U.S. patent application number 15/042647 was filed with the patent office on 2017-08-17 for aircraft maintenance and inspection with data analytics enhancement.
The applicant listed for this patent is The Boeing Company. Invention is credited to Fong Shi.
Application Number | 20170236075 15/042647 |
Document ID | / |
Family ID | 57868042 |
Filed Date | 2017-08-17 |
United States Patent
Application |
20170236075 |
Kind Code |
A1 |
Shi; Fong |
August 17, 2017 |
AIRCRAFT MAINTENANCE AND INSPECTION WITH DATA ANALYTICS
ENHANCEMENT
Abstract
A system and method for analyzing aircraft operation and
maintenance history in light of maintenance reports, aircraft type
system failure history, health management reports, and aircraft
exterior structural conditions to produce a real time
recommendation regarding aircraft maintenance and dispatch. A
visual inspection report is generated by capturing a real time
image of an exterior of the aircraft, inverting the real time
image, superimposing the real time image on a previous image of the
aircraft exterior, and detecting any variation on the combination
of superimposed inverted image on the previous image of the
aircraft exterior. Aircraft type historical systems failure reports
are analyzed to identify any predictive latent system failure of
the aircraft based on the aircraft type historical systems failure
report and aircraft operation history and maintenance history.
Inventors: |
Shi; Fong; (Bellevue,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Boeing Company |
Chicago |
IL |
US |
|
|
Family ID: |
57868042 |
Appl. No.: |
15/042647 |
Filed: |
February 12, 2016 |
Current U.S.
Class: |
701/31.4 |
Current CPC
Class: |
G06T 7/001 20130101;
G07C 5/006 20130101; G06Q 10/0631 20130101; G06T 2207/20221
20130101; G06T 7/0008 20130101; G06T 2207/20081 20130101; G06T
2207/30252 20130101; G06N 20/00 20190101; G06Q 10/10 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06T 7/00 20060101 G06T007/00; G06N 99/00 20060101
G06N099/00; G07C 5/00 20060101 G07C005/00 |
Claims
1. A method for making an aircraft dispatch decision comprising the
steps of: collecting a plurality of aircraft information selected
from the group consisting of maintenance bulletins for the
aircraft, repair and maintenance history of the aircraft, a health
management report of the aircraft, a structural health management
report of the aircraft, an operation history of the aircraft, and a
visual inspection report of an exterior of the aircraft; analyzing
the plurality of aircraft information to detect whether there is a
structural or system failure of the aircraft and identify any
immediate or near term bulletin maintenance requirement, or a
combination thereof; generating an analysis report comprising
results from the analyzing step; documenting the analysis report on
a maintenance record for the aircraft; and making an aircraft
dispatch decision based on the analysis report, the dispatch
decision comprising decision information selected from the group
consisting of maintenance action, flight cancellation, flight crew
notification, maintenance engineering notification, departure
approval, and grounding action.
2. The method of claim 1 wherein the visual inspection report is
generated by capturing a real time image of an exterior of the
aircraft and comparing the real time image with a previous image of
the aircraft exterior using digital processing techniques selected
from the group consisting of contrast enhancement, compression,
noise filtering, edge detection, intensity equalization, gray-level
differences, texture-tone analysis, shape and features analysis,
color histogram, and pixel-based identity checks.
3. The method of claim 2 wherein the visual inspection report is
generated by capturing a real time image of an exterior of the
aircraft, inverting the real time image, superimposing the real
time image on a previous image of the aircraft exterior, and
detecting any variation on a combination of superimposed inverted
image on the previous image of the aircraft exterior.
4. The method of claim 1 wherein the analyzing step further
comprises the step of correlating a real time image of an exterior
of the aircraft and a previous image of the aircraft exterior with
other aspects of the plurality of aircraft information.
5. The method of claim 1 wherein the step of making an aircraft
dispatch decision further comprises the step of performing machine
translation and text segmentation on the plurality of aircraft
information.
6. The method of claim 1 wherein the step of making an aircraft
dispatch decision further comprises the step of analyzing results
of a cumulative learning process.
