System and method for predicting solutions to nonconformities in vehicle components during maintenance operation

Abstract

To provide a system capable of predicting the probability of corrosion-related defects found during aircraft depot-level heavy maintenance, a method, and a non-transitory computer-readable storage medium.SOLUTION: A nonconformity prediction system (200) includes a control unit (252). The control unit (252) is configured to verify historical work records (202, 204) related to a vehicle model, calculate the probability of one or more nonconformities (210) occurring during vehicle induction from the historical work records (202, 204), the induction being an overall record of a depot-level heavy maintenance visit, and predict labor hours (213, 218, 239) required to resolve the nonconformities (210). As predicted by the control unit (252), an electrical signal containing information about the labor hours (213, 218, 239) is output to a user interface display (228).SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] TECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to systems and methods for predicting non-conformance solutions for the maintenance of vehicles, such as aircraft. [Background technology]

[0002]

[0002] Aircraft are used to transport passengers and cargo between various locations. Numerous aircraft depart and arrive at a typical airport each day.

[0003]

[0003] Aircraft maintenance involves performing various maintenance activities to ensure the continued desired operation of the aircraft and / or aircraft components. Maintenance activities may include inspection, replacement, rework of component inconsistencies, or other activities to maintain compliance with airworthiness instructions and maintenance standards.

[0004]

[0004] Aircraft maintenance is often performed periodically. Certain scientific reliability models focus on predicting in-service reliability failures by estimating the life span between discrete events, such as component failures. The models statistically predict failures as a function of, for example, flight hours or number of landings. Unlike failures, nonconformities represent degradation states that do not result in detectable failures, which may occur later in service. Nonconformities are typically discovered during inspections, whether specially scheduled inspections or inspections accompanying maintenance of related systems.

[0005] Additionally, various aircraft components may be susceptible to corrosion. Corrosion findings, unlike failures, also represent a state of degradation that does not result in a detectable failure. Maintenance personnel may estimate a rough estimate of the time it will take to repair a corrosion-related defect. However, without the context of the activity creating the nonconformity, the labor hours (i.e., work hours, time performed during work, man-hours, etc.) required to repair a corrosion-related nonconformity are typically unknown and therefore difficult to predict usefully. It should be understood that, as used herein, labor hours, work hours, time performed during work, man-hours, etc. are equivalent terms.

[0006]

[0006] Because a single depot-level heavy maintenance visit for an aircraft can span weeks or months, and multiple aircraft are deployed at any given time on staggered schedules, the discovery of an unexpected corrosion nonconformity can extend maintenance time and labor and delay the aircraft's return to service. Maintenance personnel are generally prepared to support scheduled maintenance. However, failures resulting from nonconformities, which are a form of reliability lack discovered during depot-level maintenance, can stress maintenance personnel and lead to significantly increased maintenance time and reduced fleet availability. Summary of the Invention

[0007] What is needed is a system and method for efficiently and effectively scheduling aircraft maintenance, including parts and labor. Additionally, what is needed is a system and method for predicting and forecasting corrosion-related non-conformances on aircraft during maintenance operations.

[0008] With these needs in mind, certain embodiments of the present disclosure provide a system that includes one or more control units configured to: collate historical work records for a vehicle model; calculate from the historical work records a probability of one or more nonconformances during an induction for the vehicle, the induction being a global record of heavy maintenance depot visits; predict labor hours to resolve the one or more nonconformances; and output an electrical signal to a user interface display that includes information regarding the labor hours as predicted by the one or more control units.

[0009]

[0009] In at least one embodiment, the historical work record specifies one or more work tasks, nonconformities discovered during the one or more work tasks, any replacement parts required for the one or more work tasks, and one or more past work hours required to resolve the one or more nonconformities.

[0010] In at least one embodiment, the system also includes a user interface display, and the one or more control units are further configured to: display the work hours as predicted by the one or more control units on the user interface display.

[0011] In at least one embodiment, the one or more control units are further configured to determine a probability that the guide has a given work task and a probability that the guide has one or more corrosion incompatibilities. In a further embodiment, the one or more control units are configured to determine a probability of having one or more incompatibilities for the given work task from the probability that the guide has the given work task and the probability that the guide has one or more corrosion incompatibilities.

[0012] In at least one embodiment, the one or more control units are further configured to determine a probability that the disposition code is for a given work task.

[0013]

[0013] In at least one embodiment, the one or more control units are further configured to perform the following: determine a probability that one or more non-conformances have a given work task requiring a part.

[0014]

[0014] In at least one embodiment, the control unit is configured to perform the predicting of working hours, at least in part, by determining an average working hour as the sum of man-hours for a given work task involving corrosion divided by the historical count of the given work task.

[0015]

[0015] In at least one embodiment, the control unit is configured to perform the predicting of working hours, at least in part, by determining an overall value for all work tasks by multiplying the probability that the work task has a corrosion non-conformity by the average working hours required for each work task.

[0016]

[0016] The one or more control units are further configured to: automatically control the one or more maintenance devices to perform one or more maintenance tasks during working hours.

[0017]

[0017] One or more control units may be or otherwise include an artificial intelligence or machine learning system.

[0018] Certain embodiments of the present disclosure provide a method that includes: collating, by one or more control units, a historical work record for a vehicle model; calculating, by the one or more control units, a probability of one or more non-conformances during a vehicle guidance from the historical work record, the guidance being a global record of heavy supply station visits; predicting, by the one or more control units, a labor time for resolving the one or more non-conformances; outputting, by the one or more control units, an electrical signal including information regarding the labor time as predicted by the one or more control units to a user interface display; and displaying, by the one or more control units, the labor time as predicted by the one or more control units on the user interface display. [Brief explanation of the drawings]

[0019] [Figure 1]

[0019] A schematic diagram of a data processing system according to one embodiment of the present disclosure is shown. [Figure 2]

[0020] 1 illustrates a block diagram of a mismatch prediction system according to one embodiment of the present disclosure. [Figure 3]

[0021] 1 shows a diagram of replacement part forecasting according to the prior art; [Figure 4]

[0022] 1 shows a diagram of mismatch-based prediction according to one embodiment of the present disclosure. [Figure 5]

[0023] 1 shows a flowchart of a method according to one embodiment of the present disclosure. [Figure 6]

[0024] 1 illustrates a block diagram of a data processing system according to one embodiment of the present disclosure. [Figure 7]

[0025] FIG. 1 is an illustration of an aircraft manufacturing and service method in accordance with an illustrative embodiment. [Figure 8]

[0026] FIG. 1 illustrates a block diagram of an aircraft in which embodiments of the present disclosure may be implemented. [Figure 9]

[0027] 1 illustrates a block diagram of a production control system according to one embodiment of the present disclosure. [Figure 10]

[0028] 1 shows a decision tree diagram illustrating how a method for predicting incompatibility can be applied to standard supply depot guidance, according to one embodiment of the present disclosure. [Figure 11]

[0029] FIG. 2 shows a schematic block diagram of a control unit according to one embodiment of the present disclosure. [Figure 12]

[0030] 1 illustrates a perspective front view of an aircraft according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0020]

[0031] The foregoing summary, as well as the following detailed description of specific embodiments, will be better understood when read in conjunction with the accompanying drawings. As used herein, the use of the singular form "a" or "an" preceding an element or step does not necessarily exclude a plurality of such elements or steps. Furthermore, references to "one embodiment" are not intended to be interpreted as excluding the existence of additional embodiments that incorporate features described herein. Furthermore, embodiments that "comprising" or "having" one or more elements having certain conditions may include additional elements that do not have those conditions (unless expressly stated otherwise).

