Foreign Object Debris Research Articles

The development, evaluation and application of an aviation radiotelephony specialised technical vocabulary list

International Civil Aviation Organisation (ICAO) standard phraseology is an integral element of aviation radiotelephony (two-way radio) communication between air traffic controllers and other aviation personnel. A key issue with standard phraseology in this context is uncertainty as to its role. This is important in the aviation setting as miscommunication can lead to accidents. This paper presents a corpus-based Tower Aviation Radiotelephony Technical Vocabulary List (TARTVL) that is derived from ICAO standard phraseology. The list includes: technical words e.g. taxiway, proper noun classifications e.g. aircraft type, number classifications e.g. headings, acronyms e.g. FOD (Foreign Object Debris) and multiword units e.g. lineup and wait. Evaluation of the list shows that it is a promising training tool. The article discusses how the TARTVL can be used by English for specific purposes (ESP) and content instructors to provide aviation radiotelephony language training for ab initio, first language and non-native English speaking aviation personnel. Such training could help reduce miscommunication in multilingual workplaces, thus enhancing safety in aviation.

Towards optimal foreign object debris detection in an airport environment

Foreign object debris (FOD) can critically damage aircraft engines as well as injure personnel in an airport environment. Airfield inspectors routinely inspect runways for the presence of FOD items, which differ in material, shape, and color, using conventional and automated methods. The major shortcoming of the current methods is their inability to detect all types of foreign objects in an accurate and timely manner for removal from the airport runways. In this study, we address this shortcoming, i.e., the lack of accuracy and timeliness in detection, by developing an object detection framework to detect FOD for quick removal from the airfields. Our proposed FOD detection framework consists of (i) unmanned aerial system (UAS) for inspecting and collecting data from the airfields, (ii) data preprocessing and augmentation techniques to counter the issue of learning on limited types of foreign objects, weather conditions, and airport surface materials that are present in the data sets, and (iii) a computer vision-based object detection model to attain high accuracy and faster inference time for deployment in a real-world airport environment. We generated the training data with the UAS at an air force range and developed various models, including the You Only Look Once (YOLO) object detector family of models (one-stage object detectors) in our framework. Our models are evaluated on previously unseen data collected by the UAS and a publicly available data set of FOD images. The experiment results demonstrate that our proposed approach with a version of the YOLO model (YOLOv4) with transfer learning provides a faster inference time for FOD detection and outperforms the other models in the precision and recall metric values.

Forecasting airport pavement repair hours

Asphaltic airport pavements are difficult to maintain because they are often damaged by the cyclical loads from aircraft and specialized maintenance services vehicles as well as local climatic conditions (Chen et al., 2019; Horonjeff et al., 2010; Sulejmani et al., 2020). During winter, the use of de-icing agents to restore pavement skid resistance may also cause chemical damage to the pavement surface and produces Foreign Object Debris (FOD). The asphalt binders at the pavement surface are also susceptible to oxidative aging when they are exposed to high temperatures and cyclical heat and moisture (An et al., 2021; Kandhal and Chakraborty, 1996; Lu et al., 201 5; Omranian et al., 2020). Hence, airport pavements require systemic inspection at planned intervals and timely repairs for aircraft safety. At busy airports, pavement repairs are usually carried out during off-peak hours to minimize disruption to the aircraft maneuvering areas. Because of the limited repair window, it may not be possible to rectify some non-critical pavement distress within the available time. Currently, repairs are prioritized according to the severity of pavement distress using a simple rating scale (FAA, 2014a), and the repair hours are estimated based on experience or expert judgments. The accuracy of such assessments depends on the traffic environment, the repair technologies, and improvements in pavement design and materials. Hence, this paper proposes an improved method to forecast pavement repair hours that uses technical change, pavement condition, aircraft traffic, and rainfall, including their lagged values, as independent variables. Technical change is modeled as an exogenous variable that changes over time. Because of endogeneity, the structural feedback equations are estimated using the Vector-Autoregressive (VAR) modeling method (Floyd, 2005; Triacca, 2014; Trinh, 2018). The model is tested using five years of monthly data (60 observations per variable) collected at Changi Airport for asphaltic airport pavements in tropical environments. The major findings are that technical change, the lagged value of pavement condition, and the change in aircraft traffic do not explain why the repair hours are increasing over time. The main determinants of repair hours are rainfall and previous repair hours. Since the monsoon periods in Singapore coincide with the holiday seasons, more pavement distress is likely to occur due to the combined damage of traffic loads and the presence of excessive moisture.

