomaly and the structure. For example, the mapping module 108 may utilize data from anomaly records, CAD models, digital twin technology, barcoding, RFID systems, and other inspection tools to determine and record the locations of anomalies in the digital representation of the structure.

[0058] In some examples, an anomaly characteristic is determined for each of several anomalies 114 identified by the anomaly identification system. Anomaly characteristics refer to various attributes that define the nature and status of the anomaly. These anomaly characteristics may include, but are not limited to, the location of the anomaly in the digital representation of the structure, the type of anomaly (e.g., corrosion, crack, fastener problem, or surface damage), the size of the anomaly, the severity level of the anomaly, and the status of the anomaly (e.g., newly detected, under evaluation, or resolved). By capturing and organizing the anomaly characteristics, the mapping module 108 can ensure that each anomaly is classified and prioritized within the inspection and repair workflow. For example, anomalies with a higher severity level may be flagged for immediate corrective action, while minor anomalies may be monitored over time.

[0059] As shown in Figure 4A, in some examples, multiple anomalies 114 identified by the anomaly identification system are identified by three-point locations used to pinpoint their location within the digital representation of the structure. Specifically, the table in Figure 4A shows anomalies labeled as Anomaly 1 to Anomaly 10 and 114A to 114N, respectively. The table provides three-point location data for each anomaly, including horizontal position 128, vertical position 130, and longitudinal position 132. The three-point locations are defined relative to a reference plane that serves as a fixed coordinate system used to define the spatial location of the anomaly within the digital representation 120 of the structure. For example, in an aircraft application, the reference plane can correspond to a predetermined structural datum, such as the aircraft centerline or a manufacturing reference grid. As shown in Figure 4B, to provide precise localization for each of the multiple anomalies within the digital representation 120 of the structure, anomalies 114A to 114N, along with other identified anomalies, are mapped along these three axes.

[0060] The three points provide a precise spatial definition of each anomaly 114 within the digital representation 120 of the structure. The horizontal position 128 is measured along a first axis parallel to the reference plane and defines the lateral displacement of the anomaly within the digital representation. The vertical position 130 is measured along a second axis perpendicular to the reference plane and defines the relative height or depth of the anomaly within the digital representation. The longitudinal position 132 is measured along a third axis extending along the length of the digital representation and intersecting the reference plane and defines the location of the anomaly along the length of the digital representation 120 of the structure.

[0061] In other examples, multiple anomalies 114, or at least some of multiple anomalies 114, may be identified by means other than the three-point system. For example, anomalies may be assigned based on predetermined structural criteria, such as identified components (e.g., special panels, fasteners, or structural joints), without requiring explicit spatial coordinates. Alternatively, anomalies may be categorized by functional zones, in which case the anomalies are associated with operating systems rather than physical locations, such as anomalies related to pressurized systems, avionics, or hydraulic systems within an aircraft. In some cases, anomalies may be assigned across the entire digital representation of a structure, such as when an anomaly relates to an overall problem affecting multiple areas (e.g., material degradation, environmental exposure effects, or overall manufacturing inconsistencies). In such cases, the anomalies are dynamically associated with one of each of multiple zones 124 to ensure comprehensive inspection coverage and anomaly tracking.

[0062] The anomaly association module 110 is configured to dynamically associate each of multiple anomalies with a corresponding zone in multiple zones. That is, while the mapping module 108 determines the fixed location of each anomaly in the digital representation of the structure, the anomaly association module 110 ensures that each anomaly remains associated with the correct zone when zone boundaries are adjusted or subdivided in the digital representation. In particular, the anomaly associations within zones are automatically updated whenever multiple zones are adjusted. Because zones are dynamically adjustable, anomalies initially assigned to one zone may be reassigned to different zones or subzones as zone boundaries shift, subzones are introduced, or inspection priorities evolve. However, the physical location of the anomalies in the digital representation remains unchanged. Only the zone assignment is updated to reflect the most recent zoning configuration. This ensures that anomaly tracking, inspection planning, and corrective actions remain consistent with the latest zone definitions.

