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Matteo Corbetta

Publications and source records attributed to Matteo Corbetta.

17 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗
NASA NTRS2023

An Accelerated Life Testing Dataset for Lithium-Ion Batteries With Constant and Variable Loading Conditions

The dataset repository is organized into three main folders, each containing one group of life cycled battery packs. Within each folder individual battery packs own their dedicated csv file for continuous data logging, which are named with their respective battery pack number. The folders are named: - regular_alt_batteries: Containing one csv file for each battery pack cycled at the same load level or load range throughout lifetime - recommissioned_batteries: Containing one csv file for each battery pack cycled at different load levels at varying life stages - second_life_batteries: Containing one csv file for each second life battery pack cycled at constant current througout the second life

Li-ion Battery↗
NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Isolation↗
NASA NTRSDate not supplied

Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory

One of the foremost requirements for accurate in-flight safety monitoring of autonomous unmanned aerial vehicles (UAVs) is tracking of their flight trajectory. Existing UAVs leverage autonomous flight functionalities based on trajectory generation algorithms developed in robotic applications such as polynomial or spline curves in order to facilitate kinematic smoothness, minimum vibrations and fuel efficiency. However in practice, the actual path may be subjected to unexpected local weather conditions, unexpected obstacles along the path or abrupt traffic changes in the low-altitude airspace resulting in large errors of the predicted time-of-arrival at way-points. In this study, an approach to quantify and propagate uncertainty in 4D trajectories is proposed. The paper presents a simple error interval propagation method based on the expected cruise speed of the UAV and its associated uncertainty. The uncertainty is then propagated in time to estimate reasonable confidence intervals on the times-of-arrival of the vehicle at each way-point as well as along the entire flight-path. The uncertainty propagation is demonstrated on a state-of-the-art trajectory generation algorithm based on non-uniform rational B-spline (NURBS) curves. Further, the effect of a stationary wind field is incorporated in the uncertainty propagation approach. The proposed method is implemented on synthetic and real data obtained from flight experiments with a small UAV.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
NASA NTRSDate not supplied

Uncertainty Propagation in Pre-Flight Prediction of Unmanned Aerial Vehicle Separation Violation

Current forecasts on the future of aeronautics suggest an in- creasing number of unmanned aerial vehicles entering the low- altitude airspace in the next decades (FAA, 2018; Kopardekar et al., 2016). Small vehicles for package delivery as well as larger vehicles for urban air mobility will change the airspace drastically, increasing density of operations both in time, i.e. high number of take-off and landings per unit time, and in space, operating in dense urban environment. This scenario poses challenges to the current approach to air traffic control, and large efforts from academia, industry and regulatory bodies are dedicated to the development of new traffic management strategies that leverage higher computing and simulating capabilities available today. In this paper, we propose a simple look-ahead approach to predict potential minimum separation violations at the strategic level, that is before vehicles start flying, depending on the predefined 4D trajectories and uncertainty affecting the wind acting along those routes. The wind field is extracted from the NOAA North America Mesoscale Forecast System and interpolated using Gaussian process regression, while uncertainty affecting the expected cruise airspeed is propagated through error intervals. The approach allows the prediction of aircraft separation as a function of time, highlighting potential safety violations that would go undetected if uncertainty affecting the expected 4D trajectories is not considered. The paper will also discuss issues related to accuracy and scalability of the approach to multiple vehicle operations.

Trajectory Prediction↗
NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRSDate not supplied

Usage-based Lifing of Lithium-Ion Battery with HybridPhysics-Informed Neural Networks

Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs).The ability to model and forecast the remaining useful life of these batteries enables UAV reliability assurance. Building accurate models for battery state of charge and state of health based on first principles is challenging due to the complex electrochemistry that governs battery operations and computational complexity required to solve them. Therefore, reduced order models are often used due to their ability to capture the overall battery discharge. Un-fortunately, these simplifications lead to residual discrepancy between model predictions and observed data. In this paper, we present a hybrid modeling approach merging reduced-order models and neural networks. In this approach, while most of the input-output relationship is captured by Nernst and Butler-Volmer equations, data-driven kernels reduce the gap between predictions and observations. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence repository. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.

Lithium-ion Battery↗
NASA NTRSDate not supplied

Probability of Obstacle Collision for UAVs in Presence of Wind

For incorporation of unmanned aerial vehicles into the National Airspace, ensuring safety of the airspace including the vehicles, people, and property on the ground is of utmost importance. One of the safety-critical factors for unmanned aviation flights is the risk of deviating from a planned trajectory resulting in a variety of hazards, including potential loss of separation between vehicle and obstacles or unexpected battery energy consumption. Off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts can lead to unacceptable unexpected deviations from the flight trajectory. It is essential to accurately model such effects on the flight trajectory while computing safety thresholds such as minimum separation from surrounding obstacles, available battery resource to complete the mission or determining delay in the expected time of arrival of flights. In this paper, a tool is presented based on Gaussian Process Regression for wind representation over a pre-defined trajectory for fast, yet approximated, in-time evaluation of possible trajectory deviations caused by wind gusts. The deviation in the planned trajectory caused by wind is further simulated utilizing a 6 degrees-of-freedom (DOF) UAV trajectory simulator comprising of a rotorcraft lumped-mass model with LQRI controller. Both steady-state wind and wind gust effects are investigated. The probability of collision with obstacle is computed and demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in the presence of simulated obstacles and wind conditions. Effect of varying wind conditions and varying UAV airspeed is further demonstrated on experimental flights in the presence of wind measured by ground based weather service stations. The proposed approach would eventually benefit timely mitigation of current and future safety-critical events in autonomous systems by enabling risk-informed decision making.

Portia Banerjee↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
NASA NTRSDate not supplied

Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics

This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.

Physics-Informed Machine Learning↗