Method and system for automatic detection and interpretation of anomalies

Abstract

A computer-implemented method for aircraft maintenance is provided.SOLUTION: The method includes monitoring sections and components of the aircraft using sensors. The method includes detecting, by a sensor, an anomaly in an aircraft. In response to the detection, the method includes transmitting real-time information from the sensor to a computer having a AI algorithm. The method includes analyzing the datum with a AI algorithm to identify characteristics of the anomaly. The method includes sending a report to the pilot and to the computer in response to the identifying.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001]

[0001] The present disclosure relates generally to the detection of anomalies and irregularities, and more particularly to methods and systems for the automated detection and interpretation of anomalies in various sections and components of an aircraft. [Background technology]

[0002]

[0002] Aircraft in flight are frequently exposed to various hazards, including lightning strikes, foreign object collisions, and other incidents, which can lead to abnormalities or inconsistencies in various sections and components of the aircraft. Despite advances in aviation technology, pilots and maintenance personnel often remain unaware of such phenomena until periodic visual inspections are performed. However, certain areas of an aircraft's exterior are visually inspected infrequently, sometimes only once a year. For example, the top surface of an aircraft's fuselage may go unchecked for long periods of time, increasing the likelihood of unchecked damage propagating. Aircraft interior components and sections are inspected even more rarely, despite the possibility that anomalies may be lurking within the aircraft.

[0003] Current aircraft maintenance protocols rely heavily on manual visual inspections performed by maintenance personnel to detect anomalies. This approach is reactive, time-consuming, and prone to human error. Maintenance personnel must physically inspect areas and components of the aircraft, looking for any signs of anomalies. Furthermore, detecting an anomaly requires human intervention, leading to further delays and potentially grounding the aircraft for repair scheduling.

[0004]

[0004] The inefficiencies inherent in manual inspection processes highlight the need for more efficient and automated approaches to anomaly detection, interpretation, and repair. Summary of the Invention

[0005]

[0005] Exemplary embodiments provide a computer-implemented method for aircraft maintenance. The method includes monitoring sections and components of the aircraft using sensors and detecting an anomaly within the aircraft with the sensors. The method includes, in response to the detection, transmitting real-time data from the sensors to a computer having an artificial intelligence (AI) algorithm. The method includes analyzing the data with the AI ​​algorithm to identify characteristics of the anomaly. The method includes, in response to the identification, transmitting a report to a pilot and to the computer.

[0006]

[0006] In an exemplary embodiment, the computer is on the ground, in the cloud, in an aircraft, or in a satellite.

[0007]

[0007] In one exemplary embodiment, an AI algorithm utilizes real-time data and historical information to identify characteristics of anomalies.

[0008] In one exemplary embodiment, the method includes utilizing an AI algorithm to analyze the report to determine availability of a smart hangar for aircraft inspection, and determining a maintenance schedule for the aircraft based on the availability of the smart hangar. The method includes preparing a smart inspection tool for deployment based on the determined availability of the smart hangar.

[0009]

[0009] In one exemplary embodiment, the plurality of sensors includes at least one of a piezoelectric sensor, a microelectromechanical system (MEMS), an electromagnetic (EM) sensor, a LIDAR, a camera, a shearography sensor, or a nanorobot.

[0010] Another exemplary embodiment provides a system for aircraft maintenance. The system includes a storage device configured to store program instructions. The system includes one or more processors operatively connected to the storage device and configured to execute the program instructions to cause the system to monitor sections and components of the aircraft using sensors, detect anomalies within the aircraft with the sensors, transmit real-time data from the sensors to a computer having an artificial intelligence (AI) algorithm upon detection, analyze the data with the AI ​​algorithm to identify characteristics of the anomaly, and transmit a report to the pilot and the computer upon identification. The computer may be on the ground, in the cloud, on board the aircraft, or in a satellite.