[0021]

[0032] Embodiments of the present disclosure provide systems and methods for predicting the probability of corrosion-related defects discovered during depot-level heavy maintenance of aircraft, organized by (1) the inspection activities likely to reveal nonconformities, and (2) the man-hours required to resolve a given corrosion defect. The systems and methods address the need to predict this particular form of reliability failure and allow maintenance personnel to predict the man-hours required due to the reliability failure. This allows for better prediction of maintenance needs and planning of heavy maintenance accordingly.

[0022]

[0033] In at least one embodiment, the systems and methods described herein associate the required labor hours (i.e., man-hours) and disposition code for the initiating nonconformance with the work order record that produced the discovery and the specific work package (supply depot guidance) with the aircraft under consideration. The systems and methods utilize statistical modeling configured to predict the probability and amount of nonconformance likely to occur at any given stage and location of maintenance, as well as the probability of corrosion and the labor hours likely required to correct such nonconformance.

[0023]

[0034] In at least one embodiment, the system and method provide visual tools that allow the user to: (a) navigate the probability and required labor hours of any upcoming scheduled inductions, (b) update the risk of ongoing inductions (i.e., model that the risk has passed or has not yet appeared), and (c) consider where high-risk maintenance may be required in terms of labor hours.

[0024]

[0035] U.S. Patent Application Publication No. 2024 / 0046176, entitled "Heavy Maintenance Non-Conformance Forecasting," discloses a system and method for predicting non-conformance of vehicle parts, and is incorporated herein by reference in its entirety.

[0025]

[0036] Embodiments of the present disclosure recognize and take into account that a nonconformance is not a clearly defined or discrete failure condition. Instead, a nonconformance is a degrading, but not overt, deviation in condition or performance that is typically discovered during inspections, such as depot-level inspections or other maintenance procedures for an aircraft. Because heavy depot maintenance occurs at long intervals (e.g., every 5-6 years), nonconformances can occur in both highly reliable components (which tend to have little data to estimate lifespan) and rotatable components (which tend to be numerous per aircraft or other vehicle).

[0026]

[0037] Nonconformance requires mediation because the nonconforming part no longer meets the tolerance design specifications. In contrast, a deteriorated part is not necessarily nonconforming and does not necessarily require mediation.

[0027]

[0038] The systems and methods described herein recognize and take into account that a single depot-level heavy maintenance visit can last for weeks or months, making it very important to know when and where parts and labor hours are needed during the course of maintenance.

[0028]

[0039] 1 illustrates a data processing system 100 according to one embodiment of the present disclosure. Network data processing system 100 is a network of computers in which embodiments of the present disclosure may be implemented. Network data processing system 100 includes network 102. Network 102 is the medium used to provide communications links between various devices and computers connected together within network data processing system 100. Network 102 may include multiple connections, such as wired, wireless communication links, or fiber optic cables.

[0029]

[0040] Server computers 104 and 106, along with storage unit 108, connect to network 102. In addition, client devices 110 connect to network 102. Server computer 104 provides information (such as boot files, operating system images, and applications) to client devices 110. Client devices 110 may be, for example, computers, workstations, or network computers. As shown, client devices 110 include client computers 112, 114, and 116. Client devices 110 may also include other types of client devices, such as a mobile phone 118, a tablet computer 120, and smart glasses 122.

[0030]

[0041] As shown, server computer 104, server computer 106, storage device 108, and client device 110 are network devices that connect to network 102. In this case, network 102 is the communication medium for these network devices. Some or all of client device 110 may form the Internet of Things (IoT). In the IoT, these physical devices may connect to network 102 and exchange information with each other via network 102.

[0031]

[0042] Client device 110, in this example, is a client to server computer 104. Network data processing system 100 may include additional server computers, client computers, and other devices not shown. Client device 110 connects to network 102 using at least one of a wired, fiber optic, or wireless connection.

[0032]

[0043] Program code located within network data processing system 100 may be stored on a computer-recordable storage medium and downloaded to a data processing system or other device at the time of use. For example, program code may be recorded on a computer-recordable storage medium of server computer 104 and downloaded to client device 110 over network 102 for use by client device 110.

[0033]

[0044] In at least one example, network data processing system 100 is the Internet with network 102, which represents a worldwide collection of networks and gateways that use the TCP / IP (Transmission Control Protocol / Internet Protocol) suite of protocols to communicate with each other. At the heart of the Internet is a backbone of high-speed data communication lines between major nodes, or host computers, including thousands of commercial, government, educational, and other computer systems that route data and messages. Network data processing system 100 may be implemented using a number of different types of networks. For example, network 102 may include at least one of the Internet, an intranet, a local area network (LAN), a metropolitan area network (MAN), or a wide area network (WAN). Figure 1 is intended as an example and not as an architectural limitation for the different illustrative embodiments.

[0034]

[0045] 2 illustrates a block diagram of an incompatibility prediction system 200 according to one embodiment of the present disclosure. The incompatibility prediction system 200 includes multiple components that may be implemented in hardware, such as the hardware shown in the network data processing system 100 of FIG.

[0035]

[0046] The nonconformance prediction system 200 collates several historical work records 202 for a particular vehicle model. The vehicle model can be for any type of vehicle, such as an aircraft, an automobile, a train, a ship, a spacecraft, etc. In at least one embodiment, the historical work records 204 include a type of work task 206 performed on the vehicle, any nonconformances 208 discovered during the work task 206, required replacement parts 210 needed to resolve (e.g., repair, improve, replace, etc.) the nonconformance 208, the location 212 of the work task 206 (i.e., at which service location the work was performed), the labor time 213 (e.g., man-hours) to resolve the nonconformance, and the timing 214 of the work task 206 (e.g., 5-year service, 50,000-mile service, etc., depending on the type of vehicle).

[0036]

[0047] Based on the historical work records 202, the nonconformance prediction system 200 makes several work task predictions 216. Each work task prediction 218 calculates the probability 220 of discovering a nonconformance in the course of performing the work task, the replacement parts 222 (if any) that will be needed, and the required labor hours 223 that are likely to be needed to resolve the nonconformance(s). The work task predictions 218 may be based on the location 224 where the work task will be performed and the timing 226 of the work task.

[0037]

[0048] The nonconformance prediction system 200 may display several correlations related to nonconformance predictions on the user interface display 228. The nonconformance prediction system 200 may display the probability 230 of a nonconformance according to the type of work task. The nonconformance prediction system 200 may display the percentage 232 that a nonconformance requires a replacement part. The nonconformance prediction system 200 may display the probability 234 that a nonconformance requires a replacement part according to the type of work task. The nonconformance prediction system 200 may display the frequency 236 that replacement parts will be ordered to resolve the nonconformance according to the type of work task. The nonconformance prediction system 200 may also display the replacement parts and respective quantities 238 to be on hand at specified locations according to the scheduled work task. The nonconformance prediction system 200 also displays the estimated labor hours 239 to resolve the nonconformance according to the type of work task.

[0038]

[0049] The user interface display 228 provides interactive visual tools, such as a user interface dashboard 242, that allow the user to navigate the probability and quantity of nonconformances, replacement parts, and labor hours for any upcoming scheduled inductions. The user interface display 228 also allows the user to update the risk forecast for inductions already in progress (e.g., model that the risk has passed or that the risk has not yet manifested). The user can also utilize the user interface display 228 to review the locations and quantities where high-risk (e.g., out-of-stock) replacement parts are likely to be needed, allowing the supply chain to better forecast demand for parts due to nonconformances and mitigate the risk of stockouts.

