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Resilient Systems Lab Active Project

Diagnosis and Prognosis

In the framework of fault diagnosis and prognosis, the model of diagnosis and prognosis are developed from physical understanding of fault mechanisms or from historical data. For online application, the sensor data are collected and processed for feature (or condition indicator) extraction. The feature, which is the observation of the system, should have a good mapping between its values with actual fault dimensions.  From a Bayesian state estimation point of view, this can be accomplished by Kalman filtering, particle filtering, etc. Since most fault growth is nonlinear, a particle filtering is used, which has the advantages of offering a convenient compromise between data-driven and model-based techniques, but also the means to discuss its performance in terms of statistical indices.

The outcomes of diagnosis (current fault state estimation) and prognosis (remaining useful life) can be further used for system reconfiguration and mission planning to accommodate the faults in the system.

Sponsor

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Co-primary Investigator

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Project Timeline

Project Inception Date - Current

Related Publications

  • B. Zhang, M. Orchard, B. Saha, A. Saxena, Y.- J. Lee, G. Vachtsevanos, A Verification Framework with Application to a Propulsion System, Expert Systems with Applications, in print.
  • M. Orchard, P. Hevia-Koch, B. Zhang, L. Tang, Risk Measures for Particle-filtering-based State-of-Charge Prognosis in Lithium-Ion Batteries, IEEE Transactions on Industrial Electronics, Vol. 60, No. 11, Nov. 2013, p5260-5269.
  • J. Jiang, B. Zhang, A Rolling-Element Bearing Vibration Modeling with Applications to Health Monitoring, Journal of Vibration and Control, Vol.18, No.12, Oct. 2012, p1768-1776.
  • C. Chen, D. Brown, C. Sconyers, B. Zhang, G. Vachtsevanos, M. Orchard, An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems, Expert Systems with Applications, Vol. 39, 2012, p9031-9040.
  • C. Chen, B. Zhang, G. Vachtsevanos, M. Orchard, Machine Condition Prediction based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering, IEEE Transactions on Industrial Electronics, Vol. 58, No.9, Sept. 2011, p4353-4364.
  • C. Chen, B. Zhang, G. Vachtsevanos, Prediction of Machine Health Condition using Neuro-Fuzzy and Bayesian Algorithms, IEEE Transactions on Instrumentation & Measurement, Vol. 61, No.2, Feb. 2012, p297-306.
  • B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. Orchard, G. Vachtsevanos, A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection, IEEE Transactions on Industrial Electronics, Vol. 58, No. 5, May 2011, p 2011-2018.
  • B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, A Novel Blind Deconvolution De-Noise Scheme in Failure Prognosis, Transactions of the Institute of Measurement & Control, Vol. 32, No. 1, 2010, p 3-30. Also appears at: Applications of Intelligent Control to Engineering Systems, Ed. K. Valavanis, Springer, 2009.
  • B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, M. Orchard, A. Saxena, Application of Blind Deconvolution De-Noising in Failure Prognosis, IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 2, 2009, p 303-310.
  • B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, Blind Deconvolution De-noising for Helicopter Vibration signals, IEEE/ASME Transactions on Mechatronics, Vol. 13, No. 5, 2008, p 558-565.
  • B. Zhang, L. Tang, J. DeCastro, G. Kai, A Verification and Validation Methodology for Prognostic Algorithms, AUTOTESTCON, Sept. 2010, Orlando FL.
  • C. Chen, B. Zhang, G. Vachtsevanos, A .NET Framework for an Integrated Fault Diagnosis and Failure Prognosis Architecture, AUTOTESTCON, Sept. 2010, Orlando FL.
  • B. Zhang, C. Sconyers, M. Orchard, R. Patrick, G. Vachtsevanos, Fault Progression Modeling: An Application to Bearing Diagnosis and Prognosis, 2010 American Control Conference, June 2010, Baltimore, MD.
  • B. Zhang, C. Sconyers, R. Patrick, G. Vachtsevanos, A Multi-Fault Modeling Approach for Fault Diagnosis and Failure Prognosis of Engineering Systems, International Conference on Prognostics & Health Management, Oct. 2009, San Diego, CA.
  • M. Smith, B. Zhang, R. Patrick, C. Byington, G. Vachtsevanos, R. D. Rosario, D. Wade, D. Suggs, Combination of Fusion and Preprocessing Techniques to Enhance Air Vehicle HUMS, 13th Australian International Aerospace Congress (AIAC) Congress, March 2009, Melbourne, Australia
  • R. Patrick, M. Smith, B. Zhang, C. Byington, G. Vachtsevanos, R. D. Rosario, Diagnostic Enhancements for Air Vehicle HUMS to Increase Prognostic System Effectiveness, IEEE Aerospace Conference, March, 2009, Big Sky, Montana.
  • B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. Orchard, G. Vachtsevanos, Anomaly Detection: A Robust Approach to Detection of Unanticipated Faults, International Conference on Prognostics & Health Management, Oct. 2008, Denver, CO.
  • D. Brown, G. Georgoulas, B. Zhang, D. Edwards, G. Vachtsevanos, Real-Time Fault Detection and Accommodation for Resolver Position Sensors, International Conference on Prognostics and Health Management, Oct. 2008, Denver, CO.
  • M. Orchard, D. Brown, B. Zhang, G. Georgoulas, G. Vachtsevanos, Anomaly Detection: A Particle Filtering Framework with an Application to Aircraft Systems, Integrated Systems Health Management Conference, Aug. 2008, Covington KY.
  • B. Zhang, G. Georgoulas, M. Orchard, A. Saxena, D. Brown, G. Vachtsevanos, S. Liang, Rolling Element Bearing Feature Extraction and Anomaly Detection Based on Vibration Monitoring, 2008 Mediterranean Conference on Control and Automation, June 2008, Ajaccio, France, pp1792-1797
  • R. Patrick, M. Orchard, B. Zhang, G. Kacprzynski, M. Koelemay, G. Vachtsevanos, An Integrated Approach to Helicopter Planetary Gear Fault Diagnosis and Failure Prognosis, AUTOTESTCON, Sept. 2007, Baltimore MD.
  • Saxena, M. Orchard, B. Zhang, G. Vachtsevanos, L. Tang, Y. Lee, Y. Wardi, Automated Contingency Management for Propulsion Systems, 2007 European Control Conference, July 2007, Kos, Greece, p3515-3522.
  • B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, Blind Deconvolution De-noising for Helicopter Vibration Data, the 2007 American Control Conference, July 2007, New York, NY, p1864-1869.