[0011]

[0011] The novel features believed characteristic of the illustrative embodiments are set forth in the appended claims. However, the illustrative embodiments, as well as their preferred modes of use, further objects and features thereof, will best be understood by reading the following detailed description of illustrative embodiments of the present disclosure when read in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a pictorial representation of a system in which exemplary embodiments may be implemented. [Figure 2]

[0013] FIG. 1 is a functional block diagram of a system for anomaly detection, interpretation, maintenance, and repair in accordance with an illustrative embodiment. [Figure 3]

[0014] FIG. 1 is a functional block diagram for anomaly detection and interpretation using AI algorithms. [Figure 4]

[0015] 10 is a pictorial illustration of a process in accordance with an illustrative embodiment; [Figure 5]

[0016] 1 is a flowchart of a process in accordance with an illustrative embodiment; [Figure 6]

[0017] 1 is an illustration of a block diagram of a data processing system in accordance with an illustrative embodiment; [Figure 7] 1 is an illustration of aircraft manufacturing and service in accordance with an illustrative embodiment; [Figure 8]

[0019] The sections and systems of the aircraft are shown. DETAILED DESCRIPTION OF THE INVENTION

[0013]

[0020] The illustrative embodiments address limitations in current aircraft maintenance protocols. While the illustrative embodiments are described with reference to aircraft maintenance and repair, the embodiments may also be implemented in other industries (e.g., automotive, transportation, healthcare, maritime). The illustrative embodiments provide an automated approach to anomaly detection, interpretation, maintenance, and repair. Embodiments of the present disclosure utilize on-board sensors and artificial intelligence (AI) algorithms for predictive, proactive detection and interpretation of anomalies. Additionally, embodiments of the present disclosure provide communication tools and automation to reduce manual effort and improve efficiency.

[0014]

[0021] 1, a pictorial representation of a system 100 is shown in which exemplary embodiments may be implemented. System 100 is a network of computers in which exemplary embodiments may be implemented. System 100 includes a ground-based computer 104 and an aircraft-based computer 110, and facilitates automated methods and systems for anomaly detection, interpretation, maintenance, and repair.

[0015]

[0022] System 100 includes network 102. Network 102 is the medium used to provide a communications link between ground-based computer 104 and aircraft-based computer 110. Network 102 may include connections such as wires, wireless links, or fiber optic cables. Ground-based computer 104 and aircraft-based computer 110 may also rely on satellite communications link 130 in addition to network 102 for bidirectional communications.

[0016]

[0023] Ground-based computer 104 may include a server computer 106 and a storage unit 108, which are connected to network 102. In the illustrated embodiment, server computer 106 provides information such as boot files, operating system images, and applications to aircraft-based computer 110.

[0017]

[0024] Aircraft-based computers 110 may be, for example, computers, workstations, or network computers. As shown, aircraft computers 110 include computers 112, 114, and 116. Aircraft-based computers 110 may also include other types of devices, such as a mobile phone 118 or a tablet 120.

[0018]

[0025] The program code located within system 100 may be stored on a computer-readable storage medium and may be downloaded to a data processing system or other device during use. For example, the program code may be stored on computer-readable storage media in server computer 106 and storage unit 108 and downloaded to aircraft-based computer 110 over network 102 during use. The program code may also be stored on storage media within aircraft-based computer 110.

[0019]

[0026] In the exemplary embodiment of Figure 1, network 102 includes the Internet, which represents a worldwide collection of networks and gateways that use the Transmission Control Protocol / Internet Protocol (TCP / IP) suite of protocols to communicate with one another. System 100 may also be implemented using different types of networks. For example, network 102 may comprise 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 different illustrative embodiments.

[0020]

[0027] 2 is a functional block diagram of a system 200 for anomaly detection, interpretation, maintenance, and repair. System 200 includes an aircraft 202. Aircraft 202 communicates bidirectionally with a central computer 204 via communications link 206. Communications link 206 may include a wireless link, a satellite link, and / or a wired link (e.g., the Internet, fiber optic cable).