[0063] In some examples, the anomaly association module 110 is configured to update the digital representation 120 of the structure in real time. The real-time nature of the digital representation helps ensure that anomaly associations are continuously updated when new anomalies are identified, when changes in anomaly status occur, or when adjustments are made to multiple zones. Figure 5 shows one example of how the digital representation is visually rendered to display multiple anomalies 114 by anomaly density per zone. Thus, the anomaly association module 110 dynamically associates each of the multiple anomalies 114 with a corresponding zone in the multiple zones 124. The anomaly distribution is visually represented, and the multiple anomalies 114 are mapped to specific ones among the multiple zones 124 or multiple subzones 126 in the digital representation 120 of the structure. Anomalies associated with each zone are grouped along a horizontal axis according to their respective zones or subzones, and vertical bars indicate the amount of anomalies present in each zone. The visualization allows for a rapid assessment of anomaly density across different zones. Because the digital representation is updated in real time, changes in anomaly data are dynamically reflected to ensure that the device maintains an accurate current representation of multiple anomalies. That is, the digital representation is dynamically updated in response to changes in anomalies, the identification of new anomalies, and zone adjustments. In other examples, anomaly data within the digital representation may be displayed in various ways, including tabular formats, heatmaps, or other data visualization techniques.

[0064] Referring back to Figure 1, the device 100 further includes an inspection planning module 112, which is configured to automatically execute inspection plans associated with each of several zones and generate corresponding instructions for corrective actions. The inspection planning module 112 helps ensure that each anomaly within a given zone is evaluated according to appropriate inspection criteria. Thus, the inspection planning module 112 facilitates systematic anomaly management by ensuring that inspection procedures are consistently applied based on zone-specific plans. In some examples, multiple inspection plans 134 may be dynamically updated to reflect adjustments to zone boundaries, newly identified anomalies, or changes in anomaly status.

[0065] As shown in Figure 6, each of the multiple zones 124 and multiple subzones 126 within the digital representation 120 of the structure is associated with a corresponding inspection plan 134 that outlines the specific inspection requirements and procedures for that zone. In this example, an organized tabular format is represented in which each inspection plan is associated with a corresponding zone or subzone, and further lists the anomalies 114 contained within each respective zone. For example, the first subzone inspection plan 134A-1 is associated with the first subzone 126A and the corresponding multiple anomalies 114, and the second subzone inspection plan 134A-2 is associated with the second subzone 126B and the corresponding multiple anomalies 114. The inspection plan module 112 can use this structured data to ensure that all anomalies associated with zones within the digital representation 120 of the structure are evaluated according to the inspection plan.

[0066] When the inspection plan is executed, the inspection plan module 112 generates instructions to guide corrective actions for each of a group of anomalies. Instructions associated with each corresponding anomaly are generated according to the inspection plan for the zone associated with the corresponding anomaly of the group. The inspection plan may define general inspection procedures applicable to all areas of the zone, as well as more detailed steps for evaluating specific anomalies. For example, instructions may specify repair procedures, additional testing requirements, or follow-up inspections to verify anomaly resolution. The generated instructions serve as actionable guidance for corrective actions configured to be performed on the physical structure. The instructions help ensure that the identification, evaluation, and resolution of anomalies remain systematically aligned between the digital representation and the physical implementation of corrective actions. The device 100 operates as part of a broader non-conformity anomaly management system 200 that integrates anomaly management capabilities across the actual structure.

[0067] Referring to Figure 7, a nonconformity anomaly management system 200 (hereinafter referred to as the "System") is provided for monitoring, evaluating, and addressing anomalies within a structure 202 (i.e., a physical structure). As shown in the figure, the structure 202 is a body 206. The structure 202 contains a plurality of anomalies 204, each of which requires at least one corrective action. The System 200 further includes a device 100 configured to generate a digital representation of the structure and to utilize the digital representation for anomaly management. Specifically, the device 100 is configured to define a plurality of zones, map the plurality of anomalies within the digital representation of the structure, dynamically adjust the size of the zones based on operating requirements, map the plurality of anomalies within the digital representation, dynamically associate the plurality of anomalies with corresponding zones among the plurality of zones, and automatically execute inspection plans associated with the corresponding zones. The digital representation allows for real-time updates when new anomalies are identified, anomaly status changes, or zones are adjusted.

