Journal of Combinatorial Optimization

, Volume 37, Issue 1, pp 183–195 | Cite as

Prognostics and health management of life-supporting medical instruments

  • Cheng He
  • Yang Wu
  • Tong ChenEmail author


In order to deal with the maintenance problems of life-supporting medical instruments, and to improve their utilization, a prognostics and health management (PHM) system is designed. The implementation framework of PHM system is proposed. A experiment platform for critical components of life-supporting medical instruments is built. A fault is injected into the component. The model for critical components of medical instruments is established based on Lagrange method model. Using the reduced particle group to represent the state of the probability density function, the probability of failure in real-time can be calculated by particle filter algorithm. The simulation results match the experimental data. It diagnoses the faults and predicts the remaining useful life. Then appropriate maintenance advice can be given.


Prognostics and health management Life-supporting Bearing 



Funding was provided by High-speed Automatic Screw-tightening Workstation (Grant No. 14cxy38).


  1. Arkadan AA, Kielgas BW (1994) Switched reluctance motor drive systems dynamic performance prediction under internal and external fault conditions. IEEE Trans Energy Convers 9(1):45–52CrossRefGoogle Scholar
  2. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50:174–188CrossRefGoogle Scholar
  3. Bertozzi T, Le Ruyet D, Rigal G, Thien HV (2003) On particle filtering for digital communications. In: Proceedings of the IEEE Workshop SPAWC, Rome, ItalyGoogle Scholar
  4. Chen R, Liu JS (2000) Mixture Kalman filters. J R Stat Soc 62:493–508MathSciNetCrossRefzbMATHGoogle Scholar
  5. De Freitas N (2002) Rao-Blackwellised particle filtering for fault diagnosis. In: IEEE aerospace conference proceedings, vol 4, pp 1767–1772Google Scholar
  6. Devabhaktuni VK, Yagoub MCE, Zhang QJ (2001) A robust algorithm for automaticdevelopment of neural network models for microwave applications. IEEE Trans Microw Theory Technol 49(12):2282–2291CrossRefGoogle Scholar
  7. Doucet A (1998) On sequential Monte Carlo methods for Bayesian filtering. Technical Report, Engineering Department, University of Cambridge, UKGoogle Scholar
  8. Gadzheva ED, Raykovska LH (1994) Nullator-norator approach for diagnosis and fault prediction in analog circuits. IEEE Int Symp Circuits Syst 1:53–56CrossRefGoogle Scholar
  9. Orchard M (2007) A particle filtering-based framework for on-line fault diagnosis and failure prognosis. Ph.D. Thesis, Electrical and Computer Engineering, Georgia Institute of Technology, AtlantaGoogle Scholar
  10. Orchard M, Vachtsevanos G (2009) A particle filtering approach for on-line fault diagnosis and failure prognostics. Trans Inst Meas Control 31(3–4):221–246CrossRefGoogle Scholar
  11. Vachtsevanos G, Lewis F, Roemer MJ, Hess A, Wu B (2006) Intelligent fault diagnosis and prognostics for engineering system. Wiley, HobokenCrossRefGoogle Scholar
  12. Zhong L, Tang G (2015) Preface, special issue on combinatorial optimization in health care. J Comb Optim 30(4):839–840MathSciNetCrossRefGoogle Scholar
  13. Zhong L, Luo S, Wu L, Xu L, Yang H, Tang G (2014) A two-stage approach for surgery scheduling. J Comb Optim 27(3):545–556MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringShanghai Polytechnic UniversityShanghaiChina
  2. 2.Logistics Support DepartmentShanghai General HospitalShanghaiChina

Personalised recommendations