Flight Log Research Articles

Flight test for the noise source modeling of the optionally piloted personal air vehicle

An acoustic flight test is carried out in order to obtain the noise hemisphere of the optionally piloted personal air vehicle (OPPAV). The OPPAV is one-seater, research urban air mobility (UAM) being developed by Korea Aerospace Research Institute. Noise radiated from the aircraft is measured using a microphone array consisting of a total of 78 microphones. Flight logs such as the aircraft position, velocity, and attitude are recorded synchronously with the acoustic signals. Measured acoustic signals are dedopplerized and divided into a number of segments. Noise source model for the OPPAV flight is constructed using these segments. The noise source model will be used for the assessment of noise annoyance during urban flight of the UAMs.

Learning-based modeling and control design for a coaxial helicopter with aerodynamic coupling

In this paper, a mathematical model of a coaxial helicopter with a new configuration is successfully constructed using the learning-based method. This nonlinear model can reflect the aerodynamic coupling characteristics between the two rotors of the coaxial helicopter. Compared with the traditional polynomial fitting method, this method significantly improves the integrity and robustness of the mathematical model. After multiple tune of the aircraft, we successfully completed the stable flight experiment using the designed attitude controller. By analyzing the real-time flight log, we verify the high accuracy of the coupled model trained on neural networks. The results of this research have brought important enlightenment to the field of aircraft design and control and proved the great potential of neural network in improving the accuracy of aircraft model and control efficiency.

Building causal models for finding actual causes of unmanned aerial vehicle failures.

Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause-effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause-effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause-effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.

A Conceptual Digital Forensic Investigation Model Applicable to the Drone Forensics Field

Although there is a considerable amount of studies in drone forensics that describe numerous practical and technical perspectives, there is a lack of a comprehensive investigation framework. This study used design science research methodology to design a conceptual model for the comprehensive investigation of Unmanned Aerial Vehicles (UAVs) under forensic conditions. This model can identify, capture, preserve, analyze, and document UAV incidents. The proposed model consists of four stages: preparation, data collection, analysis, and documentation. In the preparation stage, data are collected and analyzed about UAV-related resources, including the origin and model of the aircraft, any software or hardware installed onboard, and the legal framework and regulations in place. The data collection stage involves the completion of the collection process, where participants gather parts of the UAV and the data needed, such as the flight controller, flight log, and memory cards. The analysis stage involves analyzing the collected evidence. Lastly, the documentation stage involves documenting relevant evidence, analysis results, and any conclusions derived. This model provides a comprehensive process to forensically investigate UAV incidents and provides an efficient and effective approach to the analysis of UAV evidence, ensuring that evidence was collected and analyzed according to accepted forensic techniques. The proposed model can be applied to any UAV type and legal framework.

High-Power Microwave Pulse-Induced Failure on Unmanned Aerial Vehicle System

With the wide application of unmanned aerial vehicles (UAVs), more attention has been paid to the safe operation of UAVs in complex electromagnetic environments. In this article, we investigate the impact of high-power microwave (HPM) pulses on three types of typical commercial UAVs. We established the HPM irradiation experiment system first and obtained the responses of UAVs under the HPM attack with different electric field strengths. The flight logs and the damage analysis reports indicate that the damaged components of the type I UAV and the type II UAV are the rotor motor and the electronic speed controller. The experiment results prove that L-band narrowband HPM microwave has strong backdoor coupling effects on UAVs. The damage mechanism of UAVs by the HPM is studied by simulation. The simulation results show that an HPM pulse will cause a high-voltage pulse coupled to the cable connecting the electronic speed controller and the rotor motor, which caused the electronic speed controller to send out a false signal that caused the rotor motor speed to rise sharply, eventually making the rotor motor to burn out. Experiment results show that strengthening electromagnetic protection on the cable can effectively prevent HPM damage to UAVs. The simulation results match the experiment results and the damage analysis reports. Finally, some suggestions are provided to improve the UAV’s anti-HPM capability.

DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device

DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This model is hosted on the HuggingFace platform to make it publicly available and accessible. The tool expects decrypted DJI flight log files as input and generates a forensic report in a PDF containing a forensic timeline highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">With the increasing deployment of small Unmanned Aerial Systems (sUAS) on various tasks, it becomes crucial to analyze and detect anomalies from their flight logs. To support research in this area, we curate DLA, the first real-world time series anomaly detection dataset in the domain of sUAS, which contains 41 sUAS flight logs annotated with various types of anomalies. As anomalies tend to occur in low-density areas within a distribution, we propose Graphical Normalizing Flows (GNF), a graph-based autoregressive deep learning model, to perform anomaly detection through density estimation. GNF contains (1) a temporal encoding module using a transformer to capture the temporal dynamics, (2) an inter-feature encoding module leveraging graph representation learning on a Bayesian network to model the statistical dependencies among time series features, and (3) a density estimation module with Normalizing Flows. Extensive experiments have demonstrated GNF’s superior anomaly detection power on DLA compared with state-of-the-art baselines.</i>

Design and implementation of a hardware-in-the-loop simulation platform for a tail-sitter UAV

Purpose A high-fidelity simulation platform helps to verify the feasibility of the controller and reduce the cost of subsequent experiments. Therefore, this paper aims to design a high-fidelity hardware-in-the-loop (HIL) simulation platform for the tail-sitter vehicles. Design/methodology/approach The component breakdown approach is used to develop a more reliable model. Thruster dynamics and ground contact force are also modeled. Accurate aerodynamic coefficients are obtained through wind tunnel tests. This simulation system adopts a mode transition method to achieve continuous simulation for all flight modes. Findings Simulation results are in good agreement with the flight log and successfully predict the state of the vehicle. Originality/value First, the effects of the propeller slipstream are considered. Second, most researchers ignore the parasitic drag caused by the landing gear and other appendages, which is discussed in this study. Third, a ground contact model is implemented to allow a realistic simulation of the takeoff and landing phases. Fourth, complete wind tunnel tests are conducted to obtain more accurate aerodynamic coefficients. Finally, a mode transition method is deployed in the HIL simulation system to achieve continuous simulation for all flight modes.

Transformer-Based Named Entity Recognition on Drone Flight Logs to Support Forensic Investigation

The increase in drone usage by the public brings the number of drone incident and attack up. Sophisticated preventive mechanisms, as well as post-incident procedures and frameworks, are needed. Forensic investigation is performed upon a drone incident, aiming to uncover the incident scenario, mitigate the risk and report the examination results. Generally, standard drone forensic procedure consists of three stages, i.e., evidence acquisition, evidence analysis, and reporting. Among the existing research, many attempts have been made in framework proposal and evaluation, study case, and tools proposal and evaluation. However, less research focuses on utilizing specific data artifacts from the drone forensic image, such as telemetry, dataflash, and flight log data. Therefore, this research aims to propose the use of log message data to discover and extract some incident-related information using a deep learning-based NLP technique, i.e., named entity recognition using the Transformer. Cosine similarity is proposed as a substitute for dot-product in the self-attention mechanism of the Transformer encoder layer. Additionally, we propose NER architecture built from a mix of several existing methods and report the performance evaluation. We extract the DJI drone forensic image from a publicly available dataset using Autopsy and DJI Phantom Help and collect the decrypted log messages. Six entity types are defined after carefully reading the log message. These entity types are used in the manual annotation process using the IOB2 scheme as the label. The constructed dataset is used to evaluate the proposed model along with several baseline models. The proposed method outperforms the previous baseline model with a 91.348% F1 score. Finally, we conclude the experiment and mention several future directions.