[0068] At least one corrective action is performed on structure 202 for each of the multiple anomalies 204 of structure 202, based on instructions generated by device 100. The corrective action is determined according to an inspection plan associated with the corresponding zone in the digital representation of the structure. The corrective action may include, but is not limited to, repair procedures, rework guidelines, additional diagnostic tests, or follow-up inspections. The corrective action is then performed on structure 202 to ensure the resolution of each of the multiple anomalies.

[0069] System 200 may also be configured to interface with an external work order management system to facilitate a structured anomaly resolution workflow. In some examples, once an inspection plan has been executed and corrective actions have been determined, System 200 can provide anomaly resolution data that can generate a corresponding work order or integrate with the external work order system. This can enable end-to-end traceability from anomaly identification to resolution, ensuring that corrective actions are documented, assigned, and efficiently tracked.

[0070] In some cases, digital representations enable real-time tracking of corrective actions to ensure that the status of each anomaly is continuously updated as corrective actions are implemented. This allows for a comprehensive anomaly management workflow in which corrective actions are systematically implemented, monitored, and verified within their respective zones or subzones.

[0071] Referring to Figure 8, one example illustrates a method 300 for managing nonconforming anomalies in structure 202. Method 300 includes a step (block 302) of identifying multiple anomalies in the structure using an anomaly identification system. The anomaly identification system may include an automated inspection tool or manual inspection data. Method 300 also includes a step (block 304) of mapping each of the multiple anomalies to a corresponding location in a digital representation of the structure. The mapping process assigns a spatial reference to each anomaly using a coordinate-based reference, a structural identifier, or a predetermined reference point in the digital representation. For example, an anomaly may be mapped to a reference plane using three points (e.g., horizontal, vertical, and longitudinal coordinates).

[0072] Method 300 further includes the step (block 306) of defining multiple zones within a digital representation of the structure. The size of each of the multiple zones is dynamically adjustable and associated with corresponding inspection plans of multiple different inspection plans. The zones may be defined based on structural divisions, functional roles, or stages or assemblies to facilitate targeted inspection and efficient anomaly management. Method 300 further includes the step (block 308) of dynamically adjusting at least one of the multiple zones within the digital representation of the structure based on operational requirements. Zone adjustments may occur due to inspection priorities, anomaly distribution, accessibility constraints, or changes in structural configuration. The ability to dynamically adjust zones helps ensure that anomaly assessment remains consistent with real-time operational requirements.

[0073] Method 300 also includes a step (block 310) of dynamically associating each of several anomalies with corresponding zones in a digital representation of the structure. This ensures that anomalies are continuously tracked within the corresponding zones, even if the zone boundaries are changed. Method 300 further includes a step (block 312) of automatically executing an inspection plan associated with the corresponding zones in the multiple zones. Executing the inspection plan involves applying predetermined inspection procedures, criteria, and evaluation techniques to assess anomalies within each zone. The inspection plan may define general inspection steps that apply to all areas of a zone, as well as specific steps tailored to particular anomalies.

[0074] Additionally, method 300 includes a step (block 314) of performing at least one corrective action for each of several anomalies in the structure, in accordance with an inspection plan for the zones associated with the corresponding anomalies of the several anomalies. The corrective action may include repair, replacement of components, structural reinforcement, additional testing, or reinspection. In some examples, once the corrective action is performed, the digital representation is updated in real time to reflect the status of the anomaly.

[0075] In some examples, Method 300 further includes the step of subdividing at least one of several zones in the digital representation of the structure into several subzones. Each subzone is dynamically adjustable and associated with a corresponding inspection plan that defines inspection criteria. Additionally or alternatively, in some examples, Method 300 includes the step of defining segments in the digital representation of the structure, each segment corresponding to a given part of the digital representation. Segmentation facilitates the definition of zones within distinct parts of the structure by enabling the structural organization of the digital representation.

[0076] Additionally, in some examples, Method 300 includes a step of generating a digital representation of the structure in real time based on at least one of newly identified anomalies, changes in anomaly status, or adjustments to multiple zones. Real-time updates ensure that anomaly management operations remain up-to-date by reflecting ongoing corrections to multiple zones, anomaly statuses, and corrective actions.