[0039]

[0050] User interface display 228 is a physical hardware system that includes one or more display devices on which a user interface, such as user interface dashboard 242, may be displayed. In at least one embodiment, user interface dashboard 242 is a graphical user interface.

[0040]

[0051] The display devices of user interface display 228 may include at least one of a light emitting diode (LED) display, a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a computer monitor, a projector, a flat panel display, a heads-up display (HUD), a head-mounted display (HMD), a television, and / or any suitable device that can output information for visual presentation of the information.

[0041]

[0052] Based on the correlations regarding the nonconformances, the nonconformance prediction system 200 may send (a) requests 240 for replacement parts to specific locations according to the scheduled work tasks at those locations, and / or (b) labor time predictions 241 for the work required to resolve one or more nonconformances.

[0042]

[0053] In at least one embodiment, nonconformance prediction system 200 provides a "living document" process that uses live, real-time data and can serve as a platform for testing different predictive models. For example, nonconformance prediction system 200 can be used to test how far back historical part data or labor-hour data remains relevant. Nonconformance prediction system 200 can also provide data and performance monitoring features. For example, it can indicate which data sets (e.g., ongoing inductions) are being included or excluded, allowing users to monitor the quality and reliability of the predictions being generated.

[0043]

[0054] The mismatch prediction system 200 may be implemented in software, hardware, firmware, or a combination thereof. When software is used, the operations performed by the mismatch prediction system 200 may be implemented in program code configured to run on hardware, such as a processor unit. When firmware is used, the operations performed by the mismatch prediction system 200 may be implemented in program code and data and stored in persistent memory for execution by a processor. When hardware is employed, the hardware may include circuitry that operates to perform the operations within the mismatch prediction system 200.

[0044]

[0055] In example embodiments, the hardware may take the form of one or more circuit systems, integrated circuits, application specific integrated circuits (ASICs), programmable logic devices, or any other suitable type of hardware configured to perform certain operations. In a programmable logic device, a device may be configured to perform certain operations. The device may be later reconfigurable or may be permanently configured to perform certain operations. Programmable logic devices include, for example, programmable logic arrays, programmable array logic, field programmable logic arrays, field programmable gate arrays, and other suitable hardware devices. Furthermore, the process may be implemented in organic components integrated with inorganic components, or may be composed entirely of non-human organic components. For example, the process may be implemented as a circuit in an organic semiconductor.

[0045]

[0056] Computer system 250 is a physical hardware system and includes one or more data processing systems. When two or more data processing systems are present within computer system 250, the data processing systems can communicate with each other using a communication medium. The communication medium may be a network. The data processing systems may be selected from at least one of a computer, a server computer, a tablet computer, or any other suitable data processing system.

[0046]

[0057] As shown, computer system 250 includes several control units 252 (e.g., processor units, processors, etc.) configured to execute program code 254 that implements processes in exemplary embodiments. As used herein, a processor unit or processor is a hardware device and includes hardware circuitry, such as on an integrated circuit, that responds to and processes instructions and program code to operate a computer. When several control units 252 execute program code 254 for a process, the several control units 252 are one or more processors, which may be on the same computer or different computers. In other words, processes may be distributed among processor units or processors on the same computer or different computers within a computer system. Furthermore, several control units 252 may be the same or different types of processor units or processors. For example, several control units may include one or more of a single-core processor, a dual-core processor, a multi-processor core, a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), or some other type of processor unit.

[0047]

[0058] As described herein, embodiments of the present disclosure provide a system that includes one or more control units (e.g., one or more control units 252) configured to: collate historical work records 204 for a vehicle model; calculate from the historical work records 204 a probability of a non-conformance during vehicle navigation; predict labor hours to resolve the non-conformance; and output an electrical signal containing information about the labor hours to a user interface display 228. In at least one embodiment, the historical work records specify one or more work tasks, non-conformances discovered during the work task(s), any replacement part(s) required for the work task(s), and one or more past labor hours (i.e., previous labor hours for completion of a particular maintenance task) required to resolve the non-conformance. In at least one embodiment, the one or more control units are further configured to display the predicted labor hours on the user interface display 228.

[0048]

[0059] Figure 3 shows a diagram of replacement parts forecasting according to the prior art. As shown, previous demand modeling focused on predicting the total volume of individual parts from bulk historical records and did not consider the labor hours required to resolve nonconformances. Such demand modeling is best suited to modeling continuous physical failures as a function of operational usage.

[0049]

[0060] Modeling the total demand for individual parts is suitable for recommending scheduled replacements, but not for modeling inspections. While such an approach may work for minor hardware, it does not provide insight into why, where, or when replacement parts are needed, nor does it provide labor hours to resolve nonconformities.

[0050]

[0061] 4 shows a diagram of nonconformance-based forecasting, according to one embodiment of the present disclosure. Nonconformance-based forecasting 400 may be implemented in nonconformance forecasting system 200. In at least one embodiment, one or more of control units 252 are configured to receive data, such as historical work records 202, and then predict parts and labor hours to resolve one or more nonconformances.

[0051]

[0062] In at least one embodiment, the control unit(s) 252 provides a nonconformance-based forecast 400 based on nonconformance records, part orders resulting from the nonconformance, the specific activity (work task) during which the nonconformance is discovered, and the labor hours required to resolve the nonconformance. This approach links replacement part demand to root causes and also accounts for labor hours. The nonconformance-based forecast 400 recognizes and takes into account that parts tend to be ordered in groups according to specific work tasks, and that resolving a nonconformance involves maintenance personnel time (i.e., labor hours (man-hours)). Thus, in addition to the main part being replaced, there are often associated parts, such as bearings or bushings, that are also replaced, requiring the time of one or more maintenance technicians to replace the parts. By focusing on the depot's guiding tasks (work cards) rather than modeling the lifespan of individual parts, an illustrative embodiment turns otherwise unexpected reliability issues into scheduled events. This approach informs the user of what nonconformities are likely to be found, where and when they are likely to be found, replacement parts likely to resolve the nonconformities, and labor hours to resolve the nonconformities.

[0052]

[0063] FIG. 5 illustrates a flowchart of a method according to one embodiment of the present disclosure. FIG. 5 illustrates a process for predicting nonconformance of vehicle parts. The process of FIG. 5 may be implemented in hardware, software, or both. When implemented in software, the process may take the form of program code executed by one or more control units located in one or more hardware devices in one or more computer systems. For example, the process may be implemented in nonconformance prediction system 200 in computer system 250 of FIG. 2.

[0053]

[0064] Process 500 begins by the control unit(s) 252 (shown in FIG. 2 ) collating historical work records for a vehicle model, where each work record specifies a work task, a non-conformance discovered during the work task, a replacement part required for the work task, when the work task is to be performed, where the work task is to be performed, and the labor hours required to resolve the non-conformance (step 502). The control unit(s) 252 then calculates a probability of non-conformance from the historical work records according to the type of work task, the location of the work task, the timing of the work task, and the labor hours (step 504). In at least one embodiment, a work record may not include a required replacement part.

[0054]

[0065] The system then determines replacement part requirements according to the type of nonconformance (step 506). The system further determines labor hours according to the type of nonconformance (step 507).

[0055]

[0066] The system sends a request for a replacement part to the specified service location according to the scheduled work tasks for the vehicle model at the service location, the predicted probability that the non-conformance is associated with the work tasks, and the predicted labor time to resolve the non-conformance (step 508). Process 500 then ends.

