Predictive aircraft fleet management

Abstract

Systems and devices for Disclosed herein are aspects of systems and methods to predictively maintain fleet aircraft including collecting flight data on at least flight systems, GPS, and engine systems from a plurality of the same make and model training aircraft during the same time period then provide said collected data to fleet management server. METAR data corresponding to the flight time and location of each flight is also provided to said server. The data may be normalized for any known deviations of aircraft in the fleet and, wherein the fleet server is configured to use the normalized data and generate predictive alerts on one or more aircraft in fleet.

Description

[0001] PATENT APPLICATION

[0002] FOR

[0003] PREDICTIVE AIRCRAFT FLEET MANAGEMENT

[0004]

[0001] This International PCT Application claims the benefit of priority United States (U.S.) Provisional Patent Application Serial No.: 63 / 575,306, filed April 5, 2024, and entitled “PREDICTIVE AIRCRAFT FLEET MANAGEMENT”, the disclosure of which is hereby incorporated by this reference as if fully set forth herein.

[0005] BACKGROUND OF THE DISCLOSURE

[0006]

[0002] FIELD OF THE DISCLOSURE

[0007]

[0003] The present disclosure relates aircraft fleets, more particularly, to systems and methods for predictively managing aspects of fleets of similar aircraft.

[0008]

[0004] RELATED ART

[0009]

[0005] In modern "glass cockpit" aircraft several multi -function displays (MFD) driven by flight management systems FMS which can be adjusted to display flight information as needed. Such displays include Primary Flight Display (PFD) & the Multi-Function Display (MFD). An array of sensors, including but not limited to air data computers with input from static-pitot system and outside air temperature (OAT) sensors, attitude and heading reference system which may include accelerometers and magnetometers. GPS, autopilot systems and angle of attack indicators. Simulator which are ground based systems to teach are well known.

[0010]

[0006] A fleet for training pilots and / or a fleet of non-commercial aircraft, especially light sport aircrafts may not be FAA certified. Such a fleet will be subject to minimum inspections based on at least hours and usage. Fulfilling all maintenance / inspection service required requirements may leave gaps wherein a service is or should be considered. Modem aircraft have one or more microprocessors in major operating systems.

[0011]

[0007] The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. [0081 Therefore it is a desideratum to have methods and systems to predictively manage service and maintenance for one or more aircrafts in a fleet.

[0012]

[0009] DESCRIPTION

[0013]

[0010] To normalize objective metric measures necessary to compare results orient training, a positive gamification feedback is disclosed. Aircraft are normalized on a dynamic basis before , during and / or after each flight, having substantially the same aircraft deployed for each instructor and student. Any variation in said aircraft in a fleet are normalized via the collection of fleet data which is compared and valued to adjust for any anomalies between aircraft. Modem training aircraft have a number of flight systems, engine and balance of plant sensors and alarms for exceeding thresholds. [OU] Disclosed herein are aspects of systems and methods to predictively maintain fleet aircrafts including collecting flight data on at least flight systems, GPS, and engine systems from a plurality of the same make and model training aircraft during the same time period then provide said collected data to fleet management server. MET AR data corresponding to the flight time and location of each flight is also provided to said server. The data may be normalized for any known deviations of aircraft in the fleet and, wherein the fleet server is configured to use the normalized data and generate predictive alerts on one or more aircraft in fleet.

[0014]

[0012] In some instances data collection is via one or more of, radio and wireless signal communications. In some instances wireless signal communications includes one or more of near field, WIFI, LTE,4G, 5G and satellite.

[0015]

[0013] In some instances the server uses at least the MET AR data corresponding to the flight time and location to decide if collected data meets the alert or alarm threshold. In some instances predictive alert decisioning is based at least on difference below the alarm setting on at least one of a flight systems and engine systems . In some instances predictive alert is more restrictive threshold than the alarm settings on flight system or engine systems. In some instances predictive alert is based, on data collected from two or more aircrafts. In some instances predictive alert is based on data collected from a plurality of aircrafts.

[0016]

[0014] FIGURES

[0015] The invention may be better understood by referring to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. In the figures, like reference numerals designate corresponding parts throughout the different views.

[0017]

[0016] FIGS. 1 illustrates aspects of ground traffic activity at an airport and aircraft within the ground traffic.