Development of an unmanned aerial vehicle for remote live streaming on web dashboard

As the prominence of autonomous aerial vehicles continues to rise globally, the ability of surveillance carried out remotely is likely to become a key aspect. In large spaces, random security checks are performed by security guards and are quite inefficient since on-foot monitoring is extremely non-productive and slow. This paper proposes a remote surveillance solution integrated into an aerial vehicle that performs random inspections and streams the live video on a web dashboard which is a faster and more reliable solution. The paper presents how to design and implement an Unmanned Aerial Vehicle (UAV) with surveillance capabilities wherein the live stream will be remotely accessible on any device connected to the same network. The main purpose of this drone is to transmit live data/video to a web dashboard and hence run inferences to analyse the data. A custom-made drone is integrated with Raspberry Pi with a Universal Serial Bus (USB) camera. An attempt to stream drone video feeds on local devices has been made in this field. The proposed framework operates in the robot operating system (ROS) integrated with the gazebo simulator and is designed to enable the software to communicate with the hardware. Two primary methodologies are utilised; simulations and experimental. The expected outcome should satisfy the need to view the drone’s live video feed on a web dashboard for remote surveillance and computer vision application. At the resolution of 640 × 480, the latency in the live stream varied from 1 to 3 s with 10 frame rates per second. The analysis of the flight logs of the drone suggests that it meets the desired stability required for smooth video streaming. The total estimate calculated to implement this system is Rs. 70,000 including contingencies and spare parts required for experimentation. The proposed prototype appears to be a viable solution for security solutions required by multiple large space owners on the study's findings.

Pressurization System on Aircraft Boeing 737-300F, Aircraft MSN: 28567 During Flight Hanoi to Bangkok: Problem Solving and Root Cause Analysis

The aircraft, with type Boeing 737-300 freighter, Serial Number: 28567, Flight number: TMG-918, with a flight route from Hanoi International (HAN) to Bangkok International airport (BKK) on the date of 12 April 2021, with a take-off time of 22.00 UTC and landing time 22.25 UTC, is operated by PT XYX Airlines on a non-schedule flight. After taking off from Hanoi to Bangkok (HAN – BKK), with an altitude of nearly 10.000 ft flight level, the aircraft got a problem with the pressurization system in auto mode (auto system), and the cabin altitude indicator was shown 10.000 ft; cabin rate altitude indication at 2000 – 300 ft/minute (in normal rate 200 – 300 ft/minute). The pilot was unable to control the pressurization system in the auto system; in this condition, the Pilot followed the procedure for selecting the pressurization system from auto mode to manual mode, and after that, the cabin altitude indicator suddenly was shown fluctuated or became unstable. After the pressurization system operates in Manual mode and all conditions control by the flight crew, the captain makes the decision to aircraft return to base (RTB) flight back to Hanoi International airport (HAN) and land safely at 22.25 UTC. The author wants to analyse the root cause of the pressurization system problem by using several methods, as per 5 whys, fishbone diagrams, and fault tree analysis. Then the author compares the root cause results of each method to take corrective and preventive actions to solve the problems. All these cases were in Reference to SDR Report Control Number: 012/SDR-YGH/TMG/IV/2021. The report submitted by PT.Tri M.G. Intra Asia Airlines on 12 April 2021 and the Flight Log Manual (FML) Page No: 03232, dated 12 April 2021. The purpose of this research is to identify the problem and determine the root cause of the problem's inability to control the pressurization system and make a plan for corrective action and preventive action; then, the team will implement the corrective action and preventive action. Quality Division will check and monitor the effectiveness of corrective and preventive action in three months. After that, the team &amp; Quality Division create the procedure and policy for the company standard. The author and team used the methodology 5 why (cause and effect diagram), Fishbone diagram (Ishikawa Diagram), and Fault Tree Analysis (FTA), and from each method will be found the root cause analysis. Then the author combines all root cause analyses to identify the problem and create problem-solving. The main result of this research was that the company was able to determine and implement the corrective action and preventive action for the uncontrollable problem pressurization system. The problem of the pressurization system that cannot be controlled causes the aircraft to return to base or cancel a flight to be expected not to happen again in all aircraft operated by PT. XYX company in the future.