[0077] In the above description, specific terms such as “up,” “down,” “upper side,” “lower side,” “horizontal,” “vertical,” “left,” “right,” “upward,” and “downward” may be used. Such terms are used to clarify the description when dealing with relative relationships where applicable. However, these terms are not intended to imply absolute relationships, positions, and / or orientations. For example, the “upper side” of an object can become the “lower side” by simply inverting that object. Nevertheless, this is still the same object. Furthermore, the terms “include,” “equip,” and “possess,” and their variations, mean “include, but not limited to,” unless otherwise explicitly specified. An enumerated list of items does not mean that any or all of the items are mutually exclusive and / or mutually inclusive, unless otherwise explicitly specified. The terms “a,” “an,” and “the” also mean “one or more,” unless otherwise explicitly specified. Furthermore, the term “plural” can be defined as “at least two.” Furthermore, unless otherwise specified, as defined in this disclosure, certain features do not necessarily represent all of the features of the entire set or class of features.

[0078] The terms “approximately” or “substantially” are defined in some embodiments as meaning within + / - 5% of a given value, but in additional embodiments, any disclosure of “approximately” may be further narrowed and asserted to mean within + / - 4% of a given value, within + / - 3% of a given value, within + / - 2% of a given value, within + / - 1% of a given value, or the exact given value. Furthermore, if at least two values ​​of a variable are disclosed, such disclosure is specifically intended to include a range between the two values, whether they are disclosed in relation to separate embodiments or examples, and specifically intended to include a range less than or equal to at least the smaller of the two values ​​and / or the larger of the two values. Additionally, if at least three values ​​of a variable are disclosed, such disclosure is specifically intended to include a range between any two of the values, whether they are disclosed in relation to separate embodiments or examples, and specifically intended to include a range less than or equal to at least value A and / or value B, where A may be any value of the disclosed values ​​other than the disclosed maximum value, and B may be any value of the disclosed values ​​other than the disclosed minimum value.

[0079] Additionally, the instances herein in which one element is “joined” to another element may include direct and indirect joins. A direct join can be defined as one element being joined to another element and having some kind of contact. An indirect join can be defined as a join between two elements that do not directly touch each other but have one or more additional elements between the joined elements. Furthermore, as used herein, fixing one element to another element may include direct and indirect fixings. Additionally, as used herein, “adjacent” does not necessarily indicate contact. For example, one element may be adjacent to another element without touching it.

[0080] As used herein, the phrase “at least one of” means, when used with a list of items, that one or more different combinations of the listed items may be used, and only one of the items in the list may be required. An item may be a specific object, thing, or category. In other words, “at least one of” means that any combination or any number of items may be used from the list, but not all of the items in the list may be required. For example, “at least one of item A, item B, and item C” may mean item A, item A and item B, item B, item A, item B, and item C, or item B and item C. In some cases, “at least one of item A, item B, and item C” may mean, for example, two item A, one item B, and ten item C, four item B and seven item C, or any other suitable combination, for example, but not limited to these.

[0081] Unless otherwise indicated, terms such as “first,” “second,” etc., are used herein solely as labels and are not intended to impose any order, position, or hierarchical requirements on the items they refer to. Furthermore, a reference to, for example, an item “second,” does not require or exclude the presence of, for example, an item “first” or a lower-numbered item, and / or, for example, an item “third” or a higher-numbered item.

[0082] As used herein, a system, apparatus, structure, article, element, component, or hardware “configured to perform” a specified function is not merely capable of performing the specified function after further modification, but can actually perform the specified function without any modification. In other words, a system, apparatus, structure, article, element, component, or hardware “configured to perform” a specified function is specifically selected, created, implemented, utilized, programmed, and / or designed to perform the specified function. As used herein, “configured to” refers to an existing characteristic of the system, apparatus, structure, article, element, component, or hardware that enables it to perform the specified function without further modification. For the purposes of this disclosure, a system, apparatus, structure, article, element, component, or hardware described as “configured to perform” a particular function may additionally or alternatively be described as “adapted to” and / or “operating to” perform that function.