7. The method of claim 6 wherein the cumulative learning process
analyzes known sets of accurate aircraft data, test sets of
aircraft data, and probabilities of expected outputs.
8. The method of claim 7 wherein an output of the cumulative
learning process classifies aircraft systems.
9. The method of claim 1 wherein: the plurality of aircraft
information further comprises an aircraft type historical systems
failure report; and the analyzing step further comprises
identifying any predictive latent system failure of the aircraft
based on the aircraft type historical systems failure report and
aircraft operation and maintenance history.
10. The method of claim 9 wherein the making an aircraft dispatch
decision step further comprises analyzing information from a
plurality of aircraft of the same aircraft type in a fleet.
11. The method of claim 1 wherein the collecting step and analyzing
step are performed by a computer comprising hardware and
software.
12. A system for making an aircraft dispatch decision comprising: a
database for storing a plurality of aircraft information selected
from the group consisting of maintenance bulletins for the
aircraft, repair and maintenance history of the aircraft, a health
management report of the aircraft, a structural health management
report of the aircraft, an operation history of the aircraft, and a
visual inspection report of an exterior of the aircraft; an
analysis module for: analyzing the plurality of aircraft
information to detect whether there is a structural or system
failure of the aircraft and identify any immediate or near term
bulletin maintenance requirement, or a combination thereof;
generating an analysis report comprising results from the analyzing
step; and making an aircraft dispatch decision based on the
analysis report, the dispatch decision comprising decision
information selected from the group consisting of maintenance
action, flight cancellation, flight crew notification, maintenance
engineering notification, departure approval, and grounding
action.
13. The system of claim 12 wherein the visual inspection report is
generated by capturing a real time image of an exterior of the
aircraft and comparing the real time image with a previous image of
the aircraft exterior using digital processing techniques selected
from the group consisting of contrast enhancement, compression,
noise filtering, edge detection, intensity equalization, gray-level
differences, texture-tone analysis, shape and features analysis,
color histogram, and pixel-based identity checks.
14. The system of claim 12 wherein the visual inspection report is
generated by capturing a real time image of an exterior of the
aircraft, inverting the real time image, superimposing the real
time image on a previous image of the aircraft exterior, and
detecting any variation on a combination of superimposed inverted
image on the previous image of the aircraft exterior.
15. The system of claim 12 wherein a real time image of an exterior
of the aircraft and a previous image of the aircraft exterior are
correlated with other aspects of the plurality of aircraft
information.
16. The system of claim 12 wherein the plurality of aircraft
information is machine translated and text segmented.
17. The system of claim 12 further comprising a cumulative learning
module.
18. The system of claim 17 wherein the cumulative learning module
analyzes known sets of accurate aircraft data, test sets of
aircraft data, and probabilities of expected outputs.
19. The system of claim 18 wherein an output of the cumulative
learning module classifies aircraft systems.
20. The system of claim 12 wherein: the plurality of aircraft
information further comprises an aircraft type historical systems
failure report; and the analysis report identifies any predictive
latent system failure of the aircraft based on the aircraft type
historical systems failure report and aircraft operation and
maintenance history.
21. The system of claim 20 wherein the analysis module analyzes
information from a plurality of aircraft of the same aircraft type
in a fleet.
22. The system of claim 12 wherein the analysis module is a
computer comprising hardware and software.
Description
TECHNOLOGICAL FIELD
[0001] The present disclosure relates generally to aircraft
inspection and maintenance and, in particular, to an improved
system and method for using data analytics to optimize aircraft
inspections and maintenance.
BACKGROUND
[0002] In the aviation industry, aircraft inspections and
maintenance are typically scheduled after a period of operation
time or flight hours, or when predetermined flight cycles have been
reached. The inspection and maintenance processes are comprehensive
and ongoing. For example, certain aircraft components are replaced
upon reaching a maximum allowable usage, while other parts are
periodically checked for field failures or faulty performance.
