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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


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


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


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



  • 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.


Bhaskar Saha


Research Scientist
Prognostics Center of Excellence
Intelligent Systems Division

Ames Research Center
Mail Stop 269-3
Moffett Field, CA 94035

Phone: 650-604-4379
Fax: 650-604-7563

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