[0021]

[0028] The aircraft 202 includes on-board sensors 210 configured to detect anomalies or inconsistencies within various sections and components of the aircraft 202. For example, the sensors 210 may be installed to detect anomalies in various sections of the aircraft 202, such as the exterior, subsurface, interior, engine, and cargo bay. These on-board sensors may include a variety of advanced technologies, including, for example, piezoelectric sensors, micro-electromechanical systems (MEMS), cameras, light detection and ranging (LIDAR), nanorobots, millimeter-wave technology, shearography sensors, and other types of sensors. The sensors may also include electromagnetic (EM) sensors. EM sensors operate at different frequencies, including, but not limited to, millimeter wavelengths.

[0022]

[0029] On-board sensors 210 within aircraft 202 detect anomalies when they occur during in-flight events such as lightning strikes, foreign object collisions, or atmospheric turbulence. Incidents involving passengers or crew members are also considered anomalies. Sensors 210 continuously monitor various sections and components of aircraft 202. Upon detecting any deviation from normal, sensors 202 transmit real-time data to the pilot and central computer 204.

[0023]

[0030] In some demonstrative embodiments, system 200 may include aircraft-based computer 212 configured to communicate bidirectionally with central computer 204 via communication link 206. Although system 200 is illustrated as including aircraft-based computer 202 and central computer 204, system 200 may be configured to include only a single central computer (e.g., central computer 204). Such a central computer may be located on the ground, in the cloud (e.g., a cloud-based server), or in a satellite.

[0024]

[0031] Aircraft 202 includes a communications module 214. Communications module 214 establishes communications between aircraft 202 and central computer 204. Communications module 214 provides access to other components within aircraft 202 and central computer 204.

[0025]

[0032] Aircraft 202 includes memory 216 configured to store sensor outputs and data generated by aircraft-based computer 212. Memory 216 may be a hard disk drive, a solid state drive, RAM, ROM, or flash memory.

[0026]

[0033] The central computer 204 may be, for example, a server computer, a cluster of computers, or a quantum computer. The central computer 204 includes an anomaly detection / interpretation application 218. The anomaly detection / interpretation application 218 includes artificial intelligence (AI) algorithms. The AI ​​algorithms analyze and interpret anomalies and inconsistencies.

[0027]

[0034] In some demonstrative embodiments, application 218 integrates augmented reality (AR) and mixed reality (MR) technologies with other related technologies (also known as digital twins). AR and MR capabilities and digital twins support detailed analysis and accurate interpretation of anomalies and discrepancies. Maintenance personnel may utilize AR and MR technologies during maintenance and repair processes. By utilizing AR and MR technologies, maintenance personnel can visualize complex issues, thereby improving efficiency and effectiveness in addressing anomalies and discrepancies.

[0028]

[0035] By utilizing historical and statistical data, anomaly detection / interpretation application 218 can analyze and interpret sensor outputs to identify the exact type, location, and size of the anomaly while the aircraft is airborne. Based on the analysis and interpretation by application 218, central computer 204 relays alerts and related information to pilots and ground-based maintenance personnel for preparation of maintenance, repairs, and further inspections as needed. If the anomaly includes an accident involving crew or passengers, central computer 204 can send alerts to healthcare service providers (e.g., ambulances, emergency services, hospitals) and / or law enforcement agencies as needed.

[0029]

[0036] In one exemplary embodiment, application 218 determines the optimal repair time and location, if necessary, based on the severity of the anomaly and the aircraft schedule. This proactive and predictive approach minimizes aircraft downtime, maximizes fleet availability, and ultimately improves overall operational efficiency.

[0030]

[0037] Central computer 204 includes database 220. Database 220 may store historical and statistical data associated with anomalies and inconsistencies. Application 218 may access the data stored in database 220 to interpret sensor outputs while the aircraft is airborne to identify the exact type, location, size, and severity of the anomaly.