Airlines and airworthiness authorities often refer to scheduled
inspections as A-check, B-check, C-check, or D-check, wherein
A-checks and B-checks are considered to be lighter checks, and
C-checks and D-checks are considered to be heavier checks. As
illustrated in FIG. 1, at an airport gate, for example, airline
crewmembers may conduct a ground inspection and assessment 102 of
parked aircraft. Such ground assessment, also known as an airport
ramp inspection, involves checks on airplane structure and system
components for visible damages caused by aging, wear and tear,
deterioration due to the environment, strikes by birds, lightening,
or foreign objects debris, etc. The procedure is performed in
accordance with flight crew operation manuals 104 including pilot
operation handbooks, regulatory updates 106, crewmember knowledge
108, and other information provided by the aircraft manufacturer
and operating airlines.
[0003] Typically, it is the responsibility of the airlines ground
crew to make a decision 110 on whether the aircraft is airworthy
before pushback for departure. In preparation for flight, items on
a checklist, both interior and exterior, are inspected. Discovery
of any safety issues, and correction or deferment of such issues,
is essential for ensuring a safe flight. Since the Federal Aviation
Administration (FAA) adopted the operation concept of a minimum
equipment list (MEL), flights are allowed with certain inoperative
items, as long as such items are nonessential in flight. Thus,
within the FAA MEL guidelines, airlines may defer repairs of
certain nonessential equipment. For inoperative equipment found
during inspection, a decision is made as to whether the aircraft is
safe for departure 120, whether maintenance 112 is needed prior to
departure, whether flight can be made under the limitations imposed
by the FAA MEL rules regarding deferment 122, or whether the flight
needs to be cancelled 114. The flight crew and maintenance
engineering crews are notified of the status 116, 118, and all
information is documented in log books 124 and maintenance records
126.
[0004] With the above-described inspection and maintenance
processes, on-the-spot flight safety decisions heavily depend on
the skills, knowledge, experience and level of training received by
the individuals of the airline crewmembers who are working around
the aircraft to perform ground inspections. During the inspections,
airplane instrument, sensors, probes, brakes, pressure vents,
engine blades, fluid level, cracks, and corrosions, etc., are
subject to crewmembers' interpretations. Additionally, some
inspections are conducted at night or at other times during poor
visibility, which increases the likelihood that a safety issue
could be misinterpreted or misjudged during inspection. Examples
include obstructed probes by insect nests, worn-out brake pins,
partially shut vents, twisted engine blades, fluid leaks,
lightening strikes having caused holes, cracks, or dents on top of
fuselage, corrosion of antennas, lost of fasteners, and color of
the over pressure relief caps, etc. Inevitably, there have been
reported incidents traced back to errors and improper judgment by
humans.
[0005] In some newer and more advanced aircraft, the aircraft
include onboard real-time health management systems, referred to as
Airplane Health Management (AHM) systems, which enable fleet-wide
monitoring of the aircraft while in flight. AHM systems give
airlines additional capabilities to monitor onboard systems and
components, thereby enabling efficient fleet operations for the
airlines with optimized flight planning and flight management. For
example, AHM systems aggregate the data, identify potential issues,
update maintenance manuals, and provide service and repair related
information supplied by aircraft manufacturer systems and
engineering experts. AHM summary reports can be sent via to ground
stations then routed to airline operation centers for processing.
Data going through such channels can be costly to some airlines if
the amount becomes huge and the speed of data transmissions can be
an issue. As an alternative, AHM summary reports can also be stored
on a recorder for later retrieval at the airport either wirelessly
or manually.
[0006] With onboard and off-board broadband connectivity, large
volumes of accumulated aviation service data are becoming available
for airlines to assist fleet maintenance, airplane inspections, and
repairs on the ground. Airline crewmembers may have access to this
database through an internet infrastructure or cloud-based servers
to assist their decision-making. Predictive analytics or other
machine-assisted methods are capable of extracting true values from
volumes of data. Accurate processing of such big data leads to
confident decisions, and better decisions means greater operational
efficiency and reduction of costs and risks for the airlines. With
data analytics enhanced inspection and maintenance, airline crew
are able to assess the situation in real time and make correct
decisions on-the-spot, thereby enhancing the safety of the flying
public, the crewmembers, and the airplanes.
