Diagnostics & Prognostics Group
The NASA Diagnostics and Prognostics group is a team of researchers and engineers performing research in fault detection, diagnostics, prognostics, and prognostic decision making relevant to NASA Missions.
- Air Traffic System: Safety quantification and prediction in complex multi-agent systems; in-flight risk assessment for Unmanned Aerial Vehicles (UAVs) and Urban Air Mobility (UAM); trajectory monitoring and prediction.
- Aircraft: Diagnostics & prognostics for aircraft; battery health-monitoring and prediction; powertrain diagnostics & prognostics; Inertial Measurement Unit (IMU) degradation.
- Spacecraft: Diagnostics & prognostics for spacecraft systems; intelligent spacecraft power management; planetary surface exploration a using multi-agent autonomous framework with a self-monitoring capability, extreme access and uncertain terrain exploration; rover health diagnostics’ safety quantification and prediction in complex multi-agent systems.
- Spacecraft Ground Support: Diagnostics & prognostics approaches for spacecraft ground support systems such as cryogenic refueling systems.
- Physics-Based Diagnostics & Prognostics Algorithms: Development of new algorithms and approaches for physics-based diagnostics & prognostics.
- Data-Driven Diagnostics & Prognostics Algorithms: Development of new algorithms and approaches for data-driven diagnostics & prognostics.
- Hybrid Prognostics Approaches: Development of new algorithms and approaches for hybrid physics-based and data-driven prognostics, including physics-informed machine learning.
- Uncertainty Representation and Management: Identification of sources of uncertainty, quantification and propagation of uncertainty, and representation of uncertainty in prediction.
- Resource-Constrained Prognostics: Approaches for performing prognostics in resource-constrained environments, such as small spacecraft.
- Testbed Development and Automated Testing for Prognostics: Construction of testbeds to collect data on the degradation of systems and validate prognostics algorithms with fault-insertion.
- Human-Machine Interaction: Presentation of probabilistic diagnostics & prognostics information to human stakeholders, utilization in decision-making.
- Nondestructive Evaluation / Structural Health Monitoring: Fault identification and characterization, in-flight Systems Health Management (SHM) of Unmanned Aerial Vehicle (UAV) mechanical parts, damage progression and fatigue life analysis in composite material.
- Software Architectures for Prognostics: Service-Oriented Architectures (SOAs) for diagnostics & prognostics, integration of health services into the wider digital fabric, use in autonomous decision-making.
- Health-Informed Decision-Making Under Uncertainty: Automated decision-making strategies utilizing health information (both diagnostics & prognostics) with uncertainty.
- Verification & Validation (V&V) of Prognostics and Health Management Systems: Approaches for V&V of diagnostics and health management systems.
- Design for Resilience: Design methodologies for injecting resiliency and resilience quantification.
- Distributed Diagnostics: Bayesian network approaches.
- The Prognostics Center of Excellence (PCoE) is a NASA-wide organization of diagnostics, prognostics, and health management researchers and practitioners. The Diagnostics & Prognostics group are important contributors to this organization.
- The Prognostics Health Management (PHM) Society is a professional organization dedicated to the advancement of PHM as an engineering discipline.
- Prognostics Center of Excellence (PCoE) Data Repository (updated November 2023)
- PCoE Software List
We are open to collaborations, hosting students, and answering questions. If you would like to contact Chris Teubert: firstname.lastname@example.org .
– Project management, software architectures for prognostics, algorithms for resource-constrained prognostics
Deputy Group Lead
– Aircraft, entry vehicle, and unmanned vehicle Guidance, Navigaion, and Control (GNC); controls design; state/parameter estimation; project management
Systems Health, Analytics, Resilience, and Physics-modeling (SHARP) Lab Operations Manager
– Automated testing, prototype development, experiment design, embedded systems, hardware-accelerated computing
- Edward Balaban: Decision-making under uncertainty, health-aware decision-making, and autonomous systems
- Portia Banerjee (KBR): Physics-based system modeling, fault diagnostics and prognostics, structural health monitoring, nondestructive evaluation, statistical signal processing, uncertainty management, decision-making
- Matteo Corbetta (KBR): Uncertainty quantification, physics-informed machine learning, diagnostics and prognostics algorithm and model development’
- Rajeev Ghimire
- Elizabeth Hale
- Katelyn Jarvis: Resource-constrained prognostics, physics-based modeling, and human-related prognostics
- Chetan Kulkarni (KBR): Model-based and hybrid approaches for fault diagnostics and prognostics, physics-based modeling, resilience approaches to prognostics and decision-making, hardware-in-the-loop testing
- Molly O’Connor
- Adam Sweet: Model-based diagnosis, demonstrating advanced health management in flight experiments
- Jason Watkins (KBR): Software architectures for prognostics, software development and engineering
Delft University of Technology (TUDelft) – Dr. Marcia Baptista
German Aerospace Center (DLR) Institute of Maintenance Repair and Overhaul
Idaho National Lab
Iowa State University
Jet Propulsion Laboratory (JPL)/Caltech
Northrop Grumman Corporation (NGC)
Penn State Applied Research Laboratory (ARL)
Qualtech Systems, Inc.
Research Institutes of Sweden (RISE) – Dr. Madhav Mishra and team
Scientific Monitoring, Inc.
University of California at Los Angeles (UCLA)
University of Connecticut
United States Air Force (USAF)
University of Maryland
Manuel Arias Chao
Renato Giorgiani do Nascimento
Juan Chiachio Ruano
Manuel Chiachio Ruano
Hector Sanchez Sardi
Gina Sierra Paez
Jonny Da Silva