Aircraft health monitoring and design for condition
View Patent ↗A system and method for automatically varying the flight envelope of an aircraft based upon the material health of the aircraft and the flight environment is provided. The system includes a plurality of structural health monitoring and load sensors that determine the approximate size and the approximate location of the damage. The system performs residual strength calculations for individual aircraft components to determine the overall aircraft residual strength. The system uses these calculations to determine a maximum flight envelope based on the overall aircraft residual strength, and transmits this information to the flight controller and optionally to the pilot.
1. A monitor system for determining a residual strength of an aircraft, the aircraft comprising a plurality of panels, and the system comprising:
a plurality of sensors mounted on a first panel; and
a microprocessor in communication with each of the plurality of sensors,
wherein each of the plurality of sensors is configured to detect time domain data, frequency domain data, and energy domain data, and to transmit the detected time domain data, frequency domain data, and energy domain data to the microprocessor, and
wherein the microprocessor is configured to process the detected time domain data, frequency domain data, and energy domain data and, when the first panel has been damaged, to use the processed data to determine (i) an approximate size of the damage and (ii) an approximate location of the damage, and to use the two determinations to further determine (i) an aircraft residual strength and (ii) a flight loading for which the determined aircraft residual strength allows
further comprising a flight control subsystem, the flight control subsystem being in communication with the microprocessor, wherein the microprocessor is further configured to communicate the processed data to the flight control subsystem, and wherein the flight control subsystem is configured to use the received data to limit maneuverability of the aircraft.
2. The monitor system of claim 1 , wherein the microprocessor is further configured to use the processed data to determine that a specific maintenance action should be taken to address the damage.
3. The monitor system of claim 1 , wherein each of the plurality of sensors is further configured to emit an acoustic wave signal and to receive a wave return signal corresponding to the emitted signal, and to measure an amplitude, a frequency, and a phase of the received wave return signal.
4. The monitor system of claim 3 , wherein the microprocessor is further configured to process the detected data by using at least one of a guided wave (GW) method, an acoustic emission (AE) method, and a frequency response (FR) method.
5. The monitor system of claim 3 , wherein for each of the plurality of sensors, the detected time domain data includes a set of measurements of differences between a time at which each acoustic wave signal is emitted and a time at which each respective corresponding wave return signal is received.
6. The monitor system of claim 3 , wherein for each of the plurality of sensors, the detected frequency domain data includes at least one of a data set selected from the group consisting of (i) a set of measurements of frequency shifts between a frequency at which each acoustic wave signal is emitted and a frequency at which each respective corresponding wave return signal is received, (ii) a phase at which each acoustic wave signal is emitted and a phase at which each respective corresponding wave return signal is received, and (iii) a power spectral density data set.
7. The monitor system of claim 3 , wherein for each of the plurality of sensors, the detected energy domain data includes at least one of a data set selected from the group consisting of (i) a set of measurements of amplitude shifts between an amplitude at which each acoustic wave signal is emitted and an amplitude at which each respective corresponding wave return signal is received, (ii) a mean amplitude shift, and (iii) a standard deviation of amplitude shift.
8. The monitor system of claim 1 , wherein the microprocessor is further configured to store a baseline data set, the baseline data set including sensor data obtained when the first panel included no damage, and wherein the microprocessor is further configured to determine that the first panel has been damaged when an average difference between newly received data and baseline data exceeds a first predetermined threshold.
9. The monitor system of claim 8 , wherein, when the microprocessor determines that the first panel has been damaged, the microprocessor is further configured to determine an approximate size of the damage and an approximate location of the damage by using at least one algorithm selected from the group consisting of a balanced one-way Analysis of Variances (ANOVA) algorithm, a Principal Components Analysis (PCA) algorithm, and a Pattern Recognition (PR) algorithm.
10. The monitor system of claim 9 , wherein the microprocessor is further configured to use a K-Nearest Neighbor (KNN) pattern recognition algorithm.
11. The monitor system of claim 1 , wherein the first panel includes at least one sensor per three square feet of first panel surface area.
12. A method for determining a residual strength of an aircraft, the aircraft comprising a plurality of panels, each of the plurality of panels having a plurality of sensors mounted thereon, and the method comprising the steps of:
using each of the plurality of sensors to detect time domain data, frequency domain data, and energy domain data;
transmitting the detected time domain data, frequency domain data, and energy domain data to a microprocessor;
using the microprocessor to process the detected time domain data, frequency domain data, and energy domain data;
when a panel has been damaged, using the processed data to determine (i) an approximate size of the damage and (ii) an approximate location of the damage;
using the two determinations to further determine (i) an aircraft residual strength and (ii) a flight loading for which the determined aircraft residual strength allows; and
using the processed data to limit a maneuverability of the aircraft.
13. The method of claim 12 , further comprising the step of using the processed data to determine that a specific maintenance action should be taken to address the damage.
14. The method of claim 12 , wherein the step of using each of the plurality of sensors to detect time domain data, frequency domain data, and energy domain data further comprises using each of the plurality of sensors to emit an acoustic wave signal and to receive a wave return signal corresponding to the emitted signal, and to measure an amplitude, a frequency, and a phase of the received wave return signal.
15. The method of claim 14 , wherein the step of using the microprocessor to process the detected data further comprises using at least one of a guided wave (GW) method, an acoustic emission (AE) method, and a frequency response (FR) method.
16. The method of claim 14 , wherein for each of the plurality of sensors, the detected time domain data includes a set of measurements of differences between a time at which each acoustic wave signal is emitted and a time at which each respective corresponding wave return signal is received.
17. The method of claim 14 , wherein for each of the plurality of sensors, the detected frequency domain data includes at least one of a data set selected from the group consisting of (i) a set of measurements of frequency shifts between a frequency at which each acoustic wave signal is emitted and a frequency at which each respective corresponding wave return signal is received, (ii) a phase at which each acoustic wave signal is emitted and a phase at which each respective corresponding wave return signal is received, and (iii) a power spectral density data set.
18. The method of claim 14 , wherein for each of the plurality of sensors, the detected energy domain data includes at least one of a data set selected from the group consisting of (i) a set of measurements of amplitude shifts between an amplitude at which each acoustic wave signal is emitted and an amplitude at which each respective corresponding wave return signal is received, (ii) a mean amplitude shift, and (iii) a standard deviation of amplitude shift.
19. The method of claim 12 , further comprising the steps of
using the microprocessor to store a baseline data set for each panel, the baseline data set including sensor data obtained when the respective panel included no damage, and
using the microprocessor to determine that the respective panel has been damaged when an average difference between newly received data and baseline data exceeds a first predetermined threshold.
20. The method of claim 19 , wherein when the microprocessor determines that the respective panel has been damaged, the step of using the microprocessor to determine an approximate size of the damage and an approximate location of the damage further comprises using at least one algorithm selected from the group consisting of a balanced one-way Analysis of Variances (ANOVA) algorithm, a Principal Components Analysis (PCA) algorithm, and a Pattern Recognition (PR) algorithm.
21. The method of claim 20 , further comprising the step of using a K-Nearest Neighbor (KNN) pattern recognition algorithm.
22. The method of claim 12 , wherein each panel includes at least one sensor per three square feet of panel surface area.