[0007] One downside of the above-described AHM system is that it
does not combine and integrate all relevant in-service maintenance
data among an aircraft manufacturer's aircraft portfolios. Rather,
airlines typically keep their fleet inspection results and
maintenance data from each other. Unless major incidences occur
that operators must report per FAA rules, aircraft maintenance data
is only available on a voluntary basis. Due to geographical
diversity and differences in culture, languages and data format,
current maintenance records of inspections and repairs performed by
worldwide fleet on the ground are not integrated with the aircraft
manufacturer's AHM system including data collected in-flight. For
non-English speaking countries, airlines maintenance logs are often
recorded in foreign languages, which is also incompatible with an
AHM system written in English. Thus, using the traditional methods,
airlines take time, in some case up to a week, to make necessary
actions.
[0008] Thus, it is desirable to have an improved system and method
for integrating and analyzing the contents collected from ground
maintenance and inspection with an onboard AHM system to improve
accuracy with diagnosis of safety issues and timeliness of the
appropriate responses to such issues.
BRIEF SUMMARY
[0009] In view of the foregoing background, example implementations
of the present disclosure provide a system and method for method
for making an aircraft dispatch decision including the step of
collecting a plurality of aircraft information in the system, such
as maintenance bulletins for the aircraft, repair and maintenance
history of the aircraft, a health management report of the
aircraft, a structural health management report of the aircraft, an
operation history of the aircraft, and a visual inspection report
of an exterior of the aircraft. The visual inspection report is
generated by capturing a real time image of an exterior of the
aircraft, inverting the real time image, superimposing the real
time image on a previous image of the aircraft exterior, and
detecting any variation on a combination of superimposed inverted
image on the previous image of the aircraft exterior. The visual
inspection report can also be generated by capturing a real time
image of an exterior of the aircraft and comparing the real time
image with a previous image of the aircraft exterior using digital
processing techniques such as contrast enhancement, compression,
noise filtering, edge detection, intensity equalization, gray-level
differences, texture-tone analysis, shape and features analysis,
color histogram, and pixel-based identity checks. The real time and
previous images of the exterior of the aircraft are correlated with
other aspects of the aircraft information, which may be machine
translated and text segmented.
[0010] The method for making an aircraft dispatch decision further
includes the step of the system analyzing the aircraft information
to detect whether there is a structural or system failure of the
aircraft and identify any immediate or near term bulletin
maintenance requirement, or a combination thereof. An analysis
report is generated based on results from the analyzing step, and
the analysis report is documented on a maintenance record for the
aircraft. The method further includes the step of the system making
an aircraft dispatch decision based on the analysis report. The
dispatch decision may include actions such as maintenance action,
flight cancellation, flight crew notification, maintenance
engineering notification, departure approval, and grounding
action.
[0011] In additional implementations of the present disclosure, the
plurality of aircraft information further includes an aircraft type
historical systems failure report, and the analyzing step further
includes identifying any predictive latent system failure of the
aircraft based on the aircraft type historical systems failure
report and aircraft operation and maintenance history. The step of
making an aircraft dispatch decision may include the step of
analyzing the results of a cumulative learning process, which
analyzes known sets of accurate aircraft data, test sets of
aircraft data, and probabilities of expected outputs. The output of
the cumulative learning module may classify the aircraft systems
and the aircraft dispatch decision step may be further based on
analysis of information from a plurality of aircraft of the same
aircraft type in a fleet.
[0012] The system for implementing the foregoing-described steps
comprises both hardware and software.
[0013] The features, functions and advantages discussed herein may
be achieved independently in various example implementations or may
be combined in yet other example implementations, further details
of which may be seen with reference to the following description
and drawings.
BRIEF DESCRIPTION OF THE DRAWING(S)
[0014] Having thus described example implementations of the
disclosure in general terms, reference will now be made to the
accompanying drawings, which are not necessarily drawn to scale,
and wherein:
[0015] FIG. 1 is a block diagram of a prior art system for making
aircraft field service decisions;
[0016] FIG. 2 is a block diagram of a system for making aircraft
field service decisions in accordance with an example
implementation of the present disclosure;
[0017] FIG. 3 is a block diagram of data analytics module used in a
system for making aircraft field service decisions in accordance
with an example implementation of the present disclosure;
[0018] FIG. 4 is a block diagram of aircraft production and service
methodology; and
[0019] FIG. 5 is a schematic illustration of an aircraft.
