Synthetic air data output generation

In one example, a method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

BACKGROUND

The present disclosure relates generally to air data systems, and more particularly to air data systems that can utilize artificial intelligence to generate air data outputs for an aircraft.

Modern aircraft often incorporate air data systems that calculate air data outputs based on measured parameters collected from various sensors positioned about the aircraft. For instance, many modern aircraft utilize pneumatic air data probes that measure pitot pressure, static pressure, or other parameters of airflow across the probe. Such pneumatic air data probes often include one or more air data sensing ports, such as static pressure ports and/or total pressure (i.e., stagnation pressure) ports. A portion of air flowing over the probes is diverted to the ports that are pneumatically connected to pressure sensors that sense the atmospheric pressure outside the aircraft. Such measured pressures are usable for determining air data outputs, such as aircraft pressure altitude, altitude rate (e.g., vertical speed), airspeed, Mach number, angle of attack, angle of sideslip, or other air data outputs.

To increase system reliability, aircraft manufacturers typically incorporate redundant (e.g., backup) systems that can provide outputs to consuming systems in the event that a primary system fails or is otherwise determined to be unreliable. For instance, many aircraft incorporate multiple (e.g., two, three, four, or more) pneumatic air data probes, certain of which are designated as backup systems for use when a primary system is deemed unreliable.

SUMMARY

In one example, a method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

In another example, a synthetic air data system includes at least one processor and computer-readable memory. The computer-readable memory is encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to receive, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The computer readable memory is further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and output the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

DETAILED DESCRIPTION

As described herein, a synthetic air data system can process a plurality of non-pneumatic inputs through an artificial intelligence network to generate one or more air data output values. Such non-pneumatic inputs can include, among others, aircraft thrust parameters, aircraft engine throttle settings, flight control surface positions and/or surface loading parameters, aircraft remaining fuel weight and/or usage rates, aircraft weight, landing gear position (e.g., deployed or stowed), aircraft mass balance, and aircraft acceleration and/or angular rates (e.g., received from an inertial reference system). The artificial intelligence network, such as an artificial neural network, can correlate the received inputs to one or more air data output values, such as airspeed, altitude, Mach number, angle of attack, angle of sideslip, or other air data output values. As such, a synthetic air data system implementing techniques of this disclosure can generate air data output values via a system that is dissimilar in design to traditional direct-measurement systems (e.g., pneumatic-based, optical, ultrasonic, or other sensor-based systems that directly measure the air data value) and that can be used, e.g., when a sensor-based air data output value, such as a pneumatic-based air data output value, is determined to be unreliable. Moreover, such air data output values can be generated from measured inputs that are provided by existing aircraft systems, thereby decreasing the time and cost required to install additional sensors or hardware components on new and existing aircraft platforms when incorporating the synthetic air data system.

FIG. 1is a schematic block diagram of aircraft10including synthetic air data system12that can process non-pneumatic inputs through an artificial intelligence network to generate one or more air data output values. As illustrated inFIG. 1, aircraft10can further include pneumatic air data probe14A and pneumatic air data probe14B (collectively referred to herein as “pneumatic air data probes14”), air data computer (ADC)16A and air data computer16B (collectively referred to herein as “air data computers16”), producing systems18, consuming systems20, and data concentrator unit (DCU)22.

Pneumatic air data probes14are positioned at an exterior of aircraft10to sense one or more pressures of air flowing over the probes. Pneumatic air data probes14include one or more air data sensing ports (not illustrated) to which airflow around pneumatic air data probes14is diverted. The air data sensing ports are pneumatically connected to pressure sensors (e.g., pressure transducers or other pressure sensors) that measure the collected airflows to generate measured pressures that are usable in determining, e.g., static pressure, total pressure (i.e., stagnation pressure), or other pressures of the airflow around aircraft10. Outputs of the pressure sensors are electrically connected to air data computers16, which generate air data output values based on the received pneumatic pressures.