[0018]

[0017] FIG. 2 illustrates aspects of glide slope landing.

[0019]

[0018] FIG. 3 illustrates aspects of air traffic air patterns.

[0020]

[0019] FIG. 4 is a system block diagram of an example of an implementation of system for providing flight data collected during a specific flight to a network.

[0021]

[0020] FIG. 5 is a system block diagram of aspects of an exemplary implementation of the one or more servers of the system of fleet management and instructor training.

[0022]

[0021] FIG. 6 aircraft data upload download also in air.

[0023]

[0022] FIG. 7 aircraft data upload download on ground.

[0024]

[0023] FIG. 8 is a system block diagram of aspects of an exemplary implementation of the one or more servers of the system of fleet management and instructor training.

[0025]

[0024] FIG. 9 is a system block diagram of aspects of an exemplary implementation of the fleet management system server.

[0026]

[0025] FIG. 10 shows aspects of a simplified electrical diagram.

[0027]

[0026] FIG. 11 shows aspects of an avionics system for a single engine aircraft.

[0028]

[0027] FIG. 12 is a table from a Pilot Display Interface Specification for a Rotax engine.

[0029]

[0028] All descriptions and callouts in the Figures and all content therein are hereby incorporated by this reference as if fully set forth herein.

[0030]

[0029] FURTHER DESCRIPTION

[0031]

[0030] The disclosed systems and methods are applicable to training aircraft which include, but are not limited to, digital instruments providing data feeds which can be stored in local databases during flight. The system include altimeter at least altimeters, tachometer, oil pressure, oil temperature, engine temperature, manifold pressure, airspeed indicator, vertical speed indicator, attitude indicator, heading indicator, turn coordinator and GPS Time, latitude, longitude, magnetic heading, GPS (altitude, time, date, ground speed, velocity, altitude, vertical airspeed speed, airspeed, pitch, roll, Lateral Acceleration (G), Acceleration (G), angle of attack (AO A) selected heading, selected altitude, barometer, communication frequency selected, second communication frequency selected, navigation frequency, outside air temperature (OAT), density altitude, height above ground, wind speed, wind direction, fuel, flaps, trim. Ideally autopilot information including but not limited to navigation distances, navigation course, navigation bearing.

[0032]

[0031] Local dashboard (dashcams) or positional cameras on fuselage, empennage, wings or and gear of the aircraft time stamped digital video or digital still photographs of aircraft movement and position at an airport, taxiway or runway can be acquired. . Such image acquisition can be constant during movement of the aircraft on the ground.

[0033]

[0032] In flight collection of system and subsystem performance during use by a specific human asset (instructor) and / or by specific students is configured to normalize aircraft in a fleet to normalize difference known between the same model aircraft in the fleet. In some instances such machine asset collected data from a fleet of substantially similar aircraft shows anomalies which are below a threshold which would trigger a warning sensor from the onboard flight instruments and engine systems but which can impact student performance or skew student performance and therefore impact instructor effectiveness is not normalized.

[0034]

[0033] In some instances machine asset data collected in real time may be used by fleet control to intervene in a predictive fashion before a machine asset fails or may become dangerous . In some instances machine asset data collected in real time may be used by fleet control to service or have a maintenance check on an aircraft in advance of regular required maintenance in a predictive fashion before a machine asset fails or may become dangerous. Additionally, such data is analyzed to determine if a corrective action or predictive alert extending beyond a single aircraft to other machine assets in the fleet may be appropriate. [0341 It is appreciated by those skilled in the art that the circuits, components, modules, and / or devices in this disclosure are described as being in “signal communication” with each other, where signal communication refers to any type of communication and / or connection between the circuits, components, modules, and / or devices that allows a circuit, component, module, and / or device to pass and / or receive signals and / or information from another circuit, component, module, and / or device. The communication and / or connection may be along any “signal path” between the circuits, components, modules, and / or devices that allows signals and / or information to pass from one circuit, component, module, and / or device to another and includes wireless or wired signal paths. The signal paths may be physical such as, for example, conductive wires, electromagnetic wave guides, attached and / or electromagnetic or mechanically coupled terminals, semi-conductive or dielectric materials or devices, or other similar physical connections or couplings. Additionally, signal paths may be nonphysical such as free-space (in the case of electromagnetic propagation) or information paths through digital components where communication information is passed from one circuit, component, module, and / or device to another in varying digital formats without passing through a direct electromagnetic connection.