[0056]

[0067] The control unit(s) 252 may display on the user interface display 228 a chart of the probability of finding a nonconformance according to the type of work task, the percentage of nonconformances requiring replacement parts, the probability of a nonconformance requiring ordering replacement parts according to the type of work task, the frequency with which replacement parts will be ordered to resolve the nonconformance according to the type of work task, the replacement parts and their respective quantities to be on hand at designated locations according to the scheduled work tasks, and / or the projected labor hours to resolve the nonconformances. For example, the control unit(s) 252 may display on the user interface display 228 the projected labor hours (e.g., man-hours) to resolve various nonconformances.

[0057]

[0068] 6 shows a block diagram of a data processing system 600 according to one embodiment of the present disclosure. Data processing system 600 may be used to implement server computers 104 and 106 and client device 110 in FIG. 1, as well as computer system 250 in FIG. 2. In this illustrative example, data processing system 600 includes a communications framework 602 that provides communications between a processor unit 604, a memory 606, a persistent storage device 608, a communications unit 610, an input / output (I / O) unit 612, and a display 614. In this example, communications framework 602 takes the form of a bus system.

[0058]

[0069] In at least one embodiment, processor unit 604 is one embodiment of control unit(s) 252 shown in FIG. 2. Processor unit 604 serves to execute instructions for software that may be loaded into memory 606. Processor unit 604 may be several processors, a multi-processor core, or some other type of processor, depending on the particular implementation. In at least one embodiment, processor 604 includes one or more conventional general-purpose central processing units (CPUs). Optionally, processor unit 604 includes one or more graphical processing units (GPUs).

[0059]

[0070] Memory 606 and persistent storage 608 are examples of storage device 616. A storage device is any piece of hardware that can store information, such as, but not limited to, data, program code in a functional form, or other suitable information, on a temporary basis, permanently, or both. Storage device 616, in these illustrative examples, may also be referred to as a computer-readable storage device. In these examples, memory 606 may be, for example, a random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 608 may take various forms, depending on the particular implementation.

[0060]

[0071] For example, persistent storage 608 may include one or more components or devices. Persistent storage 608 may be a hard drive, a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination thereof. The medium used by persistent storage 608 may also be removable. For example, a removable hard drive may be used for persistent storage 608. In these illustrative examples, communications unit 610 provides for communication with other data processing systems or devices. In these examples, communications unit 610 is a network interface card.

[0061]

[0072] Input / output unit 612 allows for the input and output of data to and from other devices that may be connected to data processing system 600. For example, input / output unit 612 may provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Further, input / output unit 612 may send output to a printer. Display 614 provides a mechanism for displaying information to a user.

[0062]

[0073] Instructions for at least one of the operating system, applications, or programs may be located in storage device 616, which is in communication with processor unit 604 through communications framework 602. The processes of various different embodiments may be performed by processor unit 604 using computer-implemented instructions that may be located in a memory (such as memory 606).

[0063]

[0074] These instructions, referred to as program code, computer usable program code, or computer readable program code, are read and executed by a processor in processor unit 604. The program code in different embodiments may be embodied on different physical or computer readable storage media, such as memory 606 or persistent storage 608.

[0064]

[0075] Program code 618 is located in a functional form on selectively removable computer readable media 620 and may be loaded onto or transferred to data processing system 600 for execution by processor unit 604. In these illustrative examples, program code 618 and computer readable media 620 form computer program product 622. In at least one example, computer readable media 620 may be computer readable storage medium 624 or computer readable signal medium 626.

[0065]

[0076] In these illustrative examples, computer readable storage medium 624 is a physical or tangible storage device used to store program code 618, rather than a medium that propagates or transmits program code 618. As used herein, computer readable storage medium 624 should not be construed as being, per se, a transitory signal such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or another transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted over a wire.

[0066]

[0077] Optionally, program code 618 may be transmitted to data processing system 600 using computer readable signal medium 626. Computer readable signal medium 626 may be, for example, a propagated data signal embodying program code 618. For example, computer readable signal medium 626 may be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals may be transmitted over at least one of communications links, such as wireless communications links, fiber optic cable, coaxial cable, a wire, or any other suitable type of communications link.

[0067]

[0078] The different components illustrated for data processing system 600 are not meant to provide architectural limitations to the manner in which different embodiments may be implemented. Different illustrative embodiments may be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system 600. Other components illustrated in FIG. 6 may differ from the illustrated examples. Different embodiments may be implemented using any hardware device or system capable of running program code 618.

[0068]

[0079] Illustrative examples of the present disclosure will be described in the context of an aircraft manufacturing and service method 700 shown in Figure 7 and an aircraft 800 shown in Figure 8. Referring initially to Figure 7, an aircraft manufacturing and service method is illustrated in accordance with an illustrative embodiment. During pre-production, aircraft manufacturing and service method 700 may include specification and design 702 and material procurement 704 of aircraft 800 in Figure 8.

[0069]

[0080] During production, component and subassembly manufacturing 706 and system integration 708 of the aircraft 800, Figure 8 takes place. The aircraft 800, Figure 8 then undergoes certification and delivery 710 before being placed into service 712. While in customer service 712, the aircraft 800, Figure 8 is scheduled for routine maintenance and service 714, which may include modifications, reconfigurations, refurbishments, and other maintenance and maintenance.

[0070]

[0081] Each process of aircraft manufacturing and service method 700 may be performed or carried out by a system integrator, a third party, an entity, or some combination thereof. In these examples, the operator may be the customer. For purposes of this specification, a system integrator may include, but is not limited to, any number of aircraft manufacturers and major system subcontractors; a third party may include, but is not limited to, any number of vendors, subcontractors, and suppliers; and an entity may be an airline, a leasing company, a military entity, a service organization, etc.

[0071]

[0082] Referring now to Figure 8, a diagram of an aircraft is shown in which an illustrative embodiment may be implemented. In this example, aircraft 800 is manufactured according to aircraft manufacturing and service method 700 in Figure 7 and may include airframe 802 having systems 804 and interior 806. Examples of systems 804 include one or more of propulsion system 808, electrical system 810, hydraulic system 812, and environmental system 814. Any number of other systems may also be included. While an aerospace industry example is shown, various illustrative embodiments may be applicable to other industries, such as the automotive industry.

[0072]

[0083] Apparatus and methods embodied herein may be utilized during at least one of the stages of aircraft manufacturing and service method 700 in Figure 7 .

[0073]

[0084] In one illustrative example, the components or subassemblies produced in component and subassembly manufacturing 706 in Figure 7 may be fabricated or manufactured in a similar manner to the components or subassemblies produced while aircraft 800 is in service 712 in Figure 7. In yet another example, one or more apparatus embodiments, method embodiments, or a combination thereof may be utilized during production stages such as component and subassembly manufacturing 706 and system integration 708 in Figure 7. One or more apparatus embodiments, method embodiments, or a combination thereof may be utilized while aircraft 800 is in service 712 in Figure 7, during maintenance and service 714, or both. The use of several different illustrative embodiments may significantly streamline the assembly of aircraft 800, reduce the cost of aircraft 800, or streamline the assembly of aircraft 800 and reduce the cost of aircraft 800.

[0074]

[0085] 9 illustrates a block diagram of a production control system 900 according to one embodiment of the present disclosure. The production control system 900 is a physical hardware system. In at least one embodiment, the production control system 900 includes at least one of a manufacturing system 902 or a maintenance system 904.

[0075]

[0086] Manufacturing system 902 is configured to manufacture products, such as aircraft 800 in Figure 8. As shown, manufacturing system 902 includes manufacturing equipment 906. Manufacturing equipment 906 includes at least one of processing equipment 908 or assembly equipment 910.