As illustrated inFIG. 1, air data computer16A is adjacent pneumatic air data probe14A and air data computer16B is adjacent pneumatic air data probe14B. In other examples, air data computers16need not be adjacent air data probes14. For instance, air data computers16can be located within the interior of aircraft10at a location that is remote from pneumatic air data probes14, such as within an electronics bay of aircraft10. In addition, while illustrated as including two pneumatic air data probes14and two corresponding air data computers16, aspects of this disclosure are not so limited. For instance, in other examples, aircraft10can include more or fewer than two of each of pneumatic air data probes14and air data computers16, and the number of air data computers16need not match the number of pneumatic air data probes14. In general, aircraft10includes one or more air data computers16that are electrically and/or communicatively coupled with one or more air data probes14to receive indications of measured pneumatic pressures (e.g., static pressure and total pressure) of airflow around the exterior of aircraft10sensed by the one or more pneumatic air data probes14.

Air data computers16house electrical components, such as one or more processors, computer-readable memory, or other electrical components configured to generate air data outputs corresponding to one or more operational states of aircraft10. Non-limiting examples of such air data outputs include calibrated airspeed, true airspeed, Mach number, altitude (e.g., pressure altitude), angle of attack (i.e., an angle between oncoming airflow or relative wind and a reference line of a wing of aircraft10), vertical speed (e.g., altitude rate), and angle of sideslip (i.e., an angle between a direction of travel and a direction extending through a nose of aircraft10). Accordingly, air data outputs generated by air data computers16based on pneumatic pressures received by pneumatic air data probes14can be considered to be pneumatic-based air data outputs.

As further illustrated inFIG. 1, aircraft10includes producing systems18. Producing systems18include operational systems of aircraft10that produce non-pneumatic outputs usable by synthetic air data system12as inputs to generate air data output values, as is further described below. For example, producing systems18can include aircraft engines and/or thrust control systems, aircraft fuel systems, flight management control systems, aircraft navigational systems such as inertial reference systems (IRS), attitude heading and reference systems (AHARS), global positioning system (GPS) and/or satellite information systems, landing gear systems, or other operational systems of aircraft10. Producing systems18, as illustrated, are communicatively coupled with data concentrator unit (DCU)22.

Data concentrator unit22is an electronic device comprising one or more processors, computer-readable memory, and data transceivers configured to receive digital and/or analog signals from various aircraft systems and format the received signals for transmission according to a defined communications protocol, such as the protocol defined by the Aeronautical Radio, Incorporated (ARINC) 429 standard. For instance, as illustrated inFIG. 1, data concentrator unit22can receive inputs from producing systems18and can transmit the inputs over communications data bus24for receipt by one or more aircraft systems, such as synthetic air data system12, air data computers16, consuming systems20, or other systems of aircraft10. Communications data bus24can be any data bus that communicatively couples components of aircraft10and enables communication between the interconnected components via a defined communications protocol (e.g., ARINC 429).

Consuming systems20can be any operational system of aircraft10configured to receive air data output values from air data computers16and/or synthetic air data system12for use during operation of aircraft10. For instance, consuming systems20can include any one or more of flight management systems, automatic flight control systems, aircraft display systems (e.g., primary flight displays, multifunction displays, control display units, or other display systems), or other operational systems of aircraft10that can utilize the received air data output values during operation of aircraft10. In some examples, certain aircraft systems can be included in both producing systems18and consuming systems20. For instance, a flight management computer can be included as one of producing systems18that outputs a calculated aircraft mass balance, remaining fuel, fuel usage rate, aircraft altitude, or other non-pneumatic outputs that are utilized by synthetic air data system12for generation of one or more air data output values. In addition, the flight management computer can be included as one of consuming systems20that receives generated air data output values from synthetic air data system12for use when a pneumatic-based air data output value determined by, e.g., one or more of air data computers16is determined to be unreliable. Accordingly, producing systems18and consuming systems20can each include any one or more aircraft systems, and the respective systems need not be unique to either of producing systems18and consuming systems20.

As illustrated inFIG. 1, synthetic air data system12is communicatively connected to air data computers16, consuming systems20, and data concentrator unit22via communications data bus24. However, while in the example ofFIG. 1synthetic air data system12is communicatively connected to producing systems18via data concentrator unit22, in other examples, synthetic air data system12can be directly connected (e.g., communicatively and/or electrically connected) to any one or more of producing systems18.

