ERAU NASA In-Time Safety Management Data Development and Analysis Year Two

PI Kristy Kiernan

This project explores new data sources and analytical tools for extracting learning opportunities from all aviation operations.

This project explores new data sources and analytical tools for extracting learning opportunities from all aviation operations. To continue to learn from a system with very low mishap and incident rates, new data streams must be found that uncover strategies and practices that promote resilience. This project examines both existing mishap data, existing data collected from the NASA Human Contribution to Safety (HC2S) test bed, and data collected independently to identify realistic, actionable methods to support and encourage continuous learning, both at the operator level and the organizational level. The project will be divided into two tasks: Task 1 builds upon the work being done in the HC2S testbed by exploring the existing data and generating new data to address how resilient performance can be manifest at the level of the operator and also in the broader system; Task 2 examines NTSB accident dockets for evidence of resilient performance at the level of the operator and also in the broader system.

Researchers

Department
Human Factors and Behavioral Neurobiology
Degrees
Ph.D., M.A., University of Southern Mississippi
B.A., Nicholls State University
Department
Department of Aeronautics
Degrees
Ph.D., M.A.S., Embry‑Riddle Aeronautical University
B.S., Brown University
Department
Human Factors and Behavioral Neurobiology
Degrees
Ph.D., M.A., University of Illinois at Urbana-Champaign
Department
School of Graduate Studies (SGS)
Degrees
M.S., Embry‑Riddle Aeronautical University
Department
Department of Aeronautics
Degrees
Ph.D., University of North Dakota
M.S., Florida State University

Additional Researchers

  • CO-I Lucas Epperson
  • CO-I Shashank Kumar
  • CO-I Lidiaruth Jones
  • CO-I Sierra Juliano
  • CO-I Joseph O'Brien

Safety Culture Across Cultures: A Socio-Culturally driven International Validation of a Safety Culture Assessment & Improvement Survey

PI Mariateresa Sestito

While reported aviation accident rates have largely improved over time, industry statistics still indicate high accident rates and lack of continuous improvement among geographic regions. This context reflects a pressing need for interventions to improve safety performance in areas where progress has been stalemating.

Safety culture constitutes a current critical human factors challenge in commercial aviation, being recognized by the most prominent aviation organizations like ICAO, the FAA, and IATA. In environments with good safety culture, management and workers attitudes and behavior contribute to more effective identification and mitigation of safety risks. The goal of this research project is to study how national cultures permeate commercial aviation industry practices globally and influence safety culture. The understanding of safety culture as deeply embedded into cultural values gives a unique, multi-dimensional human socio-cultural perspective that can beneficially inform industry leadership practices. This will ultimately improve safety culture commitment and alignment at a more global, harmonized level according to the ICAO standards and recommended practices.

Mariateresa Sestito

Department
Boeing Center for Aviation & Aerospace Safety
Degrees
Ph.D., Università degli Studi di Parma
M.A., Università di Bologna

Department
Security Studies and International Affairs Dept
Degrees
Ph.D., George Mason University
M.A., Brandeis University
Department
School of Graduate Studies (SGS)
Degrees
Ph.D., Embry‑Riddle Aeronautical University
M., Instituto Tecnologico de Aeronautica (ITA)
M., B., Universidade Federal de Santa Catarina (UFSC)
Department
School of Business
Degrees
  • Ph.D., Doctor of Philosophy in Economics: General, Kansas State University

Additional Researchers

  • Michael Chrisman
  • Harrison Debrah
  • Nicholas Degarmo
  • Katie Lopez
  • Hana Marz
Explore research projects conducted by Embry-Riddle faculty to advance aviation and aerospace safety. 

1-9 of 9 results

  • Retrospective Detection of Valve Sticking Events in Aircraft Engines Using Historical Sensor Data

    PI Parham Ahmady Phoulady

    This project seeks to advance aviation safety by identifying historical valve sticking incidents within piston aircraft engines through the analysis of sensor data, thereby contributing to enhanced maintenance practices and operational reliability. 

    Valve sticking — resulting from factors such as deposits, corrosion, or mechanical wear—may compromise engine functionality or precipitate abrupt failures, frequently eluding conventional inspection protocols. Such undetected anomalies pose risks to safety, elevate operational expenditures, and disrupt flight continuity. To address this challenge, the researchers will examine an extensive dataset comprising more than 3,000 hours of flight records from Embry-Riddle Aeronautical University’s Daytona Beach and Prescott campuses. From this analysis, the principal objective is to develop an accessible analytical tool capable of delineating the occurrence of valve sticking incidents within historical data, thereby furnishing maintenance personnel with actionable insights into engine performance deficiencies. This tool will refine maintenance protocols, minimize superfluous interventions, and bolster aircraft safety while optimizing operational uptime.

    Categories: Faculty-Staff

  • Open-Source Validation and Verification Framework for AI-Controlled Aerial Vehicles

    PI M. Ilhan Akbas

    The goal of this project is to develop a simulation framework to streamline the testing and validation of AI-controlled aerial vehicles. The Artificial Intelligence (AI) design and verification flow consists of the digital environment creation process, an open-source AI-controlled autopilot, access to multiple open-source simulators, symbolic test generation engine, example test scenarios, and native design-for-experiment layer for each of the major subsystem of an AI-controlled aerial vehicle.