[0076]

[0087] Processing equipment 908 is equipment used to manufacture components for parts used to form aircraft 800 in FIG. 8 . For example, processing equipment 908 may include machines and tools. These machines and tools may be at least one of a drill, a hydraulic press, a furnace, an autoclave, a mold, a composite tape laying machine, an automated fiber placement (AFP) machine, a vacuum system, a robotic pick-and-place system, a flatbed cutting machine, a laser cutter, a computer numerically controlled (CNC) cutting machine, a lathe, or other suitable type of equipment. Processing equipment 908 may be used to process at least one of a metal part, a composite part, a semiconductor, a circuit, a fastener, a rib, a skin panel, a spar, an antenna, or other suitable type of part.

[0077]

[0088] Assembly equipment 910 is equipment used to assemble parts to form aircraft 800 of Figure 8. In particular, assembly equipment 910 is used to assemble components and parts to form aircraft 800 of Figure 8. Assembly equipment 910 may also include machines and tools. These machines and tools may be at least one of a robotic arm, a crawler, a fastener installation system, a rail-based drilling system, or a robot. Assembly equipment 910 may be used to assemble parts such as seats, horizontal stabilizers, wings, engines, engine housings, landing gear systems, and other parts for aircraft 800 of Figure 8.

[0078]

[0089] In at least one embodiment, maintenance system 904 includes maintenance equipment 912. Maintenance equipment 912 may include any equipment necessary to perform maintenance on aircraft 800 of FIG. 8. Maintenance equipment 912 may include tools for performing various operations on parts of aircraft 800 of FIG. 8. These operations may include at least one of disassembling a part, refurbishing a part, inspecting a part, reworking a part, manufacturing a replacement part, or other operations for performing maintenance on aircraft 800 of FIG. 8. These operations may be for routine maintenance, inspection, upgrades, modifications, or other types of maintenance work.

[0079]

[0090] In one example embodiment, maintenance equipment 912 may include ultrasonic inspection devices, x-ray imaging systems, vision systems, drills, crawlers, and other suitable devices. In some cases, maintenance equipment 912 may include processing equipment 908, assembly equipment 910, or both, for manufacturing and assembling parts required for maintenance.

[0080]

[0091] The product management system 900 also includes a control system 914. The control system 914 is a hardware system and may also include software or other types of components. The control system 914 is configured to control the operation of at least one of the manufacturing system 902 or the maintenance system 904. In particular, the control system 914 may control the operation of at least one of the processing equipment 908, the assembly equipment 910, or the maintenance equipment 912.

[0081]

[0092] The hardware in control system 914 may be implemented using hardware, which may include computers, circuits, networks, and other types of equipment. The control may take the form of direct control of manufacturing equipment 906. For example, robots, computer-controlled machines, and other equipment may be controlled by control system 914. In other examples, control system 914 may manage the processes performed by work personnel processes 916 in performing the manufacturing or maintenance of aircraft 800. For example, control system 914 may assign tasks, provide instructions, display models, or perform other processes to manage the processes performed by work personnel 916. For example, control system 914 manages at least one of the manufacturing or maintenance of aircraft 800 of FIG. 8 to employ a fuel tank protection system in a fuel tank for aircraft 800. The fuel protection system may be implemented in a fuel tank during manufacturing of the fuel tank to add a fuel tank during maintenance to aircraft 800.

[0082]

[0093] In some examples, operations personnel 916 may operate or interact with at least one of production equipment 906, maintenance equipment 912, or control system 914. This interaction may occur to produce aircraft 800 in Figure 8 .

[0083]

[0094] Product management system 900 may be configured to manage products other than aircraft 800 of Figure 8. Although product management system 900 is described in connection with manufacturing in the aerospace industry, product management system 900 may be configured to manage products for other industries. For example, product management system 900 may be configured to manufacture products for the automotive industry, as well as any other suitable industries.

[0084]

[0095] 10 illustrates a decision tree 1000 that illustrates how a method for predicting mismatches can be applied to standard supply depot guidance, according to one embodiment of the present disclosure. The tree 1000 represents the hierarchical relationships between variables during the guidance process.

[0085]

[0096] A lead is a global record of heavy maintenance depot visits for an aircraft. Each maintenance depot visit is uniquely identified. Each lead includes one or more work tasks. The one or more work tasks are scheduled tasks, and each task includes one or more work orders. The work orders include actions to be taken when a nonconformance is discovered and / or corrective maintenance to address the nonconformance. A nonconformance relates to a non-compliant part discovered during planned maintenance. A labor time, such as a man-hour, is the amount of labor time required to repair a nonconformance, such as a corrosion nonconformance. A failure code is a specific code for a specific failure, such as a corrosion failure. A failure describes a nonconformance in more detail. Because a part can be nonconforming in multiple ways, a nonconformance can generate multiple failures.

[0086]

[0097] The tree diagram 1000 shows the relationships between successive levels in a process. 1-n indicates a one-to-many relationship. For example, for a given lead, there may be multiple work tasks, and for a given work task, there may be multiple work instructions.

[0087]

[0098] 1-10 , in at least one embodiment, the control unit(s) 252 are configured to calculate the probability of finding a mismatch. Bayes' theorem can be used for a given conditional probability. In probability theory and statistics, Bayes' theorem describes the probability of an event based on prior knowledge of possible conditions surrounding that event. One application of Bayes' theorem is Bayesian inference, an approach to statistical inference. In a Bayesian interpretation of probability, the theorem describes how degrees of belief, expressed as probabilities, should reasonably vary given the availability of relevant evidence.

[0088]

[0099] Bayes' theorem is stated mathematically: P(A\B)=P(B\A)P(A) / P(B) where A and B represent events and P(B) ≠ 0. P(A\B) is the probability that event A occurs given that B is true. P(B\A) is the probability that event B occurs given that A is true. P(A) and P(B) are the respective probabilities of observing A and B.

[0089]

[0100] P(W T ) is the probability that a lead has a given work task and can be expressed as: P(W T ) = History count of a given work task in a lead / History count of multiple leads P(N C ) is the probability that the induction has a corrosion incompatibility and can be expressed as: P(N C ) = Historical count of lead tasks that resulted in NCR / Historical count of multiple leads

[0090]

[0101] Thus, given a work task, the one or more control units 252 of the non-conformance prediction system 200 calculate the probability of finding a non-conformance or the probability of finding no non-conformance. The probability of having a non-conformance for a given work task may be expressed as: P(N C \W T )=P(N C )*P(W T \N C ) / P(W T ) (apply Bayes' theorem) =P(N C )*1 / P(W T ) =P(N C ) / P(W T ) In at least one embodiment, P(W T \N C ) is equal to 1 because a non-conformance can always be associated with a work task.

[0091]

[0102] Given a disposition code, the control unit(s) 252 determine the probability that the disposition code is for a given work task. P Given is the given disposition code. T is the work task. P(W T ) is the probability of having a work task that resulted in a given disposition code. Given ) is the probability that a given induction will result in a given disposition code. Thus, the control unit(s) 252 determine a given work task that requires ordering a part, which can be expressed as: P(P0) = Historical count of work tasks that resulted in a given disposition code / Historical count of work tasks P(W T ) = Historical count of a given work task that resulted in a given disposition code / Historical count of multiple work tasks In at least one other embodiment, instead of P0, P Given can be used. P(P Given) is the probability that a given work task results in a given disposition code.