Synthetic air data system12can include one or more processors and computer readable memory encoded with instructions that, when executed by the one or more processors, cause synthetic air data system12to operate in accordance with techniques described herein. Synthetic air data system12, in some examples, can include one or more stand-alone electronic devices, such that synthetic air data system is separate from air data computers16and each of consuming systems20. In other examples, synthetic air data system12can be included in any one or more of air data computers16and/or consuming systems20, such that functionality attributed herein to synthetic air data system12is performed by and/or distributed among one or more electronic devices of such other systems. For instance, in some examples, any one or more of air data computers16can implement functionality attributed herein to synthetic air data system12. In general, synthetic air data system12includes one or more processors and computer readable memory encoded with instructions that, when executed by the one or more processors, cause synthetic air data system12to process received non-pneumatic inputs through an artificial intelligence network to generate an air data output value.

Examples of one or more processors of synthetic air data system12can include any one or more of a microprocessor, a controller (e.g., microcontroller), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry. Computer readable memory of synthetic air data system12can be configured to store information within synthetic air data system12during operation. Such computer-readable memory, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, the computer-readable memory is a temporary memory, meaning that a primary purpose of the computer-readable memory is not long-term storage. Computer-readable memory, in some examples, includes and/or is described as volatile memory, meaning that the computer-readable memory does not maintain stored contents when power to synthetic air data system12is removed. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. In some examples, computer-readable memory is used to store program instructions for execution by one or more processors of synthetic air data system12. Computer-readable memory, in one example, is used by software or applications executing on synthetic air data system12to temporarily store information during program execution.

Computer-readable memory of synthetic air data system12, in some examples, also includes one or more computer-readable storage media. Computer-readable storage media can be configured to store larger amounts of information than volatile memory. Computer-readable storage media can be configured for long-term storage of information. In some examples, computer-readable storage media include non-volatile storage elements. Examples of such non-volatile storage elements can include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

In operation, synthetic air data system12receives, over communications data bus24, non-pneumatic inputs corresponding to aircraft operational parameters. For instance, in the example ofFIG. 1, synthetic air data system12can receive a plurality of non-pneumatic inputs from producing systems18via data concentrator unit22and communications data bus24. Examples of such non-pneumatic inputs can include, but are not limited to, inputs corresponding to aircraft control surface position (e.g., ailerons, elevator, rudder, spoilerons, flaps, slats, or other control surfaces) and/or control surface loading, aircraft mass and/or mass balance (e.g., current and/or at a predefined time, such as at takeoff), remaining fuel weight, engine thrust parameters (e.g., engine N1, N2, EGT, throttle settings, or other thrust parameters), aircraft navigational information (e.g., aircraft position, heading, altitude, ground speed, airspeed, or other navigational information), air temperature information (e.g., static air temperature, total air temperature, outside air temperature, or other temperature information), aircraft acceleration and/or angular rate information (e.g., received from an IRS), landing gear position information (e.g., deployed, stowed, in transit, or other landing gear position information), or other non-pneumatic inputs. In general, non-pneumatic inputs can include any input indicative of an aircraft operational state received from a non-pneumatic source (e.g., sources other than pneumatic air data probes14).

Synthetic air data system12processes the plurality of non-pneumatic inputs through an artificial intelligence network to generate one or more air data output values (e.g., calibrated airspeed, true airspeed, Mach number, pressure altitude, angle of attack, angle of sideslip, or other air data output values). Examples of such artificial intelligence networks include artificial neural networks, probabilistic graphical models such as Bayesian networks, probabilistic classifiers and/or controllers (e.g., Gaussian mixture models), or other forms of artificial intelligence networks. As one example, the artificial intelligence network can be an artificial neural network having at least one internal layer of nodes (often referred to as a hidden layer of neurons) that apply one or more weights, biases, and/or transfer functions to the plurality of non-pneumatic inputs to correlate the plurality of non-pneumatic inputs to one or more air data output values.

In some examples, the artificial intelligence network can be pre-trained based on previously-obtained data (e.g., flight test data) to correlate the plurality of non-pneumatic inputs to the one or more air data output values. In certain examples, synthetic air data system12can utilize a single artificial intelligence network to generate a plurality of air data output values from a plurality of non-pneumatic inputs. In other examples, synthetic air data system12can utilize multiple, separate artificial intelligence networks that each correlate a particular set of non-pneumatic inputs to a selected air data output value. For instance, synthetic air data system12can utilize a first artificial intelligence network that correlates a first set of non-pneumatic inputs to a first air data output value (e.g., angle of attack), and can utilize a second artificial intelligence network that correlates a second set of non-pneumatic inputs to a second air data output value (e.g., angle of sideslip). The first and second sets of non-pneumatic inputs can be the same or difference sets of inputs.