    Findings: The proof-of-concept demonstrated the viability of the system, with the low-fidelity simulation successfully flagging key scenarios for further testing, and the high-fidelity simulation providing accurate and realistic results for the flagged scenarios. By streamlining the testing process and focusing computational resources where they are most needed, this framework offers a robust solution for improving UAV safety and reliability in increasingly complex operational environments.

    Categories: Faculty-Staff

  • 3D Printing of Continuous Carbon Fiber Composites with Programmable Thermal Behaviors: A Proactive Safety Design for Advanced Thermal Management

    PI Yizhou Jiang

    CO-I Leitao Chen

    CO-I Yanbing Chen

    ​This study aimed to fabricate composite materials, i.e. continuous carbon fibers reinforced thermoset composites (CCFRTC), in a way that makes heat transfer predictable, enabling effective control measures. The ability to control thermal transfer through 3D-printing can lead to significant improvements in preventing thermal-related accidents.

    Findings: Final report submitted 9/24. This study demonstrated the adaptability and precision of the team’s 3D printing method but also underscored its potential in advancing the field of thermosetting composite material manufacturing, paving the way for innovative applications, including fire suppression systems.

    Categories: Faculty-Staff

  • Developing Aviation ASR and NLP Datasets and Tools

    PI Jianhua Liu

    CO-I Andrew Schneider

    The goal is to create an ATC ASR dataset for open access. We have obtained 300 hours of audio data and processed 30 hours using the bootstrap approach: Using Whisper to provide the initial transcripts, Correcting the transcripts by hired transcriber team, reviewing the corrected transcripts.



    Categories: Faculty-Staff

  • Robust Automatic Speech Recognition for Aviation Applications

    PI Jianhua Liu

    CO-I Andrew Schneider

    The goal of this project is to develop speech recognition models that can be used in aviation contexts. 




    Categories: Faculty-Staff

  • Deep-Learning-Based Unobtrusive Estimation of Pilot Adverse Interactions and Loss of Energy State Awareness

    PI Hever Moncayo

    ​This project aimed at gaining more insight into the mechanisms of pilot SD and LESA occurrence, capturing their dynamic fingerprint, and developing on-board intelligent schemes capable of predicting and detecting these dangerous phenomena associated to pilot behaviors. 

    Findings: Final report submitted 9/24. Each of the mathematical models showed good capabilities of estimating each of the pilot parameters and represent a promising tool towards the characterization of pilot behavior using learning components. Continuation can be pursued by generalizing or extending the proposed results to other aircraft-pilot dynamics, possibly eVTOLs for AAM.

    Student and Curriculum impact. The simulation and testing tools will be integrated as part of the experiential learning of the course AE623 Guidance, Navigation and Control that will be taught by the PI next Fall 2025. The proposed technique also allowed a master student in Aerospace Engineering to complement and enhance her thesis outcomes.​

    Categories: Faculty-Staff

  • Big Data Analytics for Injury Data

    PI Dothang Truong

    This project leverages big data analytics tools for the exploration and transformation of injury data for a major Part 121 carrier with the goal of predictive modeling. This project offers graduate students an opportunity to work with a substantial airline dataset under the supervision of a faculty member. The outcomes have the potential to lead to more extensive future projects in the realm of big data analytics. (This project is under strict NDA).


    Categories: Faculty-Staff

  • Integrated Communication and Environmental Sensing for Safety-Critical Autonomous Systems

    PI Thomas Yang

    PI Siyao Li

    Current communication networks with transmitter/receiver nodes can provide large-scale area coverage and robust interconnection between nodes. This allows for the seamless integration of sensing functions into the existing communication framework, paving the way for Integrated Communication and Sensing (ICAS). Unlike previous generations that treated communication and sensing separately, ICAS eliminates the need for additional hardware, extra transmit power, or dedicated frequency bands, by enabling communication signals to support data transmission and environmental sensing simultaneously. This convergence makes ICAS a key feature of six-generation (6G) communication and enables advanced applications, including Unmanned Aerial Vehicle (UAV) missions, autonomous driving, surveillance, and smart cities, to be powered by a single transmitted signal.

    This project aims to develop a novel ICAS framework tailored specifically for autonomous systems operating in safety-critical environments. The primary focus is enabling environment sensing by systematically analyzing the received information-carrying communication signals, through line-of-sight and/or reflected and scattered paths.



    Categories: Faculty-Staff

  • Fabrication of Copper Lithium-ion Battery Case with Integrated Cooling Channels Using Binder Jetting Additive Manufacturing

    PI Yue Zhou

    CO-I Wenhao Zhang

    CO-I Heer Patel

    CO-I Henil Patel

    CO-I Sirish Namilae

    This project leveraged binder jetting processes to directly fabricate metallic battery cases integrated with various cooling channels, paving the way for the additive fabrication of metallic thermal management devices applied in the aerospace field.



    Findings: Developed heat transfer model for the geometrical design of cooling channels, created files for experimental design and optimized printing & sintering settings, created scale-down prototypes for battery cases with integrated cooling channels.

    Categories: Faculty-Staff

1-9 of 9 results