A Pixel-Wise Foreign Object Debris Detection Method Based on Multi-Scale Feature Inpainting

In the aviation industry, foreign object debris (FOD) on airport runways is a serious threat to aircraft during takeoff and landing. Therefore, FOD detection is important for improving the safety of aircraft flight. In this paper, an unsupervised anomaly detection method called Multi-Scale Feature Inpainting (MSFI) is proposed to perform FOD detection in images, in which FOD is defined as an anomaly. This method adopts a pre-trained deep convolutional neural network (CNN) to generate multi-scale features for the input images. Based on the multi-scale features, a deep feature inpainting module is designed and trained to learn how to reconstruct the missing region masked by the multi-scale grid masks. During the inference stage, an anomaly map for the test image is obtained by computing the difference between the original feature and its reconstruction. Based on the anomaly map, the abnormal regions are identified and located. The performance of the proposed method is demonstrated on a newly collected FOD dataset and the public benchmark dataset MVTec AD. The results show that the proposed method is superior to other methods.

Development of Korea Airport Pavement Condition Index for Panel Rating

Airports strive to prevent safety issues, such as foreign object debris (FOD), by pavement management using the pavement condition index (PCI). The index is used in decision-making processes for overall pavement maintenance and repair, such as the prevention of additional damage due to cracks and the like. However, considering the current situation in Korea where mostly mid-sized and large commercial airports exist, problems regarding direct applications of the existing PCI deduct value have been consistently pointed out. In addition, as the relationship between the PCI and whether maintenance and repair are required is unrealistic, there have been difficulties in communication between maintenance and repair staff and decision makers. Therefore, to resolve these problems, this study first analyzed the calculation procedure of the existing PCI and then redefined the main distress type of Korean airport pavements. In addition, a deduct value curve (DVC) in terms of the severity level for six main distress factors of asphalt pavements and eight main distress factors of concrete pavements and a corrected deduct value curve (CDVC) for multiple distresses in terms of the pavement form were developed using panel rating, which is an engineering approach, by forming an airport pavement expert panel. Finally, a Korea airport pavement condition index (KPCI) was proposed using the curves, and the field application results were compared against the existing PCI to examine the adequacy of the KPCI. As a result, the developed criteria showed an overall trend lower than existing PCI. Moreover, it was verified that this trend increases with worsening pavement condition. It appears that a more discriminating evaluation may be possible when determining pavement conditions by PCI results of the developed criteria.

Foreign Object Debris Material Recognition based on Ensemble Learning Algorithm

The material characteristics of foreign Object Debris (FOD) are the essential criteria in determining the extent of an aircraft’s damage. Foreign object debris (FOD) can cause significant accidents and financial losses on airport runways. A new FOD material recognition strategy is proposed in this paper using an ensemble learning algorithm, namely KNN, Adaboost, and Random Forest Tree, to classify FOD images. In addition, this study uses different feature extraction methods like Linear Discriminant Analysis (LDA) and Gray-level co-occurrence matrix(GLCM) to extract FOD features. The KNN, Adaboost, and Random Forest Tree precision are 94.20%, 98.9%, and 99.7%, respectively. The dataset that was used has been collected by researchers from several datasets. As a result, the experiment results reveal that the proposed framework is effective and accurate. The results showed that the best classification machine algorithm is Random Forest Tree.

Improvement in the Self-healing Property of Plasma-Sprayed Environmental Barrier Coatings by SiC Addition

Environmental barrier coatings (EBCs) protect SiC/SiC ceramic matrix composites from steam oxidation. Yb2Si2O7 is a promising ceramic material for the top coating of the EBCs. However, thermal shock and foreign object debris can cause cracks on the surface of an EBC, thereby reducing its ability to overcome environmental barriers. It has been found that an EBC can be strengthened by adding SiC to its top coating; this addition heals the cracks present on the EBC when subjected to high-temperature oxidation. In this study, the SiC contents of Yb2Si2O7 coatings prepared using atmospheric plasma spraying (APS) were evaluated to identify the structure and composition of the coatings. The results of crack-healing tests show that the Yb2Si2O7/SiC granulated powder could retain SiC particles in the coatings after the spraying, whereas in Yb2Si2O7/SiC mixed powder, the SiC particles evaporated during APS, indicating that Yb2Si2O7/SiC granulated powder can be used as the feedstock powder in self-healing EBCs. The time-dependent changes in surface crack closure and Yb2Si2O7/SiC granular powder coating remediation caused due to high-temperature oxidation were also evaluated. It was found that the reliability of self-healing EBCs could be improved by appropriately controlling the particle size and SiC content in the coatings.