In certain examples, such as when the artificial intelligence network is an artificial neural network, the weights, biases, and transfer functions of the hidden layer of neurons can be pre-defined (e.g., via offline pre-training) and fixed, such that synthetic air data system12does not modify the weights, biases, and transfer functions during operation of synthetic air data system12. In other examples, synthetic air data system12can incorporate an active training (or “learning”) mode in which synthetic air data system12modifies the weights, biases, and transfer functions applied by the neurons based on feedback of the generated air data output and a reference value, such as a pneumatic-based air data output value. That is, in certain examples, synthetic air data system12can receive as input one or more pneumatic-based air data outputs generated by, e.g., air data computers16via measured pressures received from pneumatic air data probes14. In such examples, synthetic air data system12can effectively train the artificial intelligence network based on non-pneumatic inputs received from producing systems18and pneumatic-based air data outputs generated by, e.g., air data computers16.

In certain examples, synthetic air data system12can identify whether the received pneumatic-based air data output value is determined to be reliable. For instance, synthetic air data system12can receive a status indication or other indication of reliability of the pneumatic-based air data output value from e.g., air data computers16or one or more of consuming systems20, such as a flight management computer, automatic flight control system, or other of consuming systems20. In other examples, synthetic air data system12can determine whether the received pneumatic-based air data output value is reliable, such as by comparing pneumatic-based air data output values received from multiple sources (e.g., multiple of air data computers16) to each other or to a threshold value. Synthetic air data system12can process the non-pneumatic inputs and the received pneumatic-based air data output through the artificial intelligence network to generate the air data output value (and optionally train the artificial neural network) when the pneumatic-based air data output value is determined to be reliable. Synthetic air data system12can process the non-pneumatic inputs alone (i.e., without processing the received pneumatic-based air data output value) through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be unreliable.

Such active training can enable synthetic air data system12to maintain and/or initialize dynamic internal states of the artificial neural network. In addition, active training and/or comparison of the air data outputs generated by synthetic air data system12to received pneumatic-based air data outputs can enable synthetic air data system12to determine an estimated error of the air data outputs generated by synthetic air data system12. In certain examples, synthetic air data system12can generate an indication of reliability of the air data outputs generated by synthetic air data system12based on the comparison.

Synthetic air data system12can output the generated air data output value (i.e., generated by synthetic air data system12) to one or more of consuming systems20(e.g., via communications data bus24) for use when a pneumatic-based air data output value is determined to be unreliable. In some examples, synthetic air data system12can determine whether the pneumatic-based air data output value is unreliable, such as by comparing received pneumatic-based air data outputs to each other and/or to a threshold deviation parameter. In other examples, one or more of consuming systems20(e.g., a flight management system) can determine the reliability of the pneumatic-based air data output value, and can designate the air data output value generated by synthetic air data system12for use when the pneumatic-based air data output value is determined to be unreliable.

In certain examples, the air data output value generated by synthetic air data system12can be utilized to determine whether the pneumatic-based air data output value is reliable. For instance, synthetic air data system12and/or one or more of consuming systems20can compare pneumatic-based air data outputs to the air data output(s) generated by synthetic air data system12. A pneumatic-based air data output can be determined to be unreliable when, e.g., a corresponding pneumatic-based air data output received from a first one of air data computers16is within a threshold deviation from the air data output generated by synthetic air data system12and the pneumatic-based air data output received from a second one of air data computers16exceeds the threshold deviation from the air data output generated by synthetic air data system12. In such an example, the pneumatic-based air data output received from the first one of air data computers16(that is within the threshold deviation from the air data output generated by synthetic air data system12) can be determined to be reliable. The pneumatic-based air data output received from the second one of air data computers16(that exceeds the threshold deviation) can be determined to be unreliable.

Consuming systems20can utilize one or more air data outputs generated by synthetic air data system12for operation when corresponding pneumatic-based air data outputs are determined to be unreliable. As such, synthetic air data system12can provide a redundant (e.g., backup) air data system that generates air data output values usable for operation of aircraft12when one or more pneumatic-based air data output values are determined to be unreliable. The air data output values generated by synthetic air data system12can be based on non-pneumatic source inputs, thereby providing an air data system that is dissimilar in design to the pneumatic-based air data systems and enhancing operational reliability of aircraft10. Moreover, the non-pneumatic inputs received and processed by synthetic air data system12can be selected from inputs available on new and existing aircraft platforms, thereby reducing the time and cost required to incorporate synthetic air data system12into such aircraft designs.