[0031]

[0038] Central computer 204 includes a graphical user interface 222. Graphical user interface 222 provides maintenance personnel with tools to interact with both the aircraft and database 220. Graphical user interface 222 provides a dashboard that displays real-time status updates, anomaly reports, and maintenance recommendations. Graphical user interface 222 allows maintenance personnel to upload, modify, delete, change, or update data.

[0032]

[0039] The central computer 204 may include memory 224 configured to store sensor outputs and data generated by the central computer 204. The memory 224 may be a hard disk drive, a solid state drive, RAM, ROM, or flash memory.

[0033]

[0040] 3 is a functional block diagram of a system 300 for anomaly detection and interpretation utilizing AI algorithms, according to one illustrative embodiment. System 300 provides an efficient, automated process to reduce manual effort and improve productivity. System 300 may be implemented within central computer 204. In other illustrative embodiments, system 300 may be implemented within aircraft 202.

[0034]

[0041] On-board sensors 210 in the aircraft detect anomalies when they occur during an in-flight event. Incidents involving passengers or crew members are also considered anomalies. Upon detecting any deviation from the norm, such as a dent, crack, surface irregularity, or in-flight incident, sensors 210 transmit real-time data to system 300. Processor 304 serves as a central computing unit responsible for executing instructions and processing data. Processor 304 receives, manages, and acts on the real-time sensor data transmitted by on-board sensors 210.

[0035]

[0042] The system 300 includes a storage device 306 coupled to the processor 304. The storage device 306 is configured to store data generated by the sensors 210. The storage device 306 may also store historical data and / or statistical data related to anomaly detection and interpretation. Historical data and statistical data play an important role in anomaly detection and interpretation. Historical data refers to a collection of past observations, events, or measurements related to anomaly or accident cases, and aircraft operation and maintenance. Historical data includes a wide range of parameters, including sensor readings, maintenance records, and flight data. Statistical data includes analysis of past observations, events, or measurements related to anomaly or accident cases, and aircraft operation and maintenance to derive meaningful insights and patterns.

[0036]

[0043] System 300 includes an AI application 308 coupled to processor 304 and storage device 306. AI application 308 includes algorithms configured to analyze and interpret sensor data using historical and statistical data. By utilizing the historical and statistical data, AI application 308 can identify patterns, trends, and recurring anomalies that may indicate underlying problems or potential risks. For example, historical data may reveal common failure modes in particular aircraft components or environmental conditions that contribute to anomalies.

[0037]

[0044] In an exemplary embodiment, the AI ​​application 308 is a computational algorithm that learns patterns and relationships from data to make predictions or decisions. The AI ​​application 308 may be programmed or configured to perform analysis and synthesis steps. The AI ​​application 308 is trained using a set of input data and corresponding output labels to learn underlying patterns in the data. During the training phase, the AI ​​application 308 may employ a learning algorithm, such as gradient descent or stochastic gradient descent, to iteratively adjust its internal parameters or weights based on the input data and output labels. Upon completion of the training phase, the AI ​​application 308 generates a trained model or mapping function that captures the relationships between the input data and the output labels learned from the training data. The trained model or mapping function essentially represents a mathematical approximation of the underlying patterns and relationships in the data. When presented with new input data, the trained model or mapping function is utilized to make predictions or decisions. Based on its interpretation of the sensor output, the AI ​​application 308 generates alerts 312 that are relayed to pilots and ground-based maintenance personnel. If the anomaly results from an in-flight incident involving crew or passengers, alerts may be sent to healthcare providers (emergency personnel, hospitals) and / or law enforcement, as appropriate.

[0038]

[0045] In some example embodiments, application 308 integrates augmented reality (AR) and mixed reality (MR) technologies and other related technologies called digital twins. Maintenance personnel can utilize AR and MR technologies and digital twins during maintenance and repair processes. By utilizing AR and MR technologies, maintenance personnel can visualize complex issues, thereby improving efficiency and effectiveness in addressing anomalies and inconsistencies. AR and MR technologies can be implemented within smart hangars, smart tools, AI factories, or any repair / maintenance facility.