DETAILED DESCRIPTION
[0020] Some implementations of the present disclosure will now be
described more fully hereinafter with reference to the accompanying
drawings, in which some, but not all implementations of the
disclosure are shown. Indeed, various implementations of the
disclosure may be embodied in many different forms and should not
be construed as limited to the implementations set forth herein;
rather, these example implementations are provided so that this
disclosure will be thorough and complete, and will fully convey the
scope of the disclosure to those skilled in the art. For example,
unless otherwise indicated, reference something as being a first,
second or the like should not be construed to imply a particular
order. Also, something may be described as being above something
else (unless otherwise indicated) may instead be below, and vice
versa; and similarly, something described as being to the left of
something else may instead be to the right, and vice versa. Like
reference numerals refer to like elements throughout.
[0021] Example implementations of the present disclosure will be
primarily described in conjunction with aviation applications. It
should be understood, however, that example implementations may be
utilized in conjunction with a variety of other applications, both
in the aviation industry and outside of the aviation industry.
[0022] According to example implementations of the present
disclosure, an improved system is provided for integrating and
analyzing data collected from ground-based maintenance and
inspection with an onboard AHM system to improve accuracy with
diagnosis of safety issues and timeliness of the appropriate
responses to such issues.
[0023] As illustrated in FIGS. 2 and 3, in the system and method of
the present disclosure, in-service maintenance data and in-flight
data are processed at the same time at the same location, with the
help of knowledge sharing among all airlines flying the same type
airplane elsewhere. The proposed method enables airlines to
diagnose safety issues and initiate necessary actions within hours,
rather than the weeks required by those traditional analysis
methods as referenced above with respect to FIG. 1, thereby
improving safety while saving time, fuel, resources, and cost.
[0024] On the ground, with the worldwide internet infrastructure
such as corporate networks and/or cloud based services, airlines
are be able to perform advanced data analytics by accessing the
aircraft manufacturer's AHM system through, for example, a
web-based portal. This allows airline maintenance crew and
technicians to conduct and perform more advanced and complete data
analytics on the spot. This integrated approach enhances
diagnostics and prognostics much more effectively. Thus, the
airline can make better and faster correct-fix-or-fly decisions and
reduce flight delays, air turn-backs, and diversions before
pushback of an airplane at the gate.
[0025] Referring more specifically now to FIG. 2, the system and
method of the present disclosure also incorporates a ground
inspection 250 of the parked aircraft, similar to the ground
inspection and assessment 102, which are conducted by crewmembers.
Such ground assessment is performed in accordance with flight crew
operation manuals 204 including pilot operation handbooks,
regulatory updates 206, crewmember knowledge 208, and other
information provided by the aircraft manufacturer and operating
airlines.
[0026] The present system and method, however, further incorporates
intelligence from imaging of the aircraft. Specifically, the
crewmembers may capture digital images of the aircraft 230 and use
known technology to greatly enhance the images 232. Such images are
digitally compared 240 to a database of existing images relevant to
the aircraft. The images in the database may be searched 234 and
may include images of the aircraft taken when the aircraft was
placed in service, at the time of installation 238 or replacement
of certain parts, or after previous flights 236. The digital
comparison of the present and past images helps the crewmembers to
determine whether or how much a particular part of the aircraft has
changed. For high-definition and/or infrared images, the processing
and comparison may involve determining the similarity of two sets
of data through contrast enhancement, compression, noise filtering,
edge detection, intensity equalization, gray-level differences,
texture-tone analysis, shape and features analysis, color
histogram, and/or pixel-based identity checks. Techniques such as
image inversion and superposition may also be employed. Intensity
equalization can be applied before inverting an image by adding an
inverted image onto the image to be compared. If the superposition
results of two data sets are net zero, or near zero, then there is
no or virtually no difference between the two images. Stated
another way, a signature of an object may be inverted and then
added to its previous signature of the same object. If the two
signals cancel each other, there is no change in the object. Other
techniques such as color histograms, numerical weighs assignment,
and math processing such as Fourier transfer and cosine inversion
can also be implemented. Texture measures may consider
co-occurrence matrices, gray-level differences, texture-tone
analysis, and features derived from the Fourier spectrum, and Gabor
filters.