A Correction Method to Systematic Phase Drift of a High Resolution Radar for Foreign Object Debris Detection

Due to the small size and various types of foreign object debris (FOD), radar detection of FOD on airport runways is a great challenge, and there are often a large number of false alarms in the detection results. Arc-scanning synthetic aperture radar (AS-SAR) is an emerging method for detecting FOD targets, which achieves omnidirectional coverage with a very high azimuth resolution. However, this method faces a similar challenge. A direct way to reduce false alarms is to increase the detection threshold based on enhancing the target signal-to-noise ratio (SNR), and in this paper, the coherent accumulation of multiple images is used to improve the target SNR. The stable phase is also an important feature of the target distinguishing background. Therefore, it is important to maintain the stability of the target phase. Aiming at the systematic phase drift (SPD) caused by atmospheric disturbance and system hardware, a spatial and temporal model is established, a corresponding correction approach is proposed, and the performance of the correction approach is validated by field experiments.

Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest.

In recent years, aviation security has become an important area of concern as foreign object debris (FOD) on the airport pavement has a huge potential risk to aircraft during takeoff and landing. Therefore, accurate detection of FOD is important to ensure aircraft flight safety. This paper proposes a novel method to detect FOD based on random forest. The complexity of information in airfield pavement images and the variability of FOD make FOD features difficult to design manually. To overcome this challenge, this study designs the pixel visual feature (PVF), in which weight and receptive field are determined through learning to obtain the optimal PVF. Then, the framework of random forest employing the optimal PVF to segment FOD is proposed. The effectiveness of the proposed method is demonstrated on the FOD dataset. The results show that compared with the original random forest and the deep learning method of Deeplabv3+, the proposed method is superior in precision and recall for FOD detection. This work aims to improve the accuracy of FOD detection and provide a reference for researchers interested in FOD detection in aviation.

Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario

Engine damage as a consequence of foreign object debris (FOD) during flight is frequently caused by birds. One approach to minimising disruption caused by this damage is to provide flight crew with accurate information relating to the continuing operational status of the aircraft’s engines. Before designing such avionic systems however, understanding of current procedures is needed. Hierarchical Task Analysis (HTA) and Systematic Human Error Reduction and Prediction Approach (SHERPA) were used to identify potential failures that flight crew may make when managing an engine bird strike. Workshops with commercial pilots generated insights into current practice and a commercial pilot SME reviewed outputs for accuracy. Over 200 potential failures were identified, most commonly related to communication. Remedial measures, considering future avionic systems, are proposed to mitigate identified failures. This analysis provides a starting point for future design concepts for assisting flight crew in dealing with engine malfunction due to FOD strikes. Practitioner summary: Hierarchical Task Analysis was conducted to show all tasks involved in dealing with an in-flight aircraft engine bird strike. Systematic Human Error Reduction and Prediction Approach analysis was performed and over 200 possible failures were identified when managing this event. Remedial measures are proposed to help mitigate possible failures.

Airport Runway Foreign Object Debris Detection System Based on Arc-Scanning SAR Technology

Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective.

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.

Detection of foreign object debris on night airport runway fusion with self-attentional feature embedding

Detection of foreign object debris on night airport runway fusion with self-attentional feature embedding

인공지능 기반 전시 활주로의 불발탄 및 폭파구 정량화에 관한 연구

At the beginning of the war, the runway of a military airfield becomes the top target for enemy missile strikes, and crater of various sizes are formed on the attacked runway, while many unexploded ordnance bombs remain. According to the existing airfield damage analysis technology to solve this problem, manual survey of unexploded ordnance and crater by reconnaissance team relying on visual observation and measuring tape measure, transmission of information about the size and location of crater by radio to the facility control room, and detonation by the facility control room Classification of old buildings, manual selection of MOS, and runway repair (damage restoration) process by the damage recovery team are sequential is done. However, the existing technology takes a long time to work due to the manual surveying of unexploded ordnance munitions and crater, and there is a high possibility that the reference point will be lost. There is a problem, and there is a possibility of secondary work by the information morning moon in the runway repair process. A fixed FOD(Foreign Object Debris) detection system can be applied to solve this visual inspection problem, but there is a problem in that the installation of a fixed tower itself is not preferred around the runway due to flight safety issues. This study is proposed to solve the above problems, and based on drone footage, artificial intelligence algorithm, and SfM(Structure from Motion) algorithm for point cloud generation, it is possible to analyze the damage at the emergency airfield in wartime and to develop an automated system for repairing damage to the flight pavement. Its purpose is to develop.

