Highlights
Multi-modality safety monitoring, detection, and data analysis
- Develop a real-time in-flight safety monitoring framework to enhance pilots’ situational awareness and prevent fatal accidents
Detection of operational and mechanical upsets in scheduled commercial flights using data-driven approaches - Investigate propagation of aircraft upsets in air traffic system for proactive air traffic system health management
Predict post upset aircraft performance and trajectory to assess risk level and prevent domino effects in air traffic system
Real World Applications
- Real-Time System-Wide Safety Assurance for the Next Generation National Airspace System (NextGen)
- Recent technology advances in sensors, networking, data mining, prognostics, and other analytic techniques enable
proactive risk management - When applied to a combination of new and existing sets of NextGen data, further processing of this new metadata set
allows us to identify, predict, and mitigate hazards - Key challenge: how to integrate the massively complex sources of data that will drive the future air traffic management system
- Aviation community foresees a future when it can recognize safety risks as they develop in real time and then implement strategies to prevent those risks from occurring
- Further develop collaboration between human operators and autonomous systems to enable a more resilient aviation system
- Systematic training of future engineers and workforce pipeline for future aerospace industries and research
Links or Tools to be Shared with the Public
Papers
Below is a list of papers with links available to read.
Controller-Pilot Communication as an Index Of Human Performance in the National Airspace System
Authors: Mustafa Demir, Sarah Ligda, Nancy Cooke, Megan Seeds, Mariah Harris, Mary Niemczyk
Monitoring Human Performance in Real-Time for NAS Safety Prognostics
Authors: Sarah V. Ligda, Mariah J. Harris, Christopher S. Lieber, and Nancy J. Cooke
Understanding Controller-Pilot Interaction Dynamics in the Context of Air Traffic Control
Authors: Mustafa Demir, Nancy J. Cooke, Christopher S. Lieber, Sarah Ligda
Real-time Production Performance Analysis Using Machine Degradation Signals: a Two-Machine Case
Authors: Yunyi Kang, Hao Yan and Feng Ju
Performance Evaluation of Production Systems Using Real-Time Machine Degradation Signals
Authors: Yunyi Kang, Hao Yan, and Feng Ju
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Authors: Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang and Fugee Tsung
Frequency Domain Instantaneous Wavenumber Estimation for Damage Quantification in Layered Plate Structures
Authors: Olivier Mesnil, Hao Yan, Massimo Ruzzene, Kamran Paynabar and Jianjun Shi
Fast wavenumber measurement for accurate and automatic location and quantification of defect in composite
Authors: Olivier Mesnil, Hao Yan, Massimo Ruzzene, Kamran Paynabar and Jianjun Shi
Comments on: On Active Learning Methods for Manifold Data
Authors: Mostafa Reisi Gahrooei, Hao Yan and Kamran Paynabar
Image-Based Process Monitoring Using Low-Rank Tensor Decomposition
Authors: Hao Yan, Kamran Paynabar, and Jianjun Shi
Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition
Authors: Hao Yan, Kamran Paynabar & Jianjun Shi
Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal
Smooth Sparse Decomposition
Authors: Hao Yan, Kamran Paynabar & Jianjun Shi
Image-based Process Monitoring via Adversarial Autoencoder with Applications to Rolling Defect Detection
Authors: Hao Yan, Huai-Ming Yeh, and Nurettin Sergin
Physics-based Deep Spatio-temporal Metamodeling for Cardiac Electrical Conduction Simulation
Authors: Hao Yan, Xinyu Zhao, Zhiyong Hu, Dongping Du
Structured Point Cloud Data Analysis Via Regularized Tensor Regression for Process
Modeling and Optimization
Authors: Hao Yan, Kamran Paynabar & Massimo Pacella
Generalized Wavelet Shrinkage of Inline Raman Spectroscopy for Quality Monitoring of
Continuous Manufacturing of Carbon Nanotube Buckypaper
Authors: Xiaowei Yue, Kan Wang, Hao Yan, Jin Gyu Park, Zhiyong Liang, Chuck Zhang, Ben Wang, and Jianjun Shi
A Wavelet-Based Penalized Mixed-Effects Decomposition for Multichannel Profile Detection of In-Line Raman Spectroscopy
Authors: Xiaowei Yue, Hao Yan, Jin Gyu Park, Zhiyong Liang, and Jianjun Shi
Multiple profiles sensor-based monitoring and anomaly detection
Authors: Chen Zhang, Hao Yan, Seungho Lee & Jianjun Shi
Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis
Authors: Chen Zhang, Hao Yan, Seungho Lee & Jianjun Shi
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Authors: Chen Zhang, Hao Yan, Seungho Lee, and Jianjun Shi
A Recurrent Neural Network Approach for Aircraft Trajectory Prediction with Weather Features From Sherlock
Authors: Yutian Pang, Houpu Yao, Jueming Hu, Yongming Liu
Aircraft Trajectory Prediction using LSTM Neural Network with Embedded Convolutional Layer
Authors: Yutian Pang, Nan Xu, and Yongming Liu
Probabilistic Aircraft Trajectory Prediction Considering Weather Uncertainties Using Dropout As Bayesian Approximate Variational Inference
Authors: Yutian Pang and Yongming Liu
Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects
Authors: Yutian Pang and Yongming Liu
Probabilistic Aircraft Trajectory Prediction with Weather Uncertainties using Approximate Bayesian Variational Inference to Neural Networks
Authors: Yutian Pang, Yuhao Wang and Yongming Liu
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
Authors: Yutian Pang, Sheng Cheng, Jueming Hu and Yongming Liu
Citations
Below is a list of papers and conference papers without links.
