A Multi-Fault Modeling Approach for Fault Diagnosis and Failure Prognosis of Engineering Systems



Published Mar 26, 2021
Bin Zhang Chris Sconyers Romano Patrick George Vachtsevanos


Accurate and reliable fault diagnosis and prognosis of safety or mission critical components/ subsystems in complex engineering systems present major challenges to the Condition-Based Maintenance (CBM) or Prognostic and Health Management (PHM) designer. A crucial step in the development of CBM/PHM strategies relates to the designer’s ability to understand and model the incipient failure or fault modes and mechanisms. A single fault growth model might not be often capable to capture a sequence of fault behaviors. Consider, for example, a rolling element bearing as a critical component of rotating machinery. The bearing may begin to corrode under certain operating conditions and, in parallel or sequentially, may be spalling and eventually, cracking. For accurate model-based fault diagnosis and failure prognosis, therefore, it is essential that fault progression models be developed to represent these evolving behaviors. This paper introduces an approach to multi-fault modeling with an application to a rolling element bearing of a helicopter’s oil cooler. A simple and cost-effective on-line parameter adaptation solution is introduced to improve the performance of modeling. Finally, a series of experiments for different fault modes are presented to verify the proposed solution.

How to Cite

Zhang, B., Sconyers, C., Patrick, R., & Vachtsevanos, G. (2021). A Multi-Fault Modeling Approach for Fault Diagnosis and Failure Prognosis of Engineering Systems. Annual Conference of the PHM Society, 1(1). Retrieved from http://www.papers.phmsociety.org/index.php/phmconf/article/view/1465
Abstract 456 | PDF Downloads 230



diagnosis, fault diagnosis, model based diagnostics, model based prognostics, prediction, prognostics

[1] P. McFadden and J. Smith, “Model for the vibration produced by a single point defect in a rolling element bearing” , Journal of Sound and Vibration, vol. 96, pp. 69-82, 1984.
[2] I. Howard, “A review of rolling element bearing vibration: detection, diagnosis and prognosis”, DSTO-RR-0013, Airframes and Engines Division, 1994.
[3] B. Li, M.-Y. Chow, Y. Tipsuwan and J. Hung, “Neural networks based motor rolling bearing fault diagnosis”, IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp 1060- 1069, Oct. 2000
[4] G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess and B. Wu, “Intelligent fault diagnosis and prognosis for engineering systems”, Wiley, 2006
[5] M. Orchard and G. Vachtsevanos, “A particle filtering based framework for real-time fault diagnosis and failure prognosis in a turbine engine”, 15th Mediterranean Conference on Control and Automation, Athens, Greece, July 2007.
[6] R. Patrick, A Model Based Framework for Fault Diagnosis and Prognosis of Dynamical Systems with an Application to Helicopter Transmissions, Ph.D. Thesis, School of Electrical and Computer Engineering, Georgia Institute of Technology, July, 2007.
[7] Y. Li, S. Billington, C. Zhang, T. Kurfess, S. Danyluck, S. Liang, “Adaptive prognosis for rolling element bearing condition” Mechanical Systems and Signal Processing, 13(1), pp.103-113, 1999.
[8] M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, G. Vachtsevanos, Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics, International Conference on Prognostics and Health Management, Oct. 2008, Denver CO, USA.
[9] B. Randall, J. Antoni, S. Chobsaard, The Relationship Between Spectral Correlation and Envelope Analysis in the Diagnostics of Bearing Faults and other Cyclostationary Machine Signals, Mechanical System and Signal Processing, 15(5), 2001, pp 945- 962.
[10] P. Tse, Y. Peng, R. Yam, Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis - Their Effectiveness and Flexibilities, J. Vibration and Acoustics, 123(3), 2001, pp 303-310.
[11] D. Ho, B. Randall, Optimisation of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals, Mechanical System and Signal Processing, 14(5), 2000, pp 765- 788.
[12] P. Goode, M.-Y. Chow, Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part I-Methodology, IEEE Transactions on Industrial Electronics, 42(2), 1995, pp 131-138.
[13] P. Zarchan, H. Musoff, Fundamentals of Kalman Filtering A Practical Approach, Progress in astronautics and aeronautics, v. 208. Reston, Va: American Institute of Aeronautics and Astronautics, 2005
[14] S. Engel, B. Gilmartin, K. Bongort, A. Hess, Prognostics, the Real Issues Involved With Predicting Life Remaining”. Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 18-25, 2000.
[15] C.,Zhang,T.Kurfess,S.Danyluk,S.Liang,Dynamicmodeling of vibration signals for bearing condition monitoring, the 2nd International Workshop on Structural Health Monitoring, Stanford, 1999, 926-935.
[16] Y. Choi, C. Liu, Rolling contact fatigue life of finish hard machined surfaces Part I. Model develop, Wear, 261, 2006, 485-491.
[17] M. Davies, Y. Chou, C. Evans, on chip morphology, tool wear and cutting mechanics in finish hard turning, Ann. CIRP 45(1), 1996, 77-82.
[18] E. Ioannides and T. Harris, A new fatigue life model for rolling bearing, Trans. ASME, J. Tribology, 1985, 107, 367-278.
[19] J. Qiu, C. Zhang, B. Seth, and S. Liang, Damage mechanics approach for bearing lifetime prognostics, Mechanical Systems and Signal Processing, 16(5), 2002, 817-829.
[20] D. He and E. Bechhoefer, Bearing Prognostics using HUMS condition indictors, American Helicopter Society 64th Annual Forum, Montreal, Canada, 2008.
[21] M.N. Kotzalas, T.A. Harris, Fatigue failure progression in ball bearings, J. Tribology 123 (2001) 238–242.
[22] C.J.Li,H.Shin,Trackingbearingspallseveritythroughinverse modeling, Proceedings of the ASME International Mechanical Engineering Congress, Anaheim, CA, USA, 2004, pp. 1–6.
Technical Research Papers

Most read articles by the same author(s)

1 2 3 > >>