[0083] The schematic flowcharts included herein are generally described as logical flowcharts. Therefore, the illustrated order and labeled steps represent one example of the presented method. Other steps and methods may be considered equivalent in function, logic, or effect to one or more steps, or parts thereof, of the illustrated method. Additionally, the formatting and symbols used are provided to illustrate the logical steps of the method and are not intended to limit the scope of the method. Various arrow and line types may be used in the flowcharts, but these are not intended to limit the scope of the corresponding method. In fact, some arrows or other connectors may be used to indicate only the logical flow of the method. For example, arrows may indicate unspecified duration wait or monitoring periods between enumerated steps of the illustrated method. Additionally, the order in which a particular method is performed may or may not strictly adhere to the order of the corresponding steps shown.

[0084] Many of the functional units described herein are labeled as modules to more specifically emphasize the independence of their implementation forms. For example, a module may be implemented as a hardware circuit including a custom VLSI circuit or off-the-shelf semiconductor such as a gate array, logic chip, transistor, or other individual components. A module may also be implemented as a programmable hardware device such as a field-programmable gate array, programmable array logic, or programmable logic device.

[0085] Modules may also be implemented in code and / or software for execution by various types of processors. A module of specified code may include one or more physical or logical blocks of executable code, which may be organized, for example, as objects, procedures, or functions. Nevertheless, the executable code of a specified module does not need to be physically located together and may include separate instructions stored in different locations that, when logically combined, accomplish the purpose of the module that is expressed and includes the module.

[0086] In fact, a module of code may be a single instruction or many instructions, and may be distributed across several different code segments, different programs, and several memory devices. Similarly, operational data may be identified and illustrated within a module herein, embodied in any suitable form, and organized within any suitable type of data structure. Operational data may be collected as a single dataset or distributed across different locations, including different computer-readable storage devices. If a module or part of a module is implemented in software, the software portion is stored on one or more computer-readable storage devices.

[0087] Any combination of one or more computer-readable media may be used. The computer-readable media may be a computer-readable storage medium. The computer-readable storage medium may be a storage device that stores code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination thereof.

[0088] More specific examples of storage devices (a non-exclusive list) include electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In the context of this specification, a computer-readable storage medium may be any tangible medium that can contain or store programs for use by or in connection with an instruction execution system, apparatus, or device.

[0089] The code for performing the actions for the example may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Python, Ruby, Java, Smalltalk, and C++, and traditional procedural programming languages ​​such as the "C" programming language, and / or machine languages ​​such as assembly language. The code may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or this connection may be to an external computer (for example, via the internet using an Internet service provider).

[0090] The described features, structures, or characteristics of the examples may be combined in any suitable manner. The above description provides numerous specific details, including examples of programming, software modules, user selection, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., in order to provide a complete understanding of the examples. However, those skilled in the art will recognize that the examples may be implemented without using one or more of the specific details, or using other methods, components, materials, etc. In other cases, well-known structures, materials, or operations are not shown or described in detail, in order to avoid obscuring aspects of the examples.

[0091] Examples of the embodiments are described above with reference to schematic flowcharts and / or schematic block diagrams of the exemplary methods, apparatus, systems, and program products. It will be understood that each block in the schematic flowcharts and / or schematic block diagrams, as well as combinations of blocks in the schematic flowcharts and / or schematic block diagrams, can be implemented by code. These codes may be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing device for manufacturing a machine, and as a result, instructions executed via the processor of the computer or other programmable data processing device create means for performing the functions / operations specified in the schematic flowcharts and / or schematic block diagrams or any combination of blocks.

[0092] The code may also be stored in a storage device that can instruct a computer, other programmable data processing device, or other device to function in a particular manner, and as a result, the instructions stored in the storage device produce a product containing instructions that perform functions / operations specified in a schematic flowchart and / or schematic block diagram or a set of blocks.

[0093] The code may also be loaded onto a computer, other programmable data processing device, or other device to perform a series of operational steps on the computer, other programmable device, or other device, thereby generating a computer implementation process, the resulting code running on the computer or other programmable device providing a process for performing functions / operations specified by blocks or combinations of blocks in a flowchart and / or block diagram.