Chen, J., Liu, Y., Cooke, N., & Tang, P. (2019). Real-time Facial Expression and Head Pose Analysis for Monitoring the Workloads of Air Traffic Controllers. In AIAA Aviation 2019 Forum(p. 3412).
Demir, M., Ligda, S., Cooke, N., Seeds, M., Harris, M, Niemczyk, M. (2019). Controller-Pilot Communication as an Index of Human Performance in the National Airspace System. 20th International Symposium on Aviation Psychology, May 2019, Dayton, OH.
Guoyi Li, Ashwin Rai, Hyunseong Lee, Aditi Chattopadhyay, “Operational anomaly detection in flight data using a multivariate gaussian mixture model,” PHM Society Conference, 10(1), 2018.
Guoyi Li, Hyunseong Lee, Ashwin Rai, Aditi Chattopadhyay, “Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model,” Transportation Research Part C: Emerging Technologies, 110, 20-39, 2020.
Hyunseong Lee, Hyung Jin Lim, Aditi Chattopadhyay, “Data-driven system health monitoring technique using autoencoder for the safety management of commercial aircraft,” Neural Computing and Applications (in press).
Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay, “Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft,” Advanced Engineering Informatics, 44, 101071, 2020.
Hyunseong Lee, Hyung Jin Lim, Paul Parker, Aditi Chattopadhyay, “Precursor detection of aircraft loss of control in-flight (LOC-I) and prediction of future trajectory,” AIAA Aviation Forum, 2879, 2020.
Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay, “Health Monitoring framework for aircraft engine system using deep neural network,” Prognostics and Health Management (PHM) Society Conference, 11(1), 2019.
Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay, “Propagation of trained flight performance and observed anomalies to air traffic models,” AIAA Aviation Forum, 2941, 2019.
Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay, “Anomaly detection of aircraft system using kernel-based learning algorithm,” AIAA Scitech Forum, 1224, 2019.
Ligda, S. V., Harris, M. J., Lieber, C. S., & Cooke, N. J. (2019). Monitoring Human Performance in Real-Time for NAS Safety Prognostics. American Institute of Aeronautics and Astronautics, June 2019, Dallas, TX.
Srinivasan, P., Nagarajan, V., & Mahadevan, S. (2019). Mining and classifying aviation accident reports. In AIAA Aviation 2019 Forum (p. 2938).
Sun, Z., Zhang, C., Tang, P., Wang, Y., & Liu, Y. (2019). Bayesian network modeling of airport runway incursion occurring processes for predictive accident control. In Advances in Informatics and Computing in Civil and Construction Engineering(pp. 669-676). Springer, Cham.
Tang, P., Wang, Y. (2020). Spatiotemporal Data-Driven Simulation and Clustering of Ground Operations of Aircraft for Comprehending Airport Jams and Collisions. In ASCE Construction Research Congress (In Print)
Tang, P., Wang, Y., Sun, Z., & Liu, Y. (2019, December). Data-driven spatiotemporal simulation of ground movements of aircraft for preventive airport safety. In 2019 Winter Simulation Conference (WSC) (pp. 2992-3000). IEEE.
Wang, W., & Ying, L. (2018). Dynamic data communications for real-time information fusion. In M. Orchard, & A. Bregon (Eds.), PHM 2018 – 10th Annual Conference of the Prognostics and Health Management Society (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM). Prognostics and Health Management Society.
W. Wang and L. Ying, “Learning Parallel Markov Chains over Unreliable Wireless Channels,” 2020 54th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2020, pp. 1-6, doi: 10.1109/CISS48834.2020.1570614323.
Wang, Y., Zhe, S., Liu, Y., & Tang, P. (2019). Predicting Collisions between Aircraft through Spatiotemporal Data-Driven Simulation of Airport Ground Operations. In AIAA Aviation 2019 Forum(p. 3414).
Zhang, X., Kong, Y., Subramanian, A., & Mahadevan, S. (2018). Data-driven Modeling for Aviation Safety Diagnosis and Prognosis. In Annual Conference of the PHM Society (Vol. 10, No. 1).
Zhang, X., & Mahadevan, S. (2019). Aviation Safety Assessment Using Historical Flight Trajectory Data. In AIAA Aviation 2019 Forum (p. 3415).
Zhang X., and Mahadevan, S., (2021). Bayesian network modeling of accident investigation reports for aviation safety assessment. Reliability Engineering & System Safety, 209, 107371.
Zhang, X., & Mahadevan, S. (2020). Bayesian neural networks for flight trajectory prediction and safety assessment. Decision Support Systems, 131, 113246.
Zhang, X., & Mahadevan, S. (2019). Ensemble machine learning models for aviation incident risk prediction. Decision Support Systems, 116, 48-63.
Zhang, X., Srinivasan, P., and Mahadevan, S. (2021). Sequential deep learning from NTSB reports for aviation safety prognosis. Safety Science, 142, 105390.