FIG. 2is a schematic diagram of an example artificial neural network26that can be used to process non-pneumatic inputs to generate one or more air data output values. For purposes of clarity and ease of discussion, the example artificial neural network26ofFIG. 2is described below within the context of aircraft10including synthetic air data system12ofFIG. 1.

As illustrated inFIG. 2, artificial neural network26includes input nodes28A-28N (collectively referred to herein as “inputs28”), internal nodes (or neurons)30A-30M (collectively referred to herein as “neurons30” and often referred to as a hidden layer), and output node32. It should be understood that in the example ofFIG. 2, the letter “N” of input node28N and the letter “M” of internal node30M represent arbitrary numbers, such that each of inputs28and neurons30can include any number of input nodes and internal nodes, respectively. In certain examples, artificial neural network26includes a number of neurons30that is one less than the number of inputs28. That is, while the letter “N” of input node28N represents an arbitrary number, in some examples, the letter “M” of internal node “30M” represents a number that is one less than the arbitrary number represented by the letter “N”.

Each of inputs28corresponds to one of the plurality of non-pneumatic inputs received from producing systems18, though in examples where synthetic air data system12processes pneumatic-based air data outputs received from air data computers16, certain of inputs28can correspond to the received pneumatic-based air data outputs. Each of neurons30applies a weight, bias, and transfer function (e.g., a sigmoid function) to each of inputs28to generate intermediate outputs provided by neurons30. In the illustrated example ofFIG. 2, the intermediate outputs provided by neurons30are provided as inputs to output node32. Output node32applies predetermined weights, biases, and/or a transfer function to the intermediate outputs to generate a particular air data output value (e.g., calibrated airspeed, true airspeed, Mach number, pressure altitude, angle of attack, angle of sideslip, or other air data output values).

In the example ofFIG. 2, artificial neural network26processes inputs28to generate a single air data output value at output node32. However, in other examples, artificial neural network26can process inputs28to determine multiple air data outputs (i.e., at multiple output nodes). In certain examples, artificial neural network26can represent a first artificial neural network that correlates a first set of inputs (e.g., inputs28) to a first air data output value (e.g., at output node32). In such examples, synthetic air data system12(ofFIG. 1) can utilize a second artificial neural network that correlates a second set of inputs to a second, different air data output value by utilizing different weights, biases, and transfer functions at neurons30. The first set of inputs (e.g., inputs28) can be the same or different than the second set of inputs.

As an example, synthetic air data system12can utilize artificial neural network26that generates a first air data output value (e.g., angle of attack) at output node32using a first set of non-pneumatic inputs corresponding to inputs28and a first set of weights, biases, and transfer functions at neurons30. Synthetic air data system12can utilize a second artificial neural network (e.g., of the same architectural form of neural network26) that generates a second air data output value (e.g., angle of sideslip) at the output node using a second set of non-pneumatic inputs and a second set of weights, biases, and transfer functions at the hidden layer of neurons. The second set of non-pneumatic inputs (utilized to generate an angle of sideslip air data output value) can be the same set of non-pneumatic inputs as the first set of non-pneumatic inputs or a different set of non-pneumatic inputs.

In some examples, synthetic air data system12can store multiple artificial neural networks that are each usable to generate a same category of air data output value (e.g., angle of sideslip, angle of attack, calibrated airspeed, or other categories of air data output value). The multiple artificial neural networks can utilize different sets of inputs and different weights, biases, and transfer functions to generate the same category of air data output value. Synthetic air data system12, in such examples, can select which of the multiple artificial neural networks to utilize to generate the category of air data output value based on an availability and/or determined reliability of inputs to the multiple artificial neural networks. For instance, synthetic air data system12can receive and/or determine a reliability and/or accuracy status of each of the inputs to each of the multiple artificial neural networks. Synthetic air data system12can select, e.g., a first of the multiple artificial neural networks for use in generating the category of air data output value. In the event that one or more of the inputs to the selected first of the multiple artificial neural networks is determined to be unreliable (or inaccurate) and each of the inputs to a second of the artificial neural networks is determined to be reliable (and accurate), synthetic air data system12can select the second of the multiple artificial neural networks for use in generating the category of air data output value. In this way, synthetic air data system12can increase robustness of air data output generation by enabling an air data output value to be generated based on any of multiple, different sets of non-pneumatic inputs.