[0092]

[0103] Thus, the control unit(s) 252 determine that the probability of having a given work task with a non-conformance requiring a part to be ordered can be expressed as: P(W T \P Given )=P(P T )*P(N Given \W T ) / P(P Given ) =P(W T )*1 / P(P Given ) =P(W T ) / P(P Given )

[0093]

[0104] The control unit(s) 252 then determines the average work time for a given work task having a corrosion mismatch. In at least one embodiment, the control unit(s) 252 determines the average work time (e.g., average man-hours) as follows: Average Man-Hours = Sum of Man-Hours for a given work task with corrosion / Historical Count of a given work task with corrosion

[0094]

[0105] Next, the control unit(s) 252 determine the expected labor hours required for a given lead. For a given lead, the control unit(s) 252 calculate the total labor hours by first calculating the expected labor hours, such as the probability and the labor hours required. In particular, the control unit(s) 252 determine an overall value for all work tasks by multiplying the probability that a work task has a corrosion nonconformance by the average labor hours (i.e., average man-hours) required for each work task (e.g., as stored in the historical work record 202).

[0095]

[0106] As one non-limiting example, from stored data (e.g., recorded in historical work log 202), control unit(s) 252 determine that a lead exists having two work tasks: WT1 and WT2. Based on the stored data, control unit(s) 252 determine that WT1 has been used 10 times previously. Further, based on the stored data, control unit(s) 252 determine that, out of the 10 times, there have been 5 corrosion non-conformances for a total of 20 man-hours. Similarly, based on the stored data, control unit(s) 252 determine that WT2 has been used 100 times previously. Again, based on the stored data, control unit(s) 252 determine that, out of the 100 times, there have been 20 corrosion non-conformances for a total of 500 man-hours. Thus, the control unit(s) 252 determines that there is a 5 / 10 (i.e., 50%) probability that WT1 has a corrosion defect and a 20 / 100 (i.e., 20%) probability that WT2 has a corrosion mismatch. Further, the control unit(s) 252 determines that the average labor hours required for WT1 is 20 hours / 5 = 4 hours and the average labor hours required for WT2 is 500 hours / 20 = 25 hours. The control unit(s) 252 then calculates the expected amount of labor hours required for future scheduled inductions as 50% * 4 hours + 20% * 25 hours, or 2 hours + 5 hours, for a total of 7 hours.

[0096]

[0107] The systems and methods described herein include one or more control units configured to determine predicted labor hours (i.e., man-hours) to resolve nonconformances, such as corrosion nonconformances. The systems and methods are configured to predict labor hours needed to resolve nonconformances of one or more parts, even if the parts do not require replacement. The systems and methods described herein model labor-hour requirements instead of modeling part failures. The systems and methods described herein minimize or otherwise reduce fleet downtime or maximize or otherwise improve fleet readiness for depot-level maintenance by considering both part requirements and labor hours.

[0097]

[0108] In at least one embodiment, the systems and methods described herein may also be configured to predict costs associated with the detection of non-conformances. In at least one embodiment, the control unit(s) are configured to predict or otherwise determine material costs and costs associated with labor hours for the components.

[0098]

[0109] 11 shows a schematic block diagram of a control unit 1100 according to one embodiment of the present disclosure. The control unit 1100 is one embodiment of the control unit 252 shown in FIG. 2. In at least one embodiment, the control unit 1100 includes at least one processor 1101 in communication with a memory 1102. The memory 1102 stores instructions 1104, received data 1106, and generated data 1108. The control unit 1100 shown in FIG. 11 is merely exemplary and non-limiting.

[0099]

[0110] As used herein, terms such as "control unit," "central processing unit," "CPU," "computer," and the like may include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASIC), logic circuits, and any other circuits or processors, including hardware, software, or a combination thereof, capable of performing the functions described herein. The above examples are illustrative only and thus are not intended to limit in any way the definition and / or meaning of the above terms. For example, control unit 1100 may be or include one or more processors configured to control operations as described herein.

[0100]

[0111] The control unit 1100 is configured to execute a set of instructions stored in one or more data storage units or elements (such as one or more memories) to process data. For example, the control unit 1100 may include or be coupled to one or more memories. The data storage units may also store data or other information as desired or needed. The data storage units may take the form of an information source or a physical memory element within a processing machine.

[0101]

[0112] The set of instructions may include various commands that instruct the control unit 1100 as a processing machine to perform particular operations (e.g., methods and processes of various embodiments of the subject matter described herein). The set of instructions may take the form of a software program. The software may take various forms such as system software or application software. Further, the software may take the form of a collection of separate programs, a program subset within a larger program, or a portion of a program. The software may also include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to user commands, in response to results of previous processing, or in response to a request made by another processing machine.

[0102]

[0113] The diagrams of the embodiments herein may depict one or more control or processing units, such as control unit 1100. It should be understood that this processing or control unit may represent a circuit, circuitry, or portion thereof, that may be implemented as hardware having associated instructions (e.g., software stored on a tangible, non-transitory computer-readable storage medium such as a computer hard drive, ROM, RAM, etc.) that perform the operations described herein. The hardware may include state machine circuitry hardwired to perform the functions described herein. Optionally, the hardware may include electronic circuitry including and / or connected to one or more logic-based devices, such as a microprocessor, processor, controller, etc. Optionally, control unit 1100 may represent processing circuitry, such as a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), microprocessor(s), etc. The circuitry in various examples may be configured to execute one or more algorithms to perform the functions described herein. Such one or more algorithms, whether or not explicitly identified in a flowchart or method, may include aspects of the embodiments disclosed herein.

[0103]

[0114] As used herein, the terms "software" and "firmware" are used interchangeably and may include any computer program stored in a data storage unit (e.g., one or more memories) for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The types of data storage units listed above are merely exemplary and thus not limiting as to the types of memory that may be used for storing computer programs.

[0104]

[0115] In at least one embodiment, the control unit 1100 may further automatically (without human intervention) control one or more devices, such as maintenance systems, devices, robots, etc., at least in part, based on the predicted parts and labor hours to resolve the nonconformance. For example, based on the determined maintenance task, the predicted needed parts, and the predicted labor hours, the control unit 1100 may automatically operate various systems, such as maintenance robots, to complete the maintenance task during the labor hours as predicted by the control unit 1100 and / or assist in completing the maintenance task.

[0105]

[0116] In at least one embodiment, all or a portion of the systems and methods described herein may be or otherwise include an artificial intelligence (AI) or machine learning system capable of automatically performing the operations of the methods described herein. For example, the control unit 1100 may be an artificial intelligence or machine learning system. These types of systems may be trained from external information and / or may be self-trained, iteratively improving the accuracy of how data is analyzed to determine nonconformities, any parts needed for maintenance procedures, and labor hours to complete the maintenance work. Over time, these systems may improve by making such decisions with increasing accuracy and speed, thereby significantly reducing the likelihood of any potential errors. The AI ​​or machine learning systems described herein may include techniques enabled by adaptive and predictive capabilities. The techniques exhibit at least some degree of autonomous learning to automate and / or enhance pattern detection (e.g., recognizing irregularities or regularities in data), customization (e.g., generating or modifying rules to optimize record matching), and the like. The system may be trained and retrained using feedback from one or more prior analyses of the data, ensemble data, and / or other such data. Based on this feedback, the system may be trained by adjusting one or more parameters, weights, rules, criteria, etc. used in the same analysis. This process may be performed using the data or ensemble data instead of training data and may be repeated multiple times to iteratively improve the determination of nonconformances, any parts required for maintenance procedures, and labor hours to complete the maintenance work. Training minimizes conflicts and interferences by implementing an iterative training algorithm. During training, the system is retrained with an updated set of data based on feedback examined prior to the most recent training of the system. This provides a robust analytical model that can more accurately predict parts for maintenance work, required parts, and labor hours.