[0027] The system and method of the present disclosure has an
additional ability to align the images 242 in the image database
with text, such as text from the flight crew operations manuals,
maintenance manuals, and service updates. As explained in more
detail below, a module 300 for assessing, analyzing, and making a
decision on the health of the aircraft factors in the ground-based
inspection 250, the image comparisons 240, the text, documents, and
drawings that are aligned with the images 242, 246, and voice input
244. The assessment, analytics, and decision module 300 ultimately
makes recommendations on airworthiness. Particularly, for
inoperative equipment found during inspection, a decision is made
as to whether the aircraft is safe for departure 220, whether
maintenance is needed prior to departure 212, whether flight can be
made under the limitations imposed by the FAA MEL rules regarding
deferment 222, or whether the flight needs to be cancelled 214. The
flight crew and maintenance engineering crews are notified of the
status 216, 218, and all information is documented in log books 224
and maintenance records 226.
[0028] Referring now to FIG. 3, the assessment, analytics, and
decision module 300 is depicted in more detail. The module 300
input includes the documents, voice, images and symbols 302 that
are referenced in FIG. 2. The module 300 also recognizes and aligns
such text, graphics, photos, and videos 304 as described above with
respect to FIG. 2. The module 300 further provides machine
translation and text segmentation functions 306 based on a corpus
of aviation data 308 including expert database and translation
memory.
[0029] The module 300 of the system and method of the present
disclosure incorporates machine learning with acquired new
knowledge and experience through a process of cumulative learning
310. The continual learning process may include training sets 318
with known good sets of data, actual case input 320, and
probabilities of expected output 322.
[0030] An output of the cumulative learning process 310 is a
machine classification 312 of onboard systems to which particular
objects 314 belong. For example, lighting is classified under the
electrical system, brakes are classified under the
structural/mechanical system, and temperature is classified under
the environmental system.
[0031] Decision making, diagnostics and prognostics are preformed
by a statistic analysis engine 316 based on knowledge of the
subject aircraft and all aircraft of the same type. Fleet
information may include data about factory installation of objects,
prior field service data, and health monitoring data. Knowledge
gained from corrective actions is the input to the cumulative
learning module 310. This approach enhances aircraft
diagnostics/prognostics much more effectively and accurately with
fully interpreted information available. The decision making data
is provided to crewmembers using broadband connectivity to assist
them with making the right decisions during inspections of the
aircraft.
[0032] The system and method as described above better equips
crewmembers with information needed to make decisions regarding the
health of an aircraft, even while performing a ground-based
inspection. This decision-making is aided by the ability to
download the latest regulatory changes, bulletin notices, reference
documents, drawings, and images into the system, while also
streaming videos or apps to assist with the decision-making
process. The system performs analytical algorithms for diagnostics
and verifications, and enables the uploading of images or video to
airlines operation centers and flight crew for good records
keeping. The system further enables electronic signatures for
logbooks. Thus, the system and method of this present disclosure is
optimized for both cost and performance benefit to airlines and
airports by aggregating aircraft maintenance data with AHM data
among a fleet's field and maintenance services portfolios. This
provides for more advanced and complete data analytics and enables
crew members to identify and address potential aircraft health
issues long before the prior art methods.
[0033] According to example implementations of the present
disclosure, the various components of the improved system and
method of the present disclosure may be implemented by various
means including hardware, alone or under direction of one or more
computer program code instructions, program instructions or
executable computer-readable program code instructions from a
computer-readable storage medium.
[0034] In one example, one or more apparatuses may be provided that
are configured to function as or otherwise implement the system and
method and respective elements shown and described herein. In
examples involving more than one apparatus, the respective
apparatuses may be connected to or otherwise in communication with
one another in a number of different manners, such as directly or
indirectly via a wireline or wireless network or the like.