Wind Speed Measurement by an Inexpensive and Lightweight Thermal Anemometer on a Small UAV

Profiling wind information when using a small unmanned aerial vehicle (sUAV) is vital for atmospheric profiling and monitoring attitude during flight. Wind speed on an sUAV can be measured directly using ultrasonic anemometers or by calculating its attitude control information. The former method requires a relatively large payload for an onboard ultrasonic anemometer, while the latter requires real-time flight log data access, which depends on the UAV manufacturers. This study proposes the feasibility of a small thermal anemometer to measure wind speeds inexpensively using a small commercial quadcopter (DJI Mavic2: M2). A laboratory experiment demonstrated that the horizontal wind speed bias increased linearly with ascending sUAV speed. A smoke experiment during hovering revealed the downward wind bias (1.2 m s−1) at a 12-cm height above the M2 body. Field experiments in the ice-covered ocean demonstrated that the corrected wind speed agreed closely with the shipboard wind data observed by a calibrated ultrasonic anemometer. A dual-mount system comprising thermal anemometers was proposed to measure wind speed and direction.

Neural Network Based Model Predictive Control for a Quadrotor UAV

A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.

Evaluation of nighttime helicopter noise in an urban neighborhood

Sound level measurements were gathered at two locations within an urban neighborhood close to a helipad. A sound level meter was placed at each location for approximately one week, where data were collected continuously from 10:00 PM to 6:00 AM in fifteen-minute intervals. L90 was measured to establish existing background levels and LAFmax was measured to capture the helicopter noise. Additionally, a sound level trigger was set up to capture details and audio recordings of elevated levels associated with helicopter flights. Due to the lengthy data collection period, care was taken to collect as much data as needed without exceeding the storage capacity of the meter. Based on preliminary data, the trigger was set to capture only events with a minimum sound pressure level and a minimum time duration, ensuring shorter noise events not of interest (barking dogs, passing cars, etc.) were not captured. Audio files for each sound event were used to identify helicopter events, which were confirmed by comparing timestamps with the helicopter flight logs. This case study describes the details of unattended long-term data collection for this application and evaluation of the helicopter noise measurements.

In-Flight Demonstration of Stall Improvement Using a Plasma Actuator for a Small Unmanned Aerial Vehicle

The flow control capability (especially for separation control) of a dielectric-barrier-discharge plasma actuator (DBD-PA) has been investigated extensively. However, these studies have been conducted under ideal conditions, such as wind tunnels and computational environments, and limited studies have investigated the effects of plasma actuators in an actual environment. In this study, the flow control capability of a DBD-PA under natural and in-flight conditions was investigated via field flight tests using an unmanned aerial vehicle (UAV). The DBD-PA driving system was constructed with a small high-voltage power supply on a 2-m-span UAV. With the support of an autonomous flight system, the pitch angle gradually increased as the airspeed decreased, and the stall occurred from the cruise state. This flight procedure was conducted with the DBD-PA on or off, and 246 pairs of flights were operated. The results revealed that a flight state with a higher pitch angle and lower airspeed occurred when DBD-PA was switched on. In addition, the moment of stall was quantitatively determined from the flight log, and it was confirmed that the maximum pitch angle when DBD-PA was switched on tended to be larger than that when DBD-PA was switched off. These results indicate that flow control with a DBD-PA on a 2-m-span UAV was effective in natural and in-flight situations.

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are seeing increased use in critical operations for law enforcement, military, industrial control surveillance and more. These hostile operating environments combined with the UAVs reliance on wireless protocols produces an increased threat level. Many attacks against the UAV are becoming commonplace as they are simple to conduct with inexpensive hardware, such as spoofing and jamming. Unfortunately, as many of these vulnerabilities exist within underlying technologies, securing the UAV becomes a difficult task. A promising approach to identifying and mitigating these attacks is the development of an intelligent intrusion detection system (IDS). The proposed approach uses principal component analysis (PCA) and one-class classifiers to detect attacks. This allows for the use of flight logs for training data, providing a versatile and ubiquitous approach. The proposed detection method is integrated into a fully developed IDS called MAVIDS. This IDS operates onboard the UAV within a resource-constrained agent device, allowing it to detect and potentially mitigate attacks even when communication to the ground control station is lost from jamming. The approach shows to be effective against GPS spoofing and jamming with macro averaged F1 scores of 90.57% and 94.3% respectively.