[0106]

[0117] Embodiments of the present disclosure provide systems and methods that enable a computing device to quickly and efficiently analyze large amounts of data. For example, the control unit 1100 may analyze various aspects of maintenance work for a large vehicle system. The large vehicle system includes numerous subsystems, components, parts, etc. Furthermore, the control unit 1100 considers variables based on various aspects and predicts labor hours from those variables, which may be in a format that is not easily interpretable by humans. Thus, a large amount of data that is not easily interpretable by humans is tracked and analyzed. As described herein, the vast amount of data is efficiently organized and / or analyzed by the control unit 1100. The control unit 1100 analyzes the data in a relatively short time to quickly and efficiently determine parts and labor hours to resolve the induced nonconformity. A human would not be able to efficiently analyze such a vast amount of data in such a short time. Therefore, embodiments of the present disclosure provide improved and efficient functionality and overwhelmingly superior performance to humans analyzing vast amounts of data.

[0107]

[0118] In at least one embodiment, system and method components, such as control unit 1100, provide and / or enable a computer system to operate as a dedicated computer system for predicting parts and labor hours for resolving various component non-conformities during aircraft maintenance.

[0108]

[0119] Figure 12 shows a perspective front view of an aircraft 1200, according to one embodiment of the present disclosure. Aircraft 1200 (such as a commercial aircraft) is an embodiment of aircraft 800 shown in Figure 8. Aircraft 1200 includes a propulsion system 1212 including, for example, engines 1214. Optionally, propulsion system 1212 may include more engines 1214 than are shown. Engines 1214 are supported by wings 1216 of aircraft 1200. In other embodiments, engines 1214 may be supported by fuselage 1218 and / or tail section 1220. Tail section 1220 may also support horizontal stabilizers 1222 and vertical stabilizers 1224. The fuselage 1218 of the aircraft 1200 defines an interior cabin 1230, which may include a cockpit or flight deck, one or more work sections (e.g., a galley, a crew baggage area, etc.), one or more passenger sections (e.g., first class, business class, and economy class), one or more restrooms, etc. It should be understood that the aircraft 1200 may be sized, shaped, and configured differently than that shown in FIG. 12 . Embodiments of the present disclosure may also be used in military aircraft. Additionally, embodiments of the present disclosure may be used with various other types of vehicles, such as automobiles, buses, trains, ships, spacecraft, etc.

[0109]

[0120] Furthermore, the present disclosure includes embodiments according to the following clauses.

[0110]

[0121] Article 1. A system comprising one or more control units, the one or more control units comprising: Collating historical work records for the vehicle model; calculating a probability of one or more non-conformances during a vehicle's guidance from the historical work record, the guidance being a comprehensive record of heavy maintenance depot visits; Estimating the amount of work required to resolve said one or more nonconformities; and outputting an electrical signal including information regarding the working hours to a user interface display as predicted by the one or more control units.

[0111]

[0122] Article 2. 10. The system of claim 1, wherein the historical work record specifies one or more work tasks, nonconformities discovered during the one or more work tasks, any replacement parts required for the one or more work tasks, and one or more past labor hours required to resolve the one or more nonconformities.

[0112]

[0123] Article 3. The system described in clause 1 or 2, further comprising a user interface display, wherein the one or more control units are further configured to: display the working hours on the user interface display as predicted by the one or more control units.

[0113]

[0124] Article 4. The system of any one of clauses 1 to 3, wherein the one or more control units are further configured to determine the probability that the guide has a given work task and the probability that the guide has one or more corrosion incompatibilities.

[0114]

[0125] Article 5. The system described in clause 4, wherein the one or more control units are configured to perform the following: determine a probability of having one or more non-conformities for the given work task from the probability that the guide has the given work task and the probability that the guide has the one or more corrosion non-conformities.

[0115]

[0126] Article 6. 6. The system of any one of clauses 1 to 5, wherein the one or more control units are further configured to determine a probability that a disposition code is for a given work task.

[0116]

[0127] Article 7. 7. The system of any one of clauses 1 to 6, wherein the one or more control units are further configured to: determine a probability that the one or more nonconformities have a given work task requiring a part.

[0117]

[0128] Article 8. 8. The system of any one of clauses 1 to 7, wherein the control unit is configured to predict the work time at least in part by determining an average work time as a sum of man-hours for a given work task involving corrosion divided by a historical count of the given work task.

[0118]

[0129] Article 9. The system of any one of clauses 1 to 8, wherein the control unit is configured to predict the working time at least in part by determining an overall value for all work tasks by multiplying the probability that a work task has a corrosion non-conformity by the average working time required for each work task.

[0119]

[0130] Article 10. 10. The system of any one of clauses 1 to 9, wherein the one or more control units are further configured to automatically control one or more maintenance devices to perform one or more maintenance tasks during the working hours.

[0120]

[0131] Article 11. 11. The system of any one of clauses 1 to 10, wherein the one or more control units are artificial intelligence or machine learning systems.

[0121]

[0132] Article 12. collating, by one or more control units, historical operating records relating to the vehicle model; calculating, by the one or more control units, a probability of one or more non-conformances during vehicle guidance from the historical work record, the guidance being an overall record of heavy supply depot visits; predicting, by said one or more control units, labor hours for resolving said one or more nonconformities; outputting, by the one or more control units, an electrical signal to a user interface display, the electrical signal including information regarding the working hours as predicted by the one or more control units; and and displaying, by the one or more control units, the working hours on the user interface display as predicted by the one or more control units.

[0122]

[0133] Article 13. 13. The method of claim 12, wherein the historical work record specifies one or more work tasks, nonconformities discovered during the one or more work tasks, any replacement parts required for the one or more work tasks, and one or more past work hours required to resolve the one or more nonconformities.

[0123]

[0134] Article 14. 14. The method of claim 12 or 13, wherein predicting includes determining a probability that the lead has a given work task and a probability that the lead has one or more corrosion incompatibilities.

[0124]

[0135] Article 15. 15. The method of claim 14, wherein predicting further comprises determining a probability of having the one or more corrosion nonconformities for the given work task from the probability that the lead has the given work task and a probability that the lead has the one or more corrosion nonconformities.

[0125]

[0136] Article 16. 16. The method of any one of clauses 12 to 15, wherein predicting includes determining a probability that a disposition code is for a given work task.

[0126]

[0137] Article 17. 17. The method of any one of clauses 12 to 16, wherein predicting includes determining a probability that the one or more nonconformities have a given work task requiring a part.

[0127]

[0138] Article 18. 18. The method of any one of clauses 12 to 17, wherein the predicting includes determining an average work time as the sum of man-hours for a given work task involving corrosion divided by the historical count for the given work task.

[0128]

[0139] Article 19. 19. The method of any one of clauses 12 to 18, wherein the predicting includes determining an overall value for all work tasks by multiplying the probability that a work task has a corrosion nonconformity by the average labor hours required for each work task.

[0129]

[0140] Article 20. A non-transitory computer-readable storage medium containing executable instructions that, when executed, cause one or more control units, including a processor, to perform a plurality of operations, the operations including: Collating historical work records for the vehicle model; calculating a probability of one or more non-conformances during a vehicle guidance from the historical work record, the guidance being an overall record of heavy supply depot visits; an estimate of the labor time required to resolve said one or more nonconformities; outputting an electrical signal containing information regarding the working hours to a user interface display as predicted by the one or more control units; and and displaying the work hours as predicted by the one or more control units on a user interface display.