While the example artificial neural network26ofFIG. 2is illustrated as a feed-forward neural network including a single hidden layer of neurons30, in some examples, artificial neural network26can take the form of a recurrent neural network in which connections between units (e.g., inputs28, neurons30, and/or output node32) form a directed cycle that enables artificial neural network26to store internal states of each of the nodes to thereby model dynamic temporal behavior. In addition, in some examples, artificial neural network26can include two or more layers of neurons30.

As described herein, artificial neural network26, implemented by synthetic air data system12, can be used to generate one or more air data output values based on non-pneumatic inputs received from various producing systems of an aircraft. The use of non-pneumatic inputs can provide air data output values that are usable during operation of the aircraft (e.g., for controlled flight) and that are generated via a system that is dissimilar in design to pneumatic-based air data systems. Accordingly, the use of air data output values generated by synthetic air data system12via artificial neural network26can help to increase the operational reliability of the aircraft by increasing the chance that an environmental or other condition that may cause anomalous behavior of the pneumatic-based air data system does not adversely affect synthetic air data system12.

FIG. 3is a flow diagram illustrating example operations to process non-pneumatic inputs through an artificial intelligence network to generate one or more air data output values. For purposes of clarity and ease of discussion, the example operations are described below within the context of aircraft10ofFIG. 1and artificial neural network26ofFIG. 2.

A plurality of non-pneumatic inputs can be received (step34). For example, synthetic air data system12can receive a plurality of non-pneumatic inputs generated by producing systems18via data concentrator unit22and over communications data bus24. It can be determined whether active training of an artificial intelligence network is enabled (step36). For instance, synthetic air data system12can determine whether an active training mode of artificial neural network26is enabled. In examples where the active training is enabled (“YES” branch of step36), parameters of the artificial intelligence network can be modified based on, e.g., feedback values of generated air data output values and/or a received reference value, such as a corresponding pneumatic-based air data output value (step38). In examples where the active training is not enabled (“NO” branch of step36), the step of modifying the parameters of the artificial intelligence network can be omitted (or skipped).

The plurality of non-pneumatic inputs can be processed through the artificial intelligence network to generate an air data output value (step40). For example, synthetic air data system12can process a plurality of non-pneumatic inputs through artificial neural network26to generate an air data output value at output node32.

It can be determined whether a pneumatic-based air data output value is unreliable (step42). For instance, any one or more of consuming systems20can determine whether a pneumatic-based air data output value generated by air data computers16is reliable, or whether the pneumatic-based air data output value is unreliable. In examples where the pneumatic-based air data output value is not determined to be unreliable (“NO” branch of step42), the pneumatic-based air data output value can be utilized for operation, such as for controlled flight of aircraft10(step44). In examples where the pneumatic-based air data output value is determined to be unreliable (“YES” branch of step42), the air data output value generated by synthetic air data system12can be utilized for operation, such as for controlled flight of aircraft10(step46). For instance, in certain examples, air data output values generated by one or more different (e.g., primary) system(s) can be utilized for flight, and the air data output value generated by synthetic air data system12can be used as a backup air data output value in response to a determination that the air data output values generated by the one or more primary systems are unreliable.

While the example operations described above include step36in which it is determined whether active training of the artificial intelligence network is enabled, other example operations may not include step36. For instance, as when the artificial neural network is adapted such that active training is unavailable, synthetic air data system12may not actively determine whether active training is enabled. Rather, synthetic air data system12may proceed directly to step40in response to receiving the plurality of non-pneumatic inputs. Similarly, in examples where the artificial neural network is adapted such that active training is always enabled, synthetic air data system12may proceed directly to step38in response to receiving the plurality of non-pneumatic inputs without actively determining whether active training is enabled.

According to techniques of this disclosure, a synthetic air data system can process a plurality of non-pneumatic inputs corresponding to aircraft operational parameters through an artificial intelligence network to generate one or more air data output values. The synthetic air data system can output the one or more air data output values for use when, e.g., a pneumatic-based air data output value is determined to be unreliable. Accordingly, a synthetic air data system as described herein can provide a source of generated air data output values for consuming systems that is dissimilar in design to traditional pneumatic-based air data systems, thereby helping to enhance aircraft operational reliability.