[0094] The schematic flowcharts and / or schematic block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, systems, methods, and program products by various examples. In this regard, each block in the schematic flowcharts and / or schematic block diagrams may represent a module, segment, or portion of code containing one or more executable instructions of code for performing a specified logical function.

[0095] The subject matter of the present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The examples described should be considered in all respects only as illustrative and not restrictive. All modifications within the meaning and equivalence of the claims shall be included within that scope. [Explanation of symbols]

[0096] 100 devices 102 processors 104 memory 105 Anomaly Management Interface 106 Zone Definition Module 108 Mapping Modules 110 Anomaly Association Module 112 Inspection Planning Module 114 Abnormality 114A Abnormal 120 Digital Expressions 122 segments 122A First segment 122B Second segment 122C Third segment 122D Fourth segment 122E Fifth segment 122N The Nth segment 124 Zones 124A Zone 1 124B Second Zone 124C Third Zone 124D Zone 4 124E Zone 5 124F Zone 6 124G Zone 7 124H Zone 8 126 Subzones 126A First subzone 126B Second Subzone 126C Third Subzone 126D Fourth subzone 128 horizontal position 130 vertical position 132 Longitudinal position 134 Inspection Plan 134A First Inspection Plan 134B Second Inspection Plan 134C Third Inspection Plan 134D Fourth Examination Plan 134E Fifth Inspection Plan 134F 6th Inspection Plan 134G 7th Inspection Plan 134H 8th Inspection Plan 200 Non-conformity and Anomaly Management System 202 Structure 204 Abnormal 206 Fuselage 300 ways

Claims

1. A device (100) for managing non-conformities within a structure, wherein the device (100) Processor (102), A memory (104) for storing code, wherein the code is stored by the processor (102), Multiple zones (124) are defined within the digital representation (120) of the aforementioned structure, where, The size of each of the aforementioned multiple zones (124) is dynamically adjustable. Each of the aforementioned multiple zones (124) is associated with a corresponding inspection plan of a plurality of different inspection plans (134). The size of at least one of the plurality of zones (124) is dynamically adjusted based on the operating requirements of the digital representation (120) of the structure. Multiple anomalies (114) identified by the anomaly identification system are mapped to corresponding locations within the digital representation (120) of the structure. Each of the multiple anomalies (114) is dynamically associated with the corresponding zone of the multiple zones (124) in the digital representation (120) of the structure. The inspection plan (134) associated with one of the multiple zones (124) is automatically executed. An instruction is generated to guide at least one corrective action for each of the plurality of anomalies (114), wherein the instruction associated with each corresponding of the plurality of anomalies (114) is generated in accordance with the inspection plan (134) of the zone (124) associated with the corresponding anomaly of the plurality of anomalies (114). Executable in such a way, memory and A device equipped with the following features.

2. The memory (104) further stores the code, and the code is processed by the processor (102). At least one of the plurality of zones (124) is subdivided into a plurality of subzones (126) within the digital representation (120) of the structure, where, The size of each of the aforementioned subzones (126) is dynamically adjustable. Each of the aforementioned subzones (126) is associated with a corresponding inspection plan of a plurality of different inspection plans (134). At least one of the plurality of anomalies (114) is dynamically associated with the corresponding subzone of the plurality of subzones (126) in the digital representation (120) of the structure, The inspection plan (134) associated with one of the subzones (126) is automatically executed. The system generates an instruction to guide at least one corrective action for each of the associated anomalies (114) within the plurality of subzones (126), wherein the instruction associated with each corresponding anomaly (114) is generated in accordance with the inspection plan (134) of the subzone (126) associated with the corresponding anomaly (114). The apparatus (100) according to claim 1, which is executable in this manner.

3. The memory (104) further stores the code, and the code is processed by the processor (102). A plurality of segments (122) are defined within the digital representation (120) of the structure, where, Each of the plurality of segments (122) represents a predetermined portion of the digital representation (120), The plurality of segments (122) are configured to organize the digital representation (120) of the structure into identifiable areas for managing the plurality of anomalies (114). Define at least one of the multiple zones (124) within the corresponding segment of the multiple segments (122) in the digital representation (120) of the structure. The apparatus (100) according to claim 1, which is executable in this manner.

