Systems and methods for determining areas of discrepancy in flight for an electric aircraft
A system for determining areas of discrepancy in flight for an electric aircraft is presented. The system includes a plurality of sensors, wherein each sensor is communicatively connected to a flight component and configured to detect a measured flight and generate a flight phase datum. The system further includes a computing device communicatively connected to the plurality of sensors, wherein the computing device is configured to determine, by a flight phase machine-learning model, a flight phase discrepancy datum. The computing device is configured to train the flight phase machine-learning using a flight phase training set, wherein the flight phase training set correlates a flight phase to a flight standard and output the flight phase discrepancy datum. The computing device is further configured to identify an anomalous performance phase as a function of the flight phase discrepancy datum and generate a discrepancy response.
1 . A system for determining areas of discrepancy in flight for an electric aircraft, the system comprising:
a sensor communicatively coupled to the electric aircraft and configured to:
detect measured flight data; and
generate flight phase data and flight component data as a function of the measured flight data; and
a computing device communicatively coupled to the sensor and configured to:
determine flight phase discrepancy data by processing the flight phase data through a flight phase machine-learning model trained using a flight phase training set correlating a plurality of flight phases associated with the electric aircraft to a flight phase standard;
determine flight component discrepancy data by processing the flight component data through a flight component machine-learning model trained using a flight component training set correlating a plurality of flight components associated with the electric aircraft to a flight component standard, wherein the flight component discrepancy data includes flight component performance values exceeding or falling below a latency parameter;
determine an anomalous source as a function of the flight phase discrepancy data and the flight component discrepancy data, the anomalous source comprising an anomalous flight phase of the plurality of flight phases and a flight component of the plurality of flight components in which an anomaly is present during the anomalous flight phase;
determine a corrective action as a function of the anomalous source; and
provide a signal to the flight component based on the corrective action.
2 . The system of claim 1 , wherein the sensor is a first sensor configured to detect first measured flight data of the flight component at a first time interval; and a second sensor communicatively coupled to the electric aircraft and configured to detect a second measured flight data of the flight component at a second time interval.
3 . The system of claim 2 , wherein the computing device is further configured to determine the flight phase discrepancy data by processing the first measured flight data and the second measured flight data through the flight phase machine-learning model.
4 . The system of claim 1 , wherein the sensor is a pilot control, and the measured flight data includes maneuver data.
5 . A method for determining areas of discrepancy in flight for an electric aircraft, the method comprising:
detecting, using a sensor communicatively coupled to the electric aircraft, a measured flight data;
generating, using the sensor, flight phase data and flight component data as a function of the measured flight data;
determining, using a computing device communicatively coupled to the sensor, a flight phase discrepancy data by processing the flight phase data through a flight phase machine-learning model trained using a flight phase training set correlating a plurality of flight phases associated with the electric aircraft to a flight phase standard;
determining, using the computing device, flight component discrepancy data by processing the flight component data through a flight component machine-learning model trained using a flight component training set correlating a plurality of flight components associated with the electric aircraft to a flight component standard, wherein the flight component discrepancy data includes flight component performance values exceeding or falling below a latency parameter;
determining, using the computing device, an anomalous source as a function of the flight phase discrepancy data and the flight component discrepancy data, the anomalous source comprising an anomalous flight phase of the plurality of flight phases and a flight component of the plurality of flight components in which an anomaly is present during the anomalous flight phase;
determining, using the computing device, a corrective action as a function of the anomalous source; and
providing, using the computing device, a signal to the flight component based on the corrective action.
6 . The method of claim 5 , wherein the electric aircraft includes the flight component, and
wherein the sensor is a first sensor,
the method further comprising:
detecting, using the first sensor, first measured flight data of the flight component at a first time interval; and
detecting, using a second sensor communicatively coupled to the electric aircraft, a second measured flight data of the flight component at a second time interval.
7 . The method of claim 6 , further comprising:
determining, using the computing device, the flight phase discrepancy data by processing the first measured flight data and the second measured flight data through the flight phase machine-learning model.
8 . The system of claim 1 , wherein the flight phase standard includes a flight phase performance index used to determine a severity of a discrepancy between at least one of the plurality of flight phases and the flight phase standard.
9 . The system of claim 1 , further comprising a remote device communicatively connected to the electric aircraft,
wherein the computing device is further configured to provide the anomalous source to the remote device, and
the remote device configured to determine, based on the anomalous source and the flight phase standard, an improvement to a flight phase of the plurality of flight phases corresponding to the anomalous flight phase.
10 . The system of claim 9 , wherein the anomalous flight phase includes a maintenance issue, and wherein the improvement to the flight phase includes an adjustment to a maintenance phase.
11 . The system of claim 1 , wherein the flight component is a propeller, and
the signal includes a command directing the flight component to reduce or reverse a torque magnitude or a torque direction.
12 . The system of claim 1 , wherein the flight component is a propeller, and
the signal includes a command directing the flight component to reduce an amount of force that is induced as a function of lift component striking or interacting with an extraneous object.
13 . The system of claim 1 , wherein the flight component is a motor, and
the signal includes a command directing the flight component to transition to a reverse thrust mode.
14 . The system of claim 1 , wherein the flight component discrepancy data further includes flight component performance values exceeding or falling below at least one additional component parameter of a plurality of component parameters.
15 . The system of claim 14 , wherein the plurality of component parameters include torque, power consumption, motor speed, or pressure.
16 . The method of claim 5 , wherein the flight phase standard includes a flight phase performance index used to determine a severity of a discrepancy between at least one of the plurality of flight phases and the flight phase standard.
17 . The method of claim 5 , further comprising:
providing, using the computing device, the anomalous source to a remote device communicatively connected to the electric aircraft; and
determining, using the remote device, an improvement to a flight phase of the plurality of flight phases corresponding to the anomalous flight phase.
18 . The method of claim 17 , wherein the anomalous flight phase includes a maintenance issue, and wherein the improvement to the flight phase includes an adjustment to a maintenance phase.
19 . The method of claim 5 , wherein the flight component discrepancy data further includes flight component performance values exceeding or falling below at least one additional component parameter of a plurality of component parameters.
20 . The method of claim 19 , wherein the plurality of component parameters include torque, power consumption, motor speed, or pressure.