[0039]

[0046] 4 pictorially illustrates process 400, according to one exemplary embodiment. When a physical anomaly occurs during flight, sensors within aircraft 402 generate real-time data 404. Computer 212 then interprets the sensor data and relays alerts and reports 406 to the pilot and / or ground-based computer 204. In other exemplary embodiments, on-board sensors send alerts and data directly to the pilot and / or central computer 204. In response to the alerts and reports, maintenance personnel prepare smart tools and / or smart hangars for inspection 408. If smart tools and smart hangars are not available, maintenance personnel use the sensor data to plan and optimize maintenance / repairs using other tools.

[0040]

[0047] Once the aircraft arrives inside the hangar, smart tools, including drones equipped with high-resolution cameras, advanced sensors, and X-ray machines, are strategically deployed to inspect identified areas for abnormalities. 410 These smart tools perform comprehensive inspections and assess the extent and severity of the anomalies using a variety of techniques, including visual inspection, ultrasonic scanning, shearography, millimeter wave technology, electromagnetic (EM) sensors, LIDAR, cameras, and other optical technologies, as well as X-ray imaging. EM sensors may operate at different frequencies, including, but not limited to, millimeter wavelengths.

[0041]

[0048] AI algorithms in the application 218 then interpret the data 412 generated by the smart tool to identify the exact type, location, size, nature, and severity of the anomaly. In some embodiments, the application 218 integrates augmented reality (AR) and mixed reality (MR). By utilizing AR and MR technologies (digital twins), maintenance personnel in the smart hangar or AI factory can visualize the exact type, location, size, nature, and severity of the anomaly, thereby improving efficiency and effectiveness in addressing anomalies and inconsistencies.

[0042]

[0049] 5 is a flowchart of a process 500 for anomaly detection, interpretation, maintenance, and repair, according to an example embodiment. Although process 500 is described with reference to aircraft maintenance and repair, the process may also be implemented in other industries (e.g., automotive, transportation, healthcare, maritime).

[0043]

[0050] At block 502, the aircraft-based computer 202 determines whether the on-board sensors have detected any anomalies within the aircraft. For example, real-time data generated by the on-board sensors may indicate the detection of an anomaly.

[0044]

[0051] If an anomaly is detected, in block 504, application 212 in aircraft-based computer 202 interprets the sensor data and relays alerts and reports to the pilot and ground-based computer 204. In block 506, in response to the alerts and reports, ground-based computer 204 determines whether any smart hangars are available. In an exemplary embodiment, the smart hangar is an Internet of Things (IoT)-based hangar. The hangar includes integrated tools, such as cameras on rails, pressure sensors, and drones, pre-positioned for inspection and maintenance. If a smart hangar is available, in block 508, the smart hangar is prepared for smart tools to inspect arriving aircraft. Such preparation may include placing cameras on rails and loading flight plans onto drones. In block 510, when the aircraft arrives at the hangar, smart tools are deployed to inspect the identified anomalous areas. The smart tools may be equipped with high-resolution cameras, advanced sensors, and X-ray machines. These smart tools perform inspections using a variety of techniques, including visual inspection, ultrasound scanning, shearography, and x-ray imaging, to assess the extent and severity of the abnormality. During this stage, AI algorithms within computer 204 interpret the data generated by the smart tools to identify the exact type, location, size, and severity of the abnormality.

[0045]

[0052] If a smart hangar is not available, in block 512, maintenance personnel prepare tools for inspection while the aircraft is in flight. In block 514, computer 204 determines whether drones and / or automated cameras are available for inspection. In some exemplary embodiments, maintenance personnel may determine the availability of smart tools (e.g., drones, cameras, etc.). If computer 204 cannot determine the availability of smart tools, maintenance personnel may be alerted by computer 204 and requested to determine availability. If drones and / or automated cameras are available, in block 516, maintenance personnel utilize the drones and cameras to obtain images of the locations of anomalies identified by the on-board sensors and aircraft-based computer 202 using AI algorithms. If drones and / or automated cameras are not available, in block 518, maintenance personnel perform a manual inspection of the areas identified by the on-board sensors and aircraft-based computer 202. In block 520, data and images generated by the cameras and drones are stored in a database in ground-based computer 204.