4. Each of the multiple anomalies (114) identified by the anomaly identification system is identified by three points in the digital representation (120) of the structure, and the three points are defined with respect to a reference plane. A horizontal position (128) measured along a first axis parallel to the aforementioned reference plane, The vertical position (130) measured along a second axis perpendicular to the aforementioned reference plane, A longitudinal position (132) measured along a third axis that extends along the length of the digital representation (120) of the structure and intersects the reference plane, and The apparatus (100) according to claim 1, including the apparatus (100).

5. The apparatus (100) according to claim 4, wherein each of the three points corresponding to the plurality of anomalies (114) is used to dynamically associate each of the plurality of anomalies (114) with the corresponding zone of the plurality of zones (124) in the digital representation (120) of the structure.

6. The apparatus (100) according to claim 1, wherein the step of defining a plurality of zones (124) within the digital representation (120) of the structure includes automatically defining one of the plurality of zones (124) based on the operating requirements of the digital representation (120) of the structure, and automatically associating the inspection plan (134) corresponding to one of the plurality of zones (124).

7. The apparatus (100) according to claim 1, wherein the step of defining a plurality of zones (124) within the digital representation (120) of the structure includes enabling a user to manually define boundaries for each of the plurality of zones (124) and manually associating the inspection plan (134) corresponding to each of the plurality of zones (124).

8. An anomaly characteristic is determined for each of the multiple anomalies (114) identified by the anomaly identification system, and the anomaly characteristic is, Each of the aforementioned multiple abnormalities, Each of the aforementioned multiple abnormalities is of one type, The size of each of the aforementioned multiple anomalies, Each of the aforementioned multiple anomalies has a severity level, and Each of the aforementioned multiple anomalies is a status The apparatus (100) according to claim 1, comprising at least one of the following.

9. The apparatus (100) according to claim 1, wherein the digital representation (120) of the structure is updated in real time based on at least one of a newly identified anomaly, a change in the anomaly status of at least one of the plurality of anomalies (114), or adjustments to the plurality of zones (124).

10. Non-conformity abnormality management system (200), A structure (202) having multiple anomalies (204), wherein each of the multiple anomalies (204) requires at least one corrective action, Apparatus (100), Processor (102), A memory (104) for storing code, wherein the code is stored by the processor (102), Multiple zones (124) are defined within the digital representation (120) of the aforementioned structure, where, The size of each of the aforementioned multiple zones (124) is dynamically adjustable. Each of the aforementioned multiple zones (124) is associated with a corresponding inspection plan of a plurality of different inspection plans (134). The size of at least one of the plurality of zones (124) is dynamically adjusted based on the operating requirements of the digital representation (120) of the structure. Multiple anomalies (114) identified by the anomaly identification system are mapped to corresponding locations in the digital representation (120) of the structure. Each of the multiple anomalies (114) is dynamically associated with the corresponding zone of the multiple zones (124) in the digital representation (120) of the structure. The inspection plan (134) associated with one of the multiple zones (124) is automatically executed. An instruction is generated to guide at least one corrective action for each of the plurality of anomalies (114), wherein the instruction associated with each corresponding of the plurality of anomalies (114) is generated in accordance with the inspection plan (134) of the zone (124) associated with the corresponding anomaly of the plurality of anomalies (114). Executable in such a way, memory and A device equipped with Equipped with, A nonconformity anomaly management system wherein the at least one corrective action is performed for each of the plurality of anomalies (204) of the structure (202) based on the instruction generated by the device (100) derived from the digital representation (120) of the structure.

11. The memory (104) further stores the code, and the code is processed by the processor (102). At least one of the aforementioned multiple zones is subdivided into multiple subzones within the digital representation (120) of the structure, where, The size of each of the aforementioned subzones is dynamically adjustable. Each of the aforementioned subzones is associated with a corresponding inspection plan of several different inspection plans. At least one of the aforementioned anomalies is dynamically associated with the corresponding subzone of the aforementioned subzones in the digital representation (120) of the structure, The inspection plan associated with the subzone among the plurality of subzones is automatically executed. Generates instructions to guide at least one corrective action for each of the multiple anomalies associated within the multiple subzones, wherein the instructions associated with each corresponding one of the multiple anomalies are generated in accordance with the inspection plan of the subzone associated with the corresponding anomaly of the multiple anomalies. A non-conformity anomaly management system (200) according to claim 10, which is executable in such a manner.