[0035] Generally, an apparatus of exemplary implementation for the
present disclosure may include one or more of a number of
components such as a processor (e.g., processor unit) connected to
a memory (e.g., storage device), as described above. The processor
is generally any piece of hardware that is capable of processing
information such as, for example, data, computer-readable program
code, instructions or the like (generally "computer programs,"
e.g., software, firmware, etc.), and/or other suitable electronic
information. More particularly, for example, the processor may be
configured to execute computer programs, which may be stored
onboard the processor or otherwise stored in the memory (of the
same or another apparatus). The processor may be a number of
processors, a multi-processor core or some other type of processor,
depending on the particular implementation. Further, the processor
may be implemented using a number of heterogeneous processor
systems in which a main processor is present with one or more
secondary processors on a single chip. As another illustrative
example, the processor may be a symmetric multi-processor system
containing multiple processors of the same type. In yet another
example, the processor may be embodied as or otherwise include one
or more application-specific integrated circuits (ASICs),
field-programmable gate arrays (FPGAs) or the like. Thus, although
the processor may be capable of executing a computer program to
perform one or more functions, the processor of various examples
may be capable of performing one or more functions without the aid
of a computer program.
[0036] The memory is generally any piece of hardware that is
capable of storing information such as, for example, data, computer
programs and/or other suitable information either on a temporary
basis and/or a permanent basis. The memory may include volatile
and/or non-volatile memory, and may be fixed or removable. Examples
of suitable memory include random access memory (RAM), read-only
memory (ROM), a hard drive, a flash memory, a thumb drive, a
removable computer diskette, an optical disk, a magnetic tape or
some combination of the above. Optical disks may include compact
disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W),
DVD or the like. In various instances, the memory may be referred
to as a computer-readable storage medium which, as a non-transitory
device capable of storing information, may be distinguishable from
computer-readable transmission media such as electronic transitory
signals capable of carrying information from one location to
another. Computer-readable medium as described herein may generally
refer to a computer-readable storage medium or computer-readable
transmission medium.
[0037] In addition to the memory, the processor may also be
connected to one or more interfaces for displaying, transmitting
and/or receiving information. The interfaces may include a
communications interface (e.g., communications unit) and/or one or
more user interfaces. The communications interface may be
configured to transmit and/or receive information, such as to
and/or from other apparatus(es), network(s) or the like. The
communications interface may be configured to transmit and/or
receive information by physical (wireline) and/or wireless
communications links. Examples of suitable communication interfaces
include a network interface controller (NIC), wireless NIC (WNIC)
or the like.
[0038] The user interfaces may include a display and/or one or more
user input interfaces (e.g., input/output unit). The display may be
configured to present or otherwise display information to a user,
suitable examples of which include a liquid crystal display (LCD),
light-emitting diode display (LED), plasma display panel (PDP) or
the like. The user input interfaces may be wireline or wireless,
and may be configured to receive information from a user into the
apparatus, such as for processing, storage and/or display. Suitable
examples of user input interfaces include a microphone, image or
video capture device, keyboard or keypad, joystick, touch-sensitive
surface (separate from or integrated into a touchscreen), biometric
sensor or the like. The user interfaces may further include one or
more interfaces for communicating with peripherals such as
printers, scanners or the like.
[0039] As indicated above, program code instructions may be stored
in memory, and executed by a processor, to implement functions of
the system. As will be appreciated, any suitable program code
instructions may be loaded onto a computer or other programmable
apparatus from a computer-readable storage medium to produce a
particular machine, such that the particular machine becomes a
means for implementing the functions specified herein. These
program code instructions may also be stored in a computer-readable
storage medium that can direct a computer, a processor or other
programmable apparatus to function in a particular manner to
thereby generate a particular machine or particular article of
manufacture. The instructions stored in the computer-readable
storage medium may produce an article of manufacture, where the
article of manufacture becomes a means for implementing functions
described herein. The program code instructions may be retrieved
from a computer-readable storage medium and loaded into a computer,
processor or other programmable apparatus to configure the
computer, processor or other programmable apparatus to execute
operations to be performed on or by the computer, processor or
other programmable apparatus.
[0040] Retrieval, loading and execution of the program code
instructions may be performed sequentially such that one
instruction is retrieved, loaded and executed at a time. In some
example implementations, retrieval, loading and/or execution may be
performed in parallel such that multiple instructions are
retrieved, loaded, and/or executed together. Execution of the
program code instructions may produce a computer-implemented
process such that the instructions executed by the computer,
processor or other programmable apparatus provide operations for
implementing functions described herein.