[0046]

[0053] If the on-board sensors do not detect any anomalies, then in block 522, maintenance personnel perform an inspection of the aircraft using tools. These tools are configured to inspect various sections, areas, and components of the aircraft. In block 524, images and other data acquired during the inspection are reviewed and analyzed using AI algorithms in computer 204. In block 526, repair work is performed by maintenance personnel, if necessary. In block 528, output from all smart tools, drones, and cameras, as well as related reports and alerts, are displayed on graphical user interface 218.

[0047]

[0054] Referring now to Figure 6, a block diagram of a data processing system is shown in accordance with an illustrative embodiment. Data processing system 600 may be used to implement aircraft-based computer 202 and ground-based computer 204. In this illustrative example, data processing system 600 includes a communications framework 602 that facilitates communications between processor unit 604, memory 606, persistent storage 608, communications unit 610, input / output unit 612, and display 614. In this example, communications framework 602 may take the form of a data bus.

[0048]

[0055] Processor unit 604 functions 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 one embodiment, processor unit 604 includes one or more conventional general-purpose central processing units (CPUs). In an alternative embodiment, processor unit 604 includes one or more graphical processing units (GPUs).

[0049]

[0056] Memory 606 and persistent storage 608 are examples of storage device(s) 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.

[0050]

[0057] For example, persistent storage 608 may contain one or more components or devices. For example, 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 illustrative examples, communications unit 610 is a network interface card.

[0051]

[0058] 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.

[0052]

[0059] Instructions for at least one of the operating system, applications, or programs may be located in storage devices 616. Storage devices 616 are in communication with processor unit 604 via communications framework 602. The processes of the different embodiments may be executed by processor unit 604 using computer-implemented instructions, which may be located in a memory, such as memory 606. These instructions are referred to as program code, computer-usable program code, or computer-readable program code, which may be read and executed by a processor within processor unit 604. The program code of the different embodiments may be embodied in different physical or computer-readable storage media, such as memory 606 or persistent storage 608.

[0053]

[0060] 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 one example, computer readable media 620 may be computer readable storage medium 624 or computer readable signal medium 626.

[0054]

[0061] In these illustrative examples, computer readable storage media 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.

[0055]

[0062] Alternatively, program code 618 may be transferred to data processing system 600 using computer readable signal media 626. Computer readable signal media 626 may be, for example, a propagated data signal containing program code 618.

[0056]

[0063] 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. The different illustrative embodiments may be implemented in a data processing system including components in addition to or instead of those illustrated for data processing system 600. Other components illustrated in FIG. 6 may differ from the illustrated example. The different embodiments may be implemented using any hardware device or system capable of executing program code 618.

[0057]

[0064] An example embodiment of the present disclosure may be described with reference to aircraft manufacturing and service method 700 shown in Figure 7 and aircraft 800 shown in Figure 8. Referring initially to Figure 7, an aircraft manufacturing and service method is illustrated in accordance with an example 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.

[0058]

[0065] During production, component and subassembly manufacturing 706 and system integration 708 of the aircraft 800 takes place. The aircraft 800 then undergoes certification and delivery 710 and is placed into service 712. While in customer service 712, the aircraft 800 in Figure 8 is scheduled for routine maintenance and service 714, which may include modifications, reconfigurations, refurbishments, and other maintenance and maintenance.

[0059]

[0066] 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. As used herein, 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.