12. The memory (104) further stores the code, and the code is processed by the processor (102). Multiple segments are defined within the digital representation (120) of the aforementioned structure, where, Each of the plurality of segments corresponds to a predetermined portion within the digital representation (120) of the structure, The plurality of segments are configured to organize the digital representation (120) of the structure into identifiable areas for managing the plurality of anomalies. Define at least one of the plurality of zones within the corresponding segment of the plurality of segments in the digital representation (120) of the structure. A non-conformity anomaly management system (200) according to claim 10, which is executable in such a manner.

13. Each of the multiple anomalies identified by the anomaly identification system is identified by three points in the digital representation (120) of the structure, and the three points are defined in the digital representation with respect to a reference plane. A horizontal position measured along a first axis parallel to the aforementioned reference plane, The vertical position measured along a second axis perpendicular to the aforementioned reference plane, A longitudinal position measured along a third axis that extends along the length of the structure and intersects the reference plane, and A non-conformity abnormality management system (200) according to claim 10, including the above.

14. An anomaly characteristic is determined for each of the multiple anomalies identified by the anomaly identification system, and the anomaly characteristic is, Each of the aforementioned multiple abnormalities, Each of the aforementioned multiple abnormalities is of one type, The size of each of the aforementioned multiple anomalies, Each of the aforementioned multiple anomalies has a severity level, and Each of the aforementioned multiple anomalies is a status A nonconformity anomaly management system (200) according to claim 10, comprising at least one of the following.

15. The non-conforming anomaly management system (200) according to claim 10, wherein the digital representation (120) of the structure is updated in real time based on at least one of newly identified anomalies, a change in the anomaly status of at least one of the plurality of anomalies (114), or adjustments to the plurality of zones (124).

16. A method (300) for managing non-conformities within a structure (202), wherein the method (300) is: Step (302) of identifying multiple anomalies within the structure (202) using an anomaly identification system, The steps include: (304) mapping each of the aforementioned anomalies to a corresponding location in the digital representation (120) of the structure; A step (306) of defining a plurality of zones within the digital representation (120) of the structure, wherein the size of each of the plurality of zones is dynamically adjustable and associated with a corresponding inspection plan of a plurality of different inspection plans, Step (308) of dynamically adjusting at least one of the multiple zones in the digital representation (120) of the structure based on the operating requirements of the structure (202), Step (310) of dynamically associating each of the plurality of anomalies with the corresponding zone of the plurality of zones in the digital representation (120) of the structure, Step (312) to automatically execute the inspection plan associated with the corresponding zone of the plurality of zones, Step (314) of performing at least one corrective action for each of the multiple anomalies in the structure (202) in accordance with the inspection plan for the zone associated with the corresponding anomaly of the multiple anomalies. Methods that include...

17. The method (300) of claim 16, further comprising the step of subdividing at least one of the plurality of zones in the digital representation (120) of the structure into a plurality of subzones, wherein the size of each of the plurality of subzones is dynamically adjustable, and each of the plurality of subzones is associated with a corresponding inspection plan of a plurality of inspection plans.

18. A step of defining the digital representation (120) of the structure into a plurality of segments, wherein each of the plurality of segments corresponds to a predetermined portion of the digital representation of the structure (202); The steps include defining at least one of the plurality of zones within the corresponding segments of the plurality of segments in the digital representation (120) of the structure, The method according to claim 16, further comprising (300).

19. The method according to claim 16 (300), further comprising the step of determining three locations in the digital representation (120) of the structure for each of the plurality of anomalies with respect to a reference plane, wherein the three locations include a horizontal position, a vertical position and a longitudinal position.

20. The method according to claim 16 (300), further comprising the step of generating the digital representation (120) of the structure in real time based on at least one of newly identified anomalies, changes in the anomaly status of at least one of the plurality of anomalies, or adjustments of the plurality of zones (124).

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