[0035]

[0035] The computing devices / smart devices disclosed herein operate with memory and processors whereby code is executed during processes to transform data, the computing devices run on a processor (such as, for example, controller or other processor that is not shown) which may include a central processing unit (“CPU”), digital signal processor (“DSP”), application specific integrated circuit (“ASIC”), field programmable gate array (“FPGA”), microprocessor, etc. Alternatively, portions DCA devices may also be or include hardware devices such as logic circuitry, a CPU, a DSP, ASIC, FPGA, etc. and may include hardware and software capable of receiving and sending information.

[0036]

[0036] Figure 1 illustrates an overview of an airfield (which includes airstrips and airports both towered and un-towered) with a runway 10. Taxiways 11 are fluidly connected to the runway 12 and separated by a runway safety area 13. Ground traffic movement onto taxiways, across taxiways and onto and off of runways is controlled by control taxiway control markers 15. Adjacent to taxiway are run-up areas 16 for preparation before take-off. Additional areas around a runway include blast pads 17 which are not suitable for taxi, landing or take off. The runway visual threshold 20 is the start of the runway. In some instances displaced thresholds 22 will restricted a portion of a runway to only taxi and takeoff. Runway designators 24 are numerical indicators which identify the runway and the direction said runway faces. A runway center line 25 is provided for positioning. After runway designators aiming markings 26 are placed to give a distance from runway threshold indicator for pilots and other distance indicators 27 may also be present, the runway boundary 28 is also shown. Each taxiway has its own center line 30 for positioning the aircraft during taxi. Taxiways also have boundary markings 32 to provide areas that should not be crossed for safety to persons, property and the aircraft.

[0037]

[0037] During ground procedures the position and movement of the aircraft is highly regulated to avoid collisions , damage to persons and property and for safety in general. Local dashcams or positional cameras can capture images during ground procedures. Figure 1 illustrates a plurality of aircraft 40A-40G on the taxiway and runway. Each position of each aircraft provides an opportunity to measure a student performance metric. Non-limiting aspects of ground performance metrics "PM" include but are not limited position on a taxiway such as distance from the center line 30 and from boundary markings 32. Entering or exiting a taxiway or runways taxiway control markers 15, wait for clearance at appropriate taxiway movement control markers, entry and exit from run-up 16 area to runways 12 or back to taxiways 11. Movement prior to take off include proceeding past any restricted area blast areas 17 and runway safety areas 13. For example aircraft 40A has been maneuvered offset to the right of the taxiway center line 30 and almost crossing the boundary markings 32 which is not a preferred position and is scored lower than aircraft 40B which has been maneuvered on center line. However, aircraft 40B has been poorly maneuvered and allowed to cross the boundary markings 32 and that student and instructor is scored poorly and would require remedial education and / or alteration to teaching methodology for the instructor.

[0038]

[0038] Aircraft 40D is shown in two positions as it is maneuvered swerving along the centerline 30. The swerving, depending on the distance and magnitude is scored differently than the on center line taxi. Aircraft 40E is shown holding short at a taxiway control marker 15 and the position of the aircraft from the control marker can be measured and scored on proximity. Aircraft 40F is shown on landing or take off on the runway. In either case the student has ended up to the right of the runway center line 25 but within the runway boundary 28. If aircraft 40F was landing then the student's actual landing position from the aiming markings 26 , for example, could be an additional data points collected as a Performance Metric "PM" . In such a case the instructor or student could use the local recorder to identify the landing target which can be measured against the actual landing via GPS, visual camera or other means. Aircraft 40G is shown holding short of a taxiway control marking. Ground movement of the aircraft during a training interval wherein the CFI (instructor) is training the student can be demarcated by a local voice recording of training interval with appropriate time stamp wherein and Performance Metris "PM" is not considered a student metric but rather can be separately analyzed as a CFI metrics "CM" which may have bearing on teaching knowledge, skill and method.