[0130]

[0141] As described herein, embodiments of the present disclosure provide systems and methods for efficiently and effectively scheduling aircraft maintenance, including parts and labor. Additionally, embodiments of the present disclosure provide systems and methods for predicting and forecasting corrosion-related non-conformances for aircraft during maintenance operations.

[0131]

[0142] For purposes of describing the embodiments of the present disclosure, various spatial and directional terms may be used, such as top, bottom, lower, center, sideways, horizontal, vertical, front, etc., but it should be understood that such terms are used solely with reference to the orientations shown in the drawings. These orientations may be flipped, rotated, or otherwise changed so that top becomes bottom, bottom becomes top, horizontal becomes vertical, etc.

[0132]

[0143] As used herein, a structure, element, or element that is "configured to" perform a task or step is particularly structurally shaped, constructed, or adapted in a manner corresponding to the task or step. For purposes of clarity and avoidance of doubt, an object that is merely modifiable to perform a task or step is not "configured to" perform a task or step as used herein.

[0133]

[0144] It should be understood that the above description is intended to be illustrative, not limiting. For example, the above-described examples (and / or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt the teachings of the various embodiments of the present disclosure to a particular situation or material without departing from the scope of the present disclosure. While the dimensions and types of materials described herein are intended to define aspects of the various embodiments of the present disclosure, the examples are by no means limiting, but are illustrative examples. Many other examples will be apparent to those skilled in the art upon reviewing the above description. The scope of the various embodiments of the present disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims and the description herein, the terms "comprising" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "in which." Furthermore, terms such as "first," "second," and "third" are used merely as labels and are not intended to impose numerical requirements on their objects. Furthermore, the following claim limitations are not stated in means-plus-function form and are not intended to be construed under 35 U.S.C. §112(f) unless such claim limitations expressly use the phrase "means for" followed by a recitation of function lacking further structure.

[0134]

[0145] The description herein uses examples to disclose various embodiments of the present disclosure, including the best mode, and to enable any person skilled in the art to practice various embodiments of the present disclosure, including making and using any device or system and practicing any methods incorporated therein. The patentable scope of various examples of the present disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if the examples have structural elements that do not differ from the literal language of the claims, or if the examples include equivalent structural elements that differ only insignificantly from the literal language of the claims.

Claims

1. A system (200) comprising one or more control units (252), said one or more control units (252) comprising: collating historical work records (202, 204) relating to the vehicle model; calculating a probability of one or more non-conformances (210) during a vehicle's guidance from the historical work records (202, 204), the guidance being a comprehensive record of heavy maintenance depot visits; predicting the labor hours (213, 218, 239) required to resolve said one or more nonconformities (210); and and outputting an electrical signal to a user interface display (228) including information regarding said working hours (213, 218, 239) as predicted by said one or more control units (252).

2. 2. The system of claim 1, wherein the historical work record specifies one or more work tasks, nonconformities discovered during the one or more work tasks, any replacement parts needed for the one or more work tasks, and one or more past work hours required to resolve the one or more nonconformities.

3. 2. The system (200) of claim 1, further comprising a user interface display (228), wherein the one or more control units (252) are further configured to: display the working hours (213, 218, 239) on the user interface display (228) as predicted by the one or more control units (252).

4. 2. The system (200) of claim 1, wherein the one or more control units (252) are further configured to determine a probability that the guide has a given work task and a probability that the guide has one or more corrosion incompatibilities (210).

5. The system (200) of claim 4, wherein the one or more control units (252) are configured to: determine a probability of having the one or more corrosion incompatibilities (210) for the given work task from the probability that the guide has the given work task and the probability that the guide has the one or more corrosion incompatibilities (210).

6. The system of claim 1 , wherein the one or more control units are further configured to: determine a probability that a disposition code is for a given work task.

7. 2. The system of claim 1, wherein the one or more control units are further configured to: determine a probability that the one or more nonconformities have a given work task requiring a part.

8. 2. The system of claim 1, wherein the control unit is configured to predict the work hours at least in part by determining an average work hour as a sum of man-hours for a given work task involving corrosion divided by a historical count of the given work task.

9. 2. The system (200) of claim 1, wherein the control unit is configured to predict the work hours (213, 218, 239) at least in part by determining an overall value for all work tasks (206, 230, 234, 236, 238) by multiplying the probability that a work task has a corrosion mismatch by the average work hours (213, 218, 239) required for each work task.

10. 2. The system (200) of claim 1, wherein the one or more control units (252) are further configured to automatically control one or more maintenance devices to perform one or more maintenance tasks during the working hours (213, 218, 239).

11. The system (200) of claim 1 , wherein the one or more control units (252) are artificial intelligence or machine learning systems.

12. collating, by one or more control units (252), historical work records (202, 204) relating to the vehicle model; calculating, by the one or more control units (252), from the historical operation records (202, 204), a probability of one or more mismatches (210) during vehicle guidance, the guidance being an overall record of heavy supply station visits; predicting, by said one or more control units, labor hours (213, 218, 239) for resolving said one or more non-conformities (210); outputting, by said one or more control units (252), an electrical signal to a user interface display (228) containing information regarding said working hours (213, 218, 239) as predicted by said one or more control units (252); and and displaying, by the one or more control units (252), on the user interface display (228) the working hours (213, 218, 239) as predicted by the one or more control units (252).

13. 13. The method of claim 12, wherein the historical work record (202, 204) specifies one or more work tasks (206, 230, 234, 236, 238), nonconformities (210) discovered during the one or more work tasks (206, 230, 234, 236, 238), any replacement parts (210, 222) needed for the one or more work tasks (206, 230, 234, 236, 238), and one or more past work hours (213, 218, 239) needed to resolve the one or more nonconformities (210).

14. The method of claim 12 , wherein the predicting comprises determining a probability that the lead has a given work task and a probability that the lead has one or more corrosion incompatibilities (210).

15. 15. The method of claim 14, wherein the predicting further comprises determining a probability of having the one or more corrosion incompatibilities (210) for the given work task from the probability that the lead has the given work task and the probability that the lead has the one or more corrosion incompatibilities (210).

16. The method of claim 12 , wherein predicting comprises determining a probability that a disposition code is for a given work task.

17. The method of claim 12 , wherein the predicting comprises determining a probability that the one or more nonconformances (210) have a given work task requiring a part.

18. 13. The method of claim 12, wherein the predicting includes determining an average work time (213, 218, 239) as a sum of man hours for a given work task involving corrosion divided by a historical count of the given work task (206, 230, 234, 236, 238).

19. 13. The method of claim 12, wherein the predicting includes determining an overall value for all work tasks (206, 230, 234, 236, 238) by multiplying the probability that a work task has a corrosion mismatch by the average labor hours (213, 218, 239) required for each work task.

20. A non-transitory computer-readable storage medium containing executable instructions that, when executed, cause one or more control units (252) including a processor to perform a plurality of operations, the operations including: collating historical work records (202, 204) relating to the vehicle model; calculating a probability of one or more non-conformances (210) during a vehicle's guidance from the historical work records (202, 204), the guidance being a comprehensive record of heavy maintenance depot visits; predicting the labor hours (213, 218, 239) required to resolve said one or more non-conformances (210); outputting an electrical signal containing information regarding said working hours (213, 218, 239) to a user interface display (228) as predicted by said one or more control units (252); and and displaying, on the user interface display, the working hours as predicted by the one or more control units.

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