Graph based signal-behavior-structure mapping for state maintenance of equipment

  • Wei Zhang (张 伟)
  • Yuemin Hou (侯悦民)


Equipment has dual nature: physical objects existing in nature, and artificial objects designed by human. The decision on the configuration and structural parameters of equipment is made by engineers based on technical-physical effects which control the behavioral parameters of the equipment. Sensors are mounted on the equipment to monitor the equipment state. Current methods for state monitoring and diagnosis mostly use mathematics and artificial intelligence technology to construct evaluation methods. This paper presents an integrated design and state maintenance method, in which graph and dual graph are used for recording design data and sensor arrangement and for mapping method from signals to substructures and connection pairs. An example of state maintenance of hydro power generating equipment is illustrated.


state maintenance technical-physical effect signal-behavior-structure mapping graph 

CLC number

X 913.4 


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  1. [1]
    SHU Y D, ZHAO J S. A simplified Markov-based approach for safety integrity level verification [J]. Journal of Loss Prevention in the Process Industries, 2014, 29: 262–266.CrossRefGoogle Scholar
  2. [2]
    KHAKZAD N, KHAN F, AMYOTTE P. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches [J]. Reliability Engineering and System Safety, 2011, 96: 925–932.CrossRefGoogle Scholar
  3. [3]
    PODOFILLINI L, DANG V N, SCHERRER P. A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction [J]. Reliability Engineering and System Safety, 2013, 117: 52–64.CrossRefGoogle Scholar
  4. [4]
    LIU H H, HAN M H. A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings [J]. Mechanism and Machine Theory, 2014, 75: 67–78.CrossRefGoogle Scholar
  5. [5]
    BREGONA A, DAIGLEB M, ROYCHOUDHURYC L, et al. An event-based distributed diagnosis framework using structural model decomposition [J]. Artificial Intelligence, 2014, 210: 1–35.CrossRefGoogle Scholar
  6. [6]
    DENG X G, TIAN X M, CHEN S. Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 127: 195–209.CrossRefGoogle Scholar
  7. [7]
    KAZARAS K, KONTOGIANNIS T, KIRYTOPOULOS K. Proactive assessment of breaches of safety constraints and causal organizational breakdowns in complex systems: A joint STAMP-VSM framework for safety assessment [J]. Safety Science, 2014, 62: 233–247.CrossRefGoogle Scholar
  8. [8]
    SIMEU-ABAZI Z, MASCOLO M D, KNOTEK M. Fault diagnosis for discrete event systems: Modelling and verification [J]. Reliability Engineering and System Safety, 2010, 95: 369–378.CrossRefGoogle Scholar
  9. [9]
    POPPER K. Three worlds: The tanner lecture on human values [R]. Ann Arbor: The University of Michigan, 1978.Google Scholar
  10. [10]
    ZHANG W, HOU Y M. Systematic safety analysis method for power generating equipment [J]. Journal of Shanghai Jiao Tong University (Science), 2015, 20(4): 508–512.MathSciNetCrossRefGoogle Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringTsinghua UniversityBeijingChina
  2. 2.School of Electromechanical EngineeringBeijing Information Science and Technology UniversityBeijingChina

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