[0039] Figure 2 shows and aircraft 50 on a glide slope indicated by line "B" . How the student maintains the glide slope or deviates above towards line "A" or below to line "C" are relevant performance metrics "PM" . Over time the student's PMs and the student instructor pairing (SIP) will show the trend of the student's PMs in each SIP this information is part of the training method and score system disclosed herein. Again if landings are during a training interval by identifying same as a training interval the metrics can be disregarded for the student performance but may be separately analyzed as CMs.

[0039]

[0040] Figure 3 illustrates aspects of a simplified airfield traffic pattern, flying in, entering and departing the pattern is a critical flight skill. Student pilot proficiency / skill in maneuvering within airfield pattern is critical to separation of aircraft and safety. Skills involved include adjusting attitude for wind, and managing altitude and flaps for controlled landing and controlled takeoff. Aircraft 52 is shown in a 2-dimensional view flying and operating within the ideal pattern 60. In the real world variation in position marked by line 62 will occur and the student will continue to be operating safety within the desired pattern , even further variation will occur within line 64. Our system collecting one or more of GPS, onboard instrument collection of data, local voice recording, and camera collects aircraft pattern information on position, speed, flap settings, altitude, glide slope, rate of decent or rate of climb on each flight with a SIP. Weather information and winds in particular including speed and direction are also provided to from Weather data and collected by the Training Module (see Figure 8) with appropriate date and time stamp. Again, if pattern work is during a training interval by identifying same as a training interval the metrics can be disregarded for the student performance but may be separately analyzed as CMs.

[0040]

[0041] Aircraft 54 is shown within variations which have been identified as acceptable pattern positioning. Said variations may be dynamically changed based on weather and wind information. Aircraft 55 is shown outside all variations plotted at the time of aircraft 55 flight and in the scoring and training module the student (and instructor (CFI)) performance metrics "PM" for the aircraft 55 after normalization for certain defined variables including but not limited to weather, student experience and aircraft suboptimal flight system(s) flight will be scored lower than the performance metrics "PM" for aircraft 54 (also normalized) which will be scored lower than the performance metrics "PM" (also normalized) for aircraft 52. It is within the scope of this disclosure that a SIP may, during pattern practice, have times wherein the aircraft is within the ideal position illustrated by aircraft 52 and then in less desirable locations shown by aircraft 54 and also in undesirable locations shown by aircraft 55. Configuring the system to capture performance metrics "PM" at a sufficiently high sampling rate reduces false negative by allowing the student time to correct minor deviations. The system and methods set the sampling rate of position and assigns value to portions of the pattern wherein position and maneuvering into position based on an importance handicap thereby scaling the score. For example, the aircraft position, altitude, bank angle, speed during the turn from base to final will be scored higher than a cross wind to downwind turn wherein the pilot has greater time to adjust aircraft altitude, speed and position before the descent to landing. How each section of the pattern is scored can be fixed or variable when the Scoring Module assigns the raw value as a score. Varying the valuation of raw data to reflect risk portions of the pattern match real world needs to performance. In some instances the Training module and the Scoring Module may be combined into a single server and module. [0421 Additionally, if Air Traffic Control (ATC) extends a leg of the approach or departure local voice recorders collect that information for appropriate normalization of the performance record for the student flight and the Training and Scoring Modules adjust for same.

[0041]

[0043] When entering the pattern aircraft 52A is shown within the desired pattern entering the downwind leg, however aircraft 55A is far outside the desired pattern. In this instance the raw Performance Metric "PM" for the pilot (and CFI) of aircraft 55 A will be valued lower than for the pilot (and CFI) of aircraft 52A.

[0042] Figure 4 shows aspects of a system overview of the flight system and method. Aircraft 50A-N collects metric data related to mechanical asset operation and human asset performance. The propulsion source (engine and / or motors) and the balance of plant (BOP) 101 (also referred to as engine systems) supporting the propulsion source is in signal communications with at least a processor and said processor has memory whereby the propulsion source and BOP operation including but not limited to tachometer, oil pressure, oil temperature, engine temperature, manifold pressure, fuel quantity, fuel pressure, alternator output operational data is sampled over the course of a flight thereby collected over time in said memory for transmission through a network 112. A radio 102 also having at least one processor and memory is configured to be sampled over time to collect a record of usage, frequencies used, back-up frequency, monitored frequencies and the like. A local digital record 103 can optionally be included with time stamp to collect communications with ATC, and communication between Student and Instructor. Said communications can later be utilized by the system for at least keyword analysis. The electronic flight display (EFD) 104 also contains processor and memory. Said EFD is configured to receive inputs from an air data computer 105 which in turn receive inputs from electromechanical sub-systems 106. Said inputs to the EFD are sampled over time and recorded (also referred to as flight systems). Weather data links 107 may also be supplied to the EFD. The data collected can include both raw Performance Metric "PM" such as selected RPM, airspeed, flap positioning, bank angel, angle of attack (AO A) during a specific flight with specific SIP combination during one or more performance intervals. . The data collected includes the aircraft's flight metrics data during the same flight. The data collected includes the aircraft's systems functional data during the same flight.

