About Me

My research at the NASA Ames Prognostics Center of Excellence is focused on applying state-of-the-art classification, regression and state estimation techniques for predicting remaining useful life of systems and their components.

I developed an integrated Bayesian framework to estimate present damage and damage growth rates as a function of operational parameters to determine remaining-useful-life probability densities. This prediction framework has now been extended to function over distributed intelligent sensor systems. I built a hardware-in-the-loop testbed to benchmark prognostic algorithms using run-to-failure tests on Li-ion batteries.

I have also helped formulate a set of metrics tailored for evaluating the performance of prognostic algorithms in order to standardize research in prognostics and advance the state-of-the-art.

Academic Background

Ph.D.

Electrical Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 2008.

M.S.

Electrical Engineering (Minor: Mechanical Engineering), Georgia Institute of Technology, Atlanta, GA, USA, 2007.

B.Tech.

Electrical Engineering (Specialization: Instrumentation Engineering), Indian Institute of Technology, Kharagpur, W.B., India, 2001.

Research Interests

• Systems and Controls
• System Health Management
• Fault Detection, Diagnostics and Prognostics
• Data-driven and Model-based Prognostics
• Optimal Control
• System Reconfiguration
• Autonomous Contingency Management
• Standards and methodologies
• Validation and Verification for Prognostic Health Management

Publications

Journals:

• B. Saha and G. Vachtsevanos, “A Model-Based Reasoning Approach to System Fault Diagnosis”, WSEAS Trans. on Systems, issue 8, vol. 5, pp. 1997–2004, August 2006.
• B. Saha, K. Goebel, S. Poll, and J. Christophersen, “Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework”, IEEE Transactions on Instrumentation and Measurement, vol.58, no.2, pp.291-296, Feb. 2009.
• B. Saha, K. Goebel, and J. Christophersen, “Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries”, Transactions of the Institute of Measurement & Control, vol. 31, no. 3-4, pp. 293-308, June 2009.
• D. Brown, G. Georgoulas, B. Bole, H. Pei, M. Orchard, L. Tang, B. Saha, A. Saxena, K. Goebel, and G. Vachtsevanos, “Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators”, submitted to IEEE Transactions on Control Systems Technology.

Book Chapters:

• M. E. Orchard, G. Kacprzynski, K. Goebel, B. Saha, and G. Vachtsevanos, “Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics”, in K. P. Valavanis (Ed.), Applications of Intelligent Control to Engineering Systems, pp. 23-35, Springer Netherlands, 2009.

Magazine Articles:

• K. Goebel, B. Saha, A. Saxena, J. R. Celaya, and J. Christophersen, “Prognostics in Battery Health Management”, Instrumentation & Measurement Magazine, IEEE , vol.11, no.4, pp.33-40, August 2008.

Conferences:

• G. Drozeski, B. Saha and G. Vachtsevanos, “A Fault-Tolerant Architecture for Unmanned Rotorcraft”, Proc. of the AHS International Specialists’ Meeting on Unmanned Rotorcraft, January 2005.
• G. Drozeski, B. Saha and G. Vachtsevanos, “A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles”, Aerospace Conference, 2005 IEEE, pp. 2955–2963, March 2005.
• B. Saha and G. Vachtsevanos, “A Novel Model-Based Reasoning Approach to System-Level Diagnostics of a Helicopter Intermediate Gearbox”, Proc. of the 60th Meeting of the Society for Machinery Failure Prevention Technology (MFPT 2006), pp. 281–290 , April 2006.
• B. Saha, K. Goebel, S. Poll, and J. Christophersen, “An Integrated Approach to Battery Health Monitoring using Bayesian Regression and State Estimation”, Proc. of IEEE AUTOTESTCON 2007, pp. 646–653, September 2007.
• B. Saha, K. Goebel, S. Poll, and J. Christophersen, “A Bayesian Framework for Remaining Useful Life Estimation”, 2007 Fall AAAI Symposium: AI for Prognostics, November 2007.
• L. Tang, G. Kacprzynski, K. Goebel, J. Reimann, M. Orchard, A. Saxena, and B. Saha, “Prognostics in the Control Loop”, 2007 Fall AAAI Symposium: AI for Prognostics, September 2007.
• B. Saha and K. Goebel, “Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques”, Aerospace Conference, 2008 IEEE, pp.1-8, Big Sky, MT, March 2008.
• K. Goebel, B. Saha, and A. Saxena, “A Comparison of Three Data-Driven Algorithms for Prognostics”, Proc. of the 62nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT 2008), pp.119-131, April 2008.
• A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha, S Saha, and M. Schwabacher, “Metrics for Evaluating Performance of Prognostic Techniques”, Intl. Conf. on Prognostics and Health Management (PHM08), Denver, CO, October 2008.
• S. Saha, B. Saha, and K. Goebel, “Distributed Prognostics Using Wireless Embedded Devices”, Intl. Conf. on Prognostics and Health Management (PHM08), Denver, CO, October 2008.
• L. Tang, G. J. Kacprzynski, K. Goebel, A. Saxena, B. Saha, and G. Vachtsevanos, “Prognostics-enhanced Automated Contingency Management for Advanced Autonomous Systems”, Intl. Conf. on Prognostics and Health Management (PHM08), Denver, CO, October 2008.
• B. Saha, J. Celaya, K. Goebel, and P. Wysocki, “Towards Prognostics for Electronics Components”, Aerospace Conference, 2009 IEEE, Big Sky, MT, March 2009.
• A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel, “Evaluating Algorithm Performance Metrics Tailored for Prognostics”, Aerospace Conference, 2009 IEEE, Big Sky, MT, March 2009.
• S. Saha, B. Saha, and K. Goebel, “Communication Optimizations for a Wireless Distributed Prognostic Framework”, Aerospace Conference, 2009 IEEE, Big Sky, MT, March 2009.
• B. Saha, S. Saha, and K. Goebel, “A Distributed Prognostic Health Management Architecture”, Proc. of the 2009 Conference of the Society for Machinery Failure Prevention Technology (MFPT 2009), April 2009.