A method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

The method of the preceding paragraph can optionally include, additionally and/or alternatively, any one or more of the following features, configurations, operations, and/or additional components.

The plurality of non-pneumatic inputs can include one or more of an aircraft engine thrust parameter, an aircraft engine throttle setting, a flight control surface position, a flight control surface loading, an aircraft fuel usage rate, an aircraft weight, a landing gear position, an aircraft mass balance, an aircraft acceleration, and an aircraft angular rate.

The generated air data output value can be selected from a group including an aircraft calibrated airspeed, an aircraft true airspeed, an aircraft Mach number, an aircraft pressure altitude, an aircraft angle of attack, an aircraft vertical speed, and an aircraft angle of sideslip.

The artificial intelligence network can include an artificial neural network having at least one internal layer of neurons that apply one or more weights, biases, or transfer functions to each of the plurality of non-pneumatic inputs to generate the air data output value.

The artificial neural network can be a feed-forward neural network.

The artificial neural network can be pre-trained to determine the one or more weights, biases, or transfer functions.

Processing the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value can include processing the plurality of non-pneumatic inputs through the artificial neural network without changing the one or more weights, biases, or transfer functions.

The pre-trained artificial neural network can modify the one or more weights, biases, or transfer functions based on the plurality of non-pneumatic inputs corresponding to the aircraft operational parameters.

The method can further include receiving the pneumatic-based air data output value from a pneumatic-based air data system, and identifying whether the received pneumatic-based air data output value is determined to be reliable or whether the received pneumatic-based air data output value is determined to be unreliable. Processing the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value can further include processing the non-pneumatic inputs and the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be reliable, and processing the non-pneumatic inputs without the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be unreliable.

The method can further include outputting the air data output value to a consuming system that determines whether the pneumatic-based air data output value is unreliable based at least in part on the generated air data value.

The method can further include determining whether the pneumatic-based air data output value is unreliable.

A synthetic air data system includes at least one processor and computer-readable memory. The computer-readable memory is encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to receive, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The computer readable memory is further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and output the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

The synthetic air data system of the preceding paragraph can optionally include, additionally and/or alternatively, any one or more of the following features, configurations, operations, and/or additional components.

The plurality of non-pneumatic inputs can include one or more of an aircraft engine thrust parameter, an aircraft engine throttle setting, a flight control surface position, a flight control surface loading, an aircraft fuel usage rate, an aircraft weight, a landing gear position, an aircraft mass balance, an aircraft acceleration, and an aircraft angular rate.

The computer-readable memory can be encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value that is selected from a group comprising an aircraft calibrated airspeed, an aircraft true airspeed, an aircraft Mach number, an aircraft pressure altitude, an aircraft angle of attack, an aircraft vertical speed, and an aircraft angle of sideslip.

The computer-readable memory can be encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through a pre-trained artificial neural network having at least one internal layer of neurons that apply one or more weights, biases, or transfer functions to each of the plurality of non-pneumatic inputs to generate the air data output value.

The computer-readable memory can be encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the pre-trained artificial intelligence network to generate the air data output value without changing the one or more weights, biases, or transfer functions.

The computer-readable memory can be encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the pre-trained artificial neural network to generate the air data output value by modifying the one or more weights, biases, or transfer functions based on the plurality of non-pneumatic inputs corresponding to the aircraft operational parameters.

The computer-readable memory can be further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to receive the pneumatic-based air data output value from a pneumatic-based air data system, and identify whether the received pneumatic-based air data output value is determined to be reliable or whether the received pneumatic-based air data output value is determined to be unreliable. The computer-readable memory can be encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to process the plurality of non-pneumatic inputs through the artificial intelligence network to generate the air data output value by at least causing the synthetic air data system to process the non-pneumatic inputs and the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be reliable, and process the non-pneumatic inputs without the received pneumatic-based air data output value through the artificial intelligence network to generate the air data output value when the received pneumatic-based air data output value is determined to be unreliable.

The computer-readable memory can be further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to output the air data output value to a consuming system that determines whether the pneumatic-based air data output value is unreliable based at least in part on the generated air data value.

The computer-readable memory can be further encoded with instructions that, when executed by the at least one processor, cause the synthetic air data system to determine whether the pneumatic-based air data output value is unreliable.