[0043]

[0044] The above non-exclusive list of data collection within aircraft 50A-N is configured to be in signal communication with and transmitted via a network 112.

[0044]

[0045] Figure 5 is a block diagram of aspects of the flight training and maintenance system 110 disclosed herein. The main modules are in signal communication with a network 112; main modules include the aircrafts 50A-N which provide inputs, the Maintenance Module 125 and its server 130 , Weather data 135, Training Module 145, and its server 150 Scoring Module 155 and its server 160 and Scheduling Module 165 and its server 170. Servers (150-170) may be the same server or separate servers and each has one or more processors and they may be referred to interchangeably as the flight training system server(s). The network 112 may be any computer-based network such as, for example, the Internet. The network may be one or more telecommunication networks that may include any type of wired and / or wireless network, including but not limited to local area networks (“LANs”), wide area networks (“WANs”), satellite networks, cable networks, Wi-Fi networks, WiMAX networks, mobile communications networks (e.g., 3G, 4G, 5G and so forth) or any combination thereof. The network 106 may utilize communications protocols, including packet-based and / or datagram-based protocols such as IP, transmission control protocol (“TCP”), user datagram protocol (“UDP”), or other types of protocols. Moreover, the network 112 may also include a number of devices that facilitate network communications and / or form a hardware basis for the networks, such as switches, routers, gateways, access points, firewalls, base stations, repeaters, backbone devices, and the like. In some examples, a server may further include devices that enable connection to a wireless network, such as a wireless access point (“WAP”). Examples support connectivity through WAPs that send and receive data over various electromagnetic frequencies (e.g., radio frequencies), including WAPs that support Institute of Electrical and Electronics Engineers (“IEEE”) 902.11 standards (e.g., 902.11g, 902.1 In, and so forth), and other standards. In this example, a server may be a personal computer, portable computer, server, etc. In general, a server may include one or more computing devices that operate in a cluster or other grouped configuration to share resources, balance load, increase performance, provide fail-over support or redundancy, or for other purposes. For instance, the computing server may belong to a variety of classes of devices such as traditional servertype devices, desktop computer-type devices, and / or mobile-type devices. In some implementations, the server includes one or more input / output (“I / O”) interfaces that enable communications with input / output devices including peripheral input devices (e.g., a keyboard, a mouse, a pen, a voice input device, a touch input device, a gestural input device, and the like) and / or output devices including peripheral output devices (e.g., a display, a printer, audio speakers, a haptic output device, and the like). The server may also include a combination of two or more devices.

[0045]

[0046] Figures 6 -7 are a pictorial of fleet aircraft 50A-N. A sufficiently large fleet in some instances can be seen as a mobile network or nodes in a network whereby aircraft to aircraft data transfer and communication 202 can occur via WIFI, LTE,4G, 5G and so one and / or via satellite. Once at an airfield 10 aircraft 50A-N can then deliver a packet of data from at least one of its flight data system and data transmitted to it from one or more other fleet members flight system(s) to fleet management system 350. Some specific systems which are listed as exemplary only and not as a limitation include Rotax 912is and 91 Sis engine systems and Garmin flight decks and displays such as those found at (https: / / www.garmin.eom / en-US / c / aviation / flight-decks-displays / ). Garmin flight decks and displays are not a limitation but exemplary, rather than provide a lengthy listing of all systems. For example, Garmin systems include a Garmin Sensor Unit sub-system consisting of at least an Air Data Computer (ADC), and the Attitude and Heading Reference System (AHRS).