[0060]

[0067] Referring now to Figure 8, an illustration of an aircraft in which illustrative embodiments may be implemented is shown. In this example, aircraft 800 is manufactured according to aircraft manufacturing and service method 700 in Figure 7 and may include airframe 802 with 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 be included. Although an aerospace example is shown, various illustrative embodiments may be applied to other industries, such as the automotive industry.

[0061]

[0068] 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 .

[0062]

[0069] 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 as 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 utilization of several different illustrative embodiments may significantly increase the efficiency of the assembly of aircraft 800, reduce the cost of aircraft 800, or both increase the efficiency of the assembly of aircraft 800 and reduce the cost of aircraft 800.

[0063]

[0070] As used herein, the term "a number of," when used in reference to an item, means one or more items. For example, "several different types of networks" means one or more different types of networks.

[0064]

[0071] Furthermore, the phrase "at least one of," when used in conjunction with a list of items, means that various combinations of one or more of the listed items may be used, and that only one of each listed item may be required. In other words, "at least one of" means that any combination of items and any number of items from the list may be used, and not all of the listed items are required. An item may be a specific object, article, or category.

[0065]

[0072] For example, without limitation, "at least one of item A, item B, and item C" may include item A, item A and item B, or item B. This example may also include item A, item B, and item C, or item B and item C. Of course, any combination of these items may be present. In some illustrative examples, "at least one of" may be, by way of example and not limitation, "two items A, one item B, and ten items C," "four items B, and seven items C," or other suitable combinations.

[0066]

[0073] The flowcharts and block diagrams in the various illustrated embodiments illustrate the structure, functionality, and operation of some possible implementations of apparatuses and methods in an example embodiment. In this regard, each block in a flow diagram or block diagram may represent at least one of a module, a segment, a function, or a portion of an operation or step. For example, one or more blocks may be realized as program code, hardware, or a combination of program code and hardware. If implemented in hardware, the hardware may take the form of, for example, an integrated circuit that is manufactured or configured to perform one or more operations in the flow diagram or block diagram. If implemented as a combination of program code and hardware, the implementation may take the form of firmware. Each block in a flow diagram or block diagram may be implemented using a dedicated hardware system that performs various operations, or a combination of dedicated hardware and program code executed by the dedicated hardware.

[0067]

[0074] In some alternative implementations of an exemplary embodiment, one or more functions noted in a block may occur out of the order noted in the figures. For example, in some cases, two blocks shown in succession may be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on the functionality involved. Also, other blocks may be added in addition to the blocks noted in a flowchart or block diagram.

[0068]

[0075] Various illustrative examples describe components that perform actions or steps. In an illustrative embodiment, a component may be configured to perform the described actions or tasks. For example, the component's structural configuration or design may provide the component with the capability to perform the actions or tasks described in the illustrative examples as being performed by the component.

[0069]

[0076] Numerous modifications and variations will be apparent to those skilled in the art. Furthermore, various exemplary embodiments may offer different features as compared to other exemplary embodiments. The selected embodiment or embodiments have been chosen and described in order to best explain the principles and practical applications of the embodiments and to facilitate others skilled in the art in understanding the disclosure of the various embodiments and various modifications suitable for the particular applications contemplated.

Claims

1. A computer-implemented method (500) for aircraft maintenance, comprising: monitoring sections and components of the aircraft (402) using sensors; detecting an anomaly (404, 502) within the aircraft by the sensor; In response to said detection, transmitting real-time data (406, 504) from said sensor to a computer having an artificial intelligence (AI) algorithm; analyzing the data with the AI ​​algorithm (412, 528) to identify characteristics of the anomaly; and In response to said identifying, transmitting a report (406, 504) to a pilot and to said computer.

2. The method of claim 1 , wherein the computer (104, 110) is on the ground, in the cloud, on the aircraft, or in a satellite.

3. The method of claim 1 , wherein the AI ​​algorithm (412, 528) utilizes the real-time data and historical information to identify the characteristics of the anomaly.