ANALISIS KEGAGALAN NOSE WHEEL STEERING SYSTEM PADA PESAWAT BOEING DENGAN MENGGUNAKAN METODE FAILURE MODE AND EFFECT ANALISYS

Aircfrat use nose wheel steering system when landing, take off and landing. In the research discuses the problem of nose wheel steering system B737 – 800 to minimize problems ini nose wheel steering system. The stage of this research are to study the work system of nose wheel steering. Then look for the cause of the problem using the FMEA analysis method. This research is based on data from AFML (aircraft Flight and Manual Log) of the B737 – 800 Aircraft from January 2020 to Junu 2021 at Lion Airline. The results showed that the problem of nose wheel steering system in the failure mode analysis process using FMEA method data in the highest RPN ( Risk priority Number) value is 175 with the case steering collar dan torQ link need lubrication and case Tire pressure that caused by the lack of lubrication on the component and different pressure in tire. Then it is necessary to do inspection before flight.

Use of Helicopter Medical Transportation to the Neuroscience Intensive Care Unit.

Helicopter medical transportation (HMT) is a valuable resource that can expedite medical care by shortening transferring times. However, there is conflicting evidence regarding its cost and efficacy. No specific studies have addressed its use in patients transferred to the neuroscience intensive care unit (NSICU). This was a retrospective study performed at a university hospital in the coastal southeastern USA. The flight logs for the air ambulance company were reviewed, and all patients undergoing HMT to the NSICU during a 1-year period were identified. Flight logs and medical records were reviewed to obtain basic demographics, diagnosis, mortality, transportation distance, and performance of time-sensitive interventions (TSIs) to include ventriculostomy placement, emergency craniotomy, emergency craniectomy, emergency aneurysm obliteration, emergency spine surgery, emergent endovascular procedures, subdural drain placement, emergent shunt revision, and continuous electroencephalography (EEG) performed within 4h of admission. We analyzed the cost of HMT and correlated the clinical variables with the performance of TSIs. A total of 101 patients underwent HMT during a 12-month period; 26 underwent 30 TSIs (4 underwent 2 TSIs) and an additional 4 were transported for EEG monitoring. The only clinical variable associated with a TSI was subarachnoid hemorrhage with ventriculostomy placement. Continuous EEG monitoring performed in four patients with suspected status epilepticus did not show status epilepticus in any of them (one was performed after 4h). Transportation distance was less than 60 miles in four patients who underwent TSIs. The total cost of HMT was $3,360,573: $842,672 for those who underwent TSIs and $2,517,901 for those who did not. When compared with ground transportation, an excess cost of $3,129,415.25 was incurred by using HMT. There was no difference in the median cost of transportation between patients who underwent TSIs and those who did not ($30,210 vs. $30,211). The median cost transportation difference between HMT and ground transportation was significantly different (p < 0.001), with a median excess cost of $28,023 (range $15,553-76,155) per patient. The majority of patients who were transferred via HMT did not undergo TSIs, and among those who underwent TSIs, approximately one in six was transported from a hospital located less than 60 miles away from the NSICU; the distances of ground and air transportation are equivalent. Helicopter transfers may play a role in subarachnoid hemorrhage management. A significant expense was incurred by using HMT for the majority of patients (75%) who did not undergo TSIs.

Evaluation method of military aircraft noise using AI analysis of aircraft images

In recent aircraft noise survey in Japan, noise data is associated with each aircraft by flight log or by radio information including transponder signals. Especially, above Tokyo metropolitan area, flight tracks are tangled extremely each other, therefore assessments from various perspectives such as departure / arrival airport, used runway, aircraft model, and operator have been demanded for determining noise policies. However, for military aircrafts, it is not easy to identify their information with the same way as commercial aircrafts, because their flight logs are not disclosed and many of them do not emit transponder signals like commercial aircrafts. Therefore, manned 24 hours survey around air bases have been necessary to obtain flight information of military aircrafts. In this paper, we propose an AI-based analysis using captured aircraft images for obtaining actual flight data of military aircrafts. In the past trials, we could determine the takeoff/landing time and the aircraft model by the above method. Associating these information and noise data measured at monitoring stations, details of noise characteristics around the air base can be clearly grasped. Advanced analysis of the causes of noise impact will lead effective and concrete countermeasures.