[0041] Execution of instructions by a processor, or storage of
instructions in a computer-readable storage medium, supports
combinations of operations for performing the specified functions.
It will also be understood that one or more functions, and
combinations of functions, may be implemented by special purpose
hardware-based computer systems and/or processors which perform the
specified functions, or combinations of special purpose hardware
and program code instructions.
[0042] As referenced above, examples of the present disclosure may
be described in the context of aircraft manufacturing and service.
As shown in FIGS. 4 and 5, during pre-production, illustrative
method 500 may include specification and design (block 502) of
aircraft 602 and material procurement (block 504). During
production, component and subassembly manufacturing (block 506) and
system integration (block 508) of aircraft 602 may take place.
Thereafter, aircraft 602 may go through certification and delivery
(block 510) to be placed in service (block 512). While in service,
aircraft 602 may be scheduled for routine maintenance and service
(block 514). Routine maintenance and service may include
modification, reconfiguration, refurbishment, etc. of one or more
systems of aircraft 602.
[0043] Each of the processes of illustrative method 500 may be
performed or carried out by a system integrator, a third party,
and/or an operator (e.g., a customer). For the purposes of this
description, a system integrator may include, without limitation,
any number of aircraft manufacturers and major-system
subcontractors; a third party may include, without limitation, any
number of vendors, subcontractors, and suppliers; and an operator
may be an airline, leasing company, military entity, service
organization, and so on.
[0044] As shown in FIG. 5, aircraft 602 produced by illustrative
method 500 may include airframe 612 with a plurality of high-level
systems 600 and interior 614. Examples of high-level systems 600
include one or more of propulsion system 604, electrical system
606, hydraulic system 608, and environmental system 610. Any number
of other systems may be included. Although an aerospace example is
shown, the principles disclosed herein may be applied to other
industries, such as the automotive industry. Accordingly, in
addition to aircraft 602, the principles disclosed herein may apply
to other vehicles, e.g., land vehicles, marine vehicles, space
vehicles, etc.
[0045] Apparatus(es) and method(s) shown or described herein may be
employed during any one or more of the stages of the manufacturing
and service method 500. For example, components or subassemblies
corresponding to component and subassembly manufacturing 506 may be
fabricated or manufactured in a manner similar to components or
subassemblies produced while aircraft 602 is in service. Also, one
or more examples of the apparatus(es), method(s), or combination
thereof may be utilized during production stages 506 and 508, for
example, by substantially expediting assembly of or reducing the
cost of aircraft 602. Similarly, one or more examples of the
apparatus or method realizations, or a combination thereof, may be
utilized, for example and without limitation, while aircraft 602 is
in service, e.g., maintenance and service stage (block 514).
[0046] Different examples of the apparatus(es) and method(s)
disclosed herein include a variety of components, features, and
functionalities. It should be understood that the various examples
of the apparatus(es) and method(s) disclosed herein may include any
of the components, features, and functionalities of any of the
other examples of the apparatus(es) and method(s) disclosed herein
in any combination, and all of such possibilities are intended to
be within the spirit and scope of the present disclosure.
[0047] Many modifications and other implementations of the
disclosure set forth herein will come to mind to one skilled in the
art to which this disclosure pertains having the benefit of the
teachings presented in the foregoing descriptions and the
associated drawings. Therefore, it is to be understood that the
disclosure is not to be limited to the specific implementations
disclosed and that modifications and other implementations are
intended to be included within the scope of the appended claims.
Moreover, although the foregoing descriptions and the associated
drawings describe example implementations in the context of certain
example combinations of elements and/or functions, it should be
appreciated that different combinations of elements and/or
functions may be provided by alternative implementations without
departing from the scope of the appended claims. In this regard,
for example, different combinations of elements and/or functions
than those explicitly described above are also contemplated as may
be set forth in some of the appended claims. Although specific
terms are employed herein, they are used in a generic and
descriptive sense only and not for purposes of limitation.
* * * * *