[0046]

[0047] The data collection and output from the sub-systems provides time stamped flight information which when correlated with the time stamped engine system and flight control systems provides a more complete picture of the conditions of flight including but not limited to airspeed, altitude, temperature, altimeter, pitch, yaw, angle of attack, flap state, airspeed and ground speed, wind, bank of the aircraft and the like. This data provides context. Context is used to determine if an abnormal data point for a member of the fleet is conditions based or a condition in a flight system.

[0047]

[0048] When at an airfield 10 transmission 302 to a ground source 304 with wired internet connectivity is accomplished by either wireless (near field, WIFI, LTE,4G, 5G and satellite) or via wired connection. Said ground source can then transmit via a network 112 to the fleet management system 350. Said ground source in two-way communication with said fleet management system can also transmit to the aircraft 50A-N data and instructions related to one or more of immediate corrective actions, immediate maintenance actions, predictive maintenance actions any of which may change the flight plan of the aircraft or the scheduling of the aircraft.

[0048]

[0049] Figure 8 shows a block diagram of aspects of a fleet management system and methodology. The fleet management system 350 has a server in signal communication with a network. Aircraft 50A-N in the fleet are in signal communication the network. Both the maintenance server 130 and the scheduling server 170 are in signal with the network. The fleet management system provides predictive notifications 400 and corrective notifications 500 to both single aircraft (410 / 510) in the fleet and to multiple aircraft (420 / 520) in the fleet. Predictive notifications can include operational, maintenance and scheduling notices. Corrective notifications can include operational, maintenance and scheduling notices. Corrective notifications can be immediate or to be carried out when fleet asset returns. Immediate corrective notice may require grounding of the aircraft until the correction is completed. Said fleet management server may have fixed or variable thresholds to trigger corrective or predictive alerts and / or alarms. Such thresholds may be more restrictive than those inspection maintenance minimums set by the FAA. In some instances the threshold may be more restrictive than those set by the flight systems and / or the engine systems. Flight systems and engine systems are used as general terms to include all systems of the aircraft which sample and collect data which may be provided to and / or transmitted to the fleet management server.

[0049]

[0050] The fleet management systems analyzes data collection from aircrafts. It is configured to send corrective (or immediate) alerts / alarms to as few as a single aircraft in said fleet or to a subset of the fleet or to all members of the fleet based on analysis of the data collect from one or more aircrafts in sad fleet of aircrafts.

[0050]

[0051] The method disclosed herein includes a server configured to correlate flight conditions in the air or on the ground of an aircraft with an anomalous data set to decide if the anomaly is likely to be systemic among multiple aircrafts or a single event. [0521 The method disclosed herein includes a server configured to correlate flight conditions in the air or on the ground of an aircraft with a data set outside of a range defined by the fleet management as nominal for that aircraft to decide if it is an anomaly which requires corrective action of grounding one or more aircrafts until it has a maintenance or inspection.

[0051]

[0053] The method disclosed herein includes a server configured to correlate flight conditions in the air or on the ground of an aircraft with a data set outside of a range defined by the fleet management as nominal for that aircraft to decide if it is an anomaly which requires corrective action when one or more aircrafts return to its home base for maintenance or inspection.

[0052]

[0054] The method disclosed herein includes a server configured to correlate flight conditions in the air or on the ground of an aircraft with a data set outside of a range defined by the fleet management as nominal for that aircraft to decide if it is an anomaly which requires a predictive action of grounding one or more aircrafts until it has a maintenance or inspection.

[0053]

[0055] The method disclosed herein includes a server configured to correlate flight conditions in the air or on the ground of an aircraft with a data set outside of a range defined by the fleet management as nominal for that aircraft to decide if it is an anomaly which requires predictive action of maintenance or inspection of one or more aircrafts after return to home base.

[0054]

[0056] In all of the above predictive or corrective instances the fleet management systems communicates to the aircraft via one or more of the primary flight display, radio, a cell phone or tablet using any wireless protocol. Said communication may be an audible and / or visual alert to the pilot.

[0055]

[0057] In some instance the alert may be provided to the ATC or ground traffic control at the airport the aircraft or aircrafts subject to the alert are at or predictive to be travelling to.