4. 10. The method of claim 1, wherein the computer includes at least one of augmented reality (AR), mixed reality (MR), and digital twin technology to assist in the analysis and interpretation of the anomaly.

5. utilizing said AI algorithm to analyze said report to determine the availability of a smart hangar (506) for aircraft inspection; and The method of claim 1 , further comprising determining a maintenance schedule (508, 510) for the aircraft based on the availability of the smart hangar.

6. The method of claim 5, further comprising preparing the smart inspection tool (508, 510) for deployment based on the determined availability of the smart hangar.

7. 10. The method of claim 1, wherein the sensor (210) comprises at least one of a piezoelectric sensor, a microelectromechanical system (MEMS), a light detection and ranging (LIDAR), an electromagnetic (EM) wave sensor, a camera, a shearography sensor, and a nanorobot.

8. The method of claim 1 , wherein the characteristics of the anomaly include at least one of a type, a location, a size, and a severity of the detected anomaly.

9. A system (100) for aircraft maintenance, comprising: a storage device (108) configured to store program instructions; one or more processors (106, 112) operatively connected to the storage device to execute the program instructions to provide the system with: monitoring sections and components of the aircraft (402) using sensors; detecting an anomaly (404, 502) within the aircraft by the sensor; In response to said detection, transmitting real-time data (406, 504) from said sensor to a computer having an artificial intelligence (AI) algorithm; analyzing the data with the AI ​​algorithm (412, 528) to identify characteristics of the anomaly; and and transmitting a report (406, 504) to a pilot and to the computer in response to the identification.

10. The system of claim 9 , wherein the computer (104, 110) is on the ground, in the cloud, on the aircraft, or in a satellite.

11. 10. The system of claim 9, wherein the AI ​​algorithm (412, 528) utilizes the real-time data and historical information to identify the characteristics of the anomaly.

12. 10. The system of claim 9, wherein the computer includes at least one of augmented reality (AR), mixed reality (MR), and digital twin technology to assist in the analysis and interpretation of the anomaly.

13. The program instructions may include: utilizing said AI algorithm to analyze said report to determine the availability of a smart hangar (506) for aircraft inspection; and 10. The system of claim 9, further comprising: determining a maintenance schedule (508, 510) for the aircraft based on the availability of the smart hangar.

14. The system of claim 13 , wherein the program instructions cause the system to prepare smart inspection tools (508, 510) for deployment based on the determined availability of smart storage.

15. 10. The system of claim 9, wherein the sensor comprises at least one of a piezoelectric sensor, a micro-electro-mechanical system (MEMS), an electromagnetic (EM) wave sensor, a camera, a shearography sensor, and a light detection and ranging (LIDAR).

16. The system of claim 9 , wherein the characteristics of the anomaly include at least one of a type, a location, a size, and a severity of the detected anomaly.

17. A computer program product (622) for aircraft maintenance, comprising: a computer-readable storage medium (622) having program instructions embodied therein, the program instructions comprising: monitoring areas and sections of the aircraft (402) using sensors; detecting an anomaly (404, 502) within the aircraft by the sensor; In response to said detection, transmitting real-time data (406, 504) from said sensor to a computer having an artificial intelligence (AI) algorithm; analyzing the data with the AI ​​algorithm (412, 528) to identify characteristics of the anomaly; and In response to said identifying, sending a report (406, 504) to a pilot and to said computer.

18. 20. The computer program product of claim 17, wherein the computer (104, 110) is on the ground, in the cloud, in the aircraft, or in a satellite.

19. utilizing said AI algorithm (412, 528) to analyze said report to determine the availability of a smart hangar for aircraft inspection; and 20. The computer program product of claim 17, further comprising instructions for executing: determining a maintenance schedule (508, 510) for the aircraft based on the availability of the smart hangar.

20. 20. The computer program product of claim 17, wherein the computer includes at least one of augmented reality (AR), mixed reality (MR), and digital twin technology to assist in the analysis and interpretation of the anomaly.

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