Impact of foreign object damage on the leading edge of TC11 titanium alloy aeroengine blade like specimen

ABSTRACT Foreign object damage (FOD) generally occurs when hard body particles are ingested into an aero-engine fan and compressor blade when the engine is taxing, flying, or moving on the runway with definite speed. Ingested foreign object debris induces severe structural damage on the leading edge of the aero-engine blade due to the time-dependent axial fatigue loading under service conditions. Induced residual stresses due to foreign object damage play a major role in mitigating the rate of fatigue crack initiation and propagation, resulting in failure of components. In this present investigation, an explicit module of commercial software package LS-DYNA was used to simulate the impact of the GCr15 stainless steel ball (52,100 steel equivalent) against leading-edge specimen (TC11 titanium alloy) by varying speeds like the in-service operating speeds. A comparative study was carried out among obtained deformed geometry (Crater) and its dimensions from the numerical simulation with experimentation results. Variation of residual stresses in three mutually perpendicular directions along with depth has been determined to predict the possible location of crack initiation at different projectile impact velocities. A stress concentration factor has also been calculated at the vicinity of the impact, which is very much an essential factor for the fatigue strength evaluation of TC11 titanium alloy.

Optically transparent thin film usage in FOD sensitive enclosures

We present a portable test setup used as a ground support equipment for the Solar Ultra Violet (SUVI) instrument on GOES-R series of satellites. The test setup is used as part of “remove before flight” or to package optical components for “install before flight”. The portable setup utilizes an inexpensive, commercially available thermo-plastic film that provides excellent barrier for moisture and debris. In addition to providing Foreign Object Debris (FOD) mitigation, this thermo-plastic film exhibits 1/10th wave optical performance. Most precision optical testing of instruments or subsystems can occur without exposing the systems to the environment.

Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection.

Unmanned Aircraft Systems (UAS) have become an important resource for public service providers and smart cities. The purpose of this study is to expand this research area by integrating computer vision and UAS technology to automate public inspection. As an initial case study for this work, a dataset of common foreign object debris (FOD) is developed to assess the potential of light-weight automated detection. This paper presents the rationale and creation of this dataset. Future iterations of our work will include further technical details analyzing experimental implementation. At a local airport, UAS and portable cameras are used to collect the data contained in the initial version of this dataset. After collecting these videos of FOD, they were split into individual frames and stored as several thousand images. These frames are then annotated following standard computer vision format and stored in a folder-structure that reflects our creation method. The dataset annotations are validated using a custom tool that could be abstracted to fit future applications. Initial detection models were successfully created using the famous You Only Look Once algorithm, which indicates the practicality of the proposed data. Finally, several potential scenarios that could utilize either this dataset or similar methods for other public service are presented.

Foreign Object Debris Automatic Target Detection for Millimeter-Wave Surveillance Radar.

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system’s detection method has a short effective range, and the detectable objects’ radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method’s effectiveness and the superior performance of FOD target detection in long-range situations.

Line of sight visibility analysis for foreign object debris detection system

It is challenging to monitor busy airports’ runway through visual inspection to precisely detect foreign object debris. Currently, many technologies for the detection of foreign object debris are available. It has been investigated that millimeter-wave radar technology’s detection capability can be one of the most effective techniques for detecting foreign object debris as it is weather-resilient. However, the positioning and height of a millimeter-wave radar pole covering the runway area, considering the existing runway infrastructure, are challenging. The task involves finding the appropriate placement and optimum height. This paper presents a novel method of line of sight visibility for placement and height of radar pole using human factor research to ensure that each point on the runway is visible from various heights of the millimeter-wave radar pole to the runway locations. Kuala Lumpur International Airport, Malaysia runway 32L/14R, has used a case study to test the visibility analysis. The visual analytic test’s successful results for different millimeter-wave radar pole locations and viewing heights under a visible and invisible line of sight conditions on the runway have been verified in the field experiment.

Review on Foreign Object Debris Detection Technologies and Advancement for Airport Safety and Surveillance

The rapid growth of air traffic worldwide leads to frequent landing and takeoff of airplanes at busy airports. Therefore, the airport runway must be safe and clean from any tiny size and shape of foreign object debris that is very hard to detect by visual inspection. For successful and fast removal of foreign object debris, an efficient system capable of precisely detecting foreign object debris is required. This paper presents a review of the available technologies to detect foreign object debris and compares commercially available foreign object debris detection technologies. The review has revealed that millimeter-wave radar technology is one of the most efficient foreign object debris detection techniques as it is weather resilient. Furthermore, millimeter-wave radar integrated with radio over fiber technology provides a cost-effective solution to detect foreign object debris within 10 seconds at 96 GHz frequency. A pilot test has been carried out at Narita International Airport, Japan and Kuala Lumpur International Airport, Malaysia, to detect foreign object debris either in a mobile or immobile position.