[0056]

[0058] In some instance the alert may be provided wirelessly to the aircraft or aircrafts subject to the alert via communication from another fleet aircraft member.

[0057]

[0059] Figure 9 is a system block diagram of aspects of an exemplary implementation of the fleet management system server showing implementation of the Fleet Management System 350 in accordance with the present disclosure. The server 355 includes one and more processors 362, memory 358, one or more interfaces 360, and a system bus 361. The memory 358 may include a computer readable medium 370 and software 372. The software 372 may include instructions 372 that are configured to control the one or more processors 362. In this example, the server 355 receives, runs, maintains and updates the aircraft flight systems captured metrics and contains aircraft profiles 600 all of which may be run on the memory 358. It provides corrective and predictive information to be communicated to one or more aircraft in the fleet as needed. The memory 358 may include one or more separate memory or storage devices that are configured to operate together. In this example, the system bus 361 is in signal communication with the one or more processors 362, the memory 358, and the one or more interfaces 360. In this example, the one or more interfaces 360 is in signal communication with the network 112.

[0058]

[0060] Figure 10 is a simplified electrical system diagram 700 for an aircraft which provides data to at least one of the ADC and AHRS shown in Figure 11. The data is sampled, time stamped and can be stored in memory for retrieval. Figure 11 shows aspects of an avionics system for a single engine aircraft 800.

[0059]

[0061] The Pilot Display Interface Specification for a Rotax 912i series engine is hereby incorporated by this reference as if fully set forth herein in its entirety , it is also attached as an Appendix to the United States priority Provisional patent application detailed in Serial No.: 63 / 575,306, filed April 5, 2024. The Rotax engines are not a limitation but exemplary, rather than provide a lengthy listing of all systems applicable to this invention. Figure 12 shows aspects of the operating ranges such as those listed in Table 3 from that Pilot Display Interface Specification for 912 i Series Rotax TM engines. The information shown in Figure 12 can be stored in memory and via the disclosure herein the collected data can be transmitted from memory to a server for analysis. The ID items 900 are sampled, time stamped and collected by the aircraft flight systems and can be stored in memory and via the disclosure herein the collected data can be transmitted from memory to a server for analysis. [0621 Disclosed herein are systems for utilizing the retrieved collection of data on each aircraft in a fleet and making predictive maintenance decision and / or predictive alerts and alarms based on such collected data for one or more aircraft of a group of similar aircrafts.

[0060]

[0063] It will be understood that various aspects or details of the disclosures may be changed combined, or removed without departing from the scope of the invention. It is not exhaustive and does not limit the claimed inventions to the precise form disclosed. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.

[0061] Modifications and variations are possible in light of the above description or may be acquired from practicing the invention. The claims and their equivalents define the scope of the invention.

Claims

CLAIMSWhat is claimed is:

1. A method of predictive maintenance for fleet aircraft the method comprising: collecting flight data on at least flight systems, GPS, and engine systems from a plurality of the same make and model training aircraft during the same time period; provide said collected data to fleet server; provide at least MET AR data corresponding to the flight time and location of each flight to fleet server; normalize data for any known deviations of aircraft in the fleet; and, wherein the fleet server is configured to use the normalized data and generate predictive alerts on one or more aircraft in fleet.

2. The method of predictive maintenance for fleet aircraft of claim 1 wherein data collection is via one or more of, radio and wireless signal communications.

3. The method of predictive maintenance for fleet aircraft of claim 1 wherein wireless signal communications includes one or more of near field, WIFI, LTE,4G, 5G and satellite.

4. The method of predictive maintenance for fleet aircraft of claim 1 wherein the server uses at least the METAR data corresponding to the flight time and location to decide if collected data meets the alert or alarm threshold.

5. The method of predictive maintenance for fleet aircraft of claim 1 wherein predictive alert decisioning is based at least on difference below the alarm setting on at least one of a flight systems and engine systems .

6. The method of predictive maintenance for fleet aircraft of claim 5 wherein predictive alert is more restrictive threshold than the alarm settings on flight system or engine systems.

7. The method of predictive maintenance for fleet aircraft of claim 5 wherein predictive alert is i based, on data collected from two or more aircrafts.

8. The method of predictive maintenance for fleet aircraft of claim 5 wherein predictive alert is based on data collected from a plurality of aircrafts.

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