Graph Based Signal-Behavior-Structure Mapping for State Maintenance of Equipment

doi:10.1007/s12204-017-1842-4

Abstract

Abstract: 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.

Key words: state maintenance| technical-physical effect| signal-behavior-structure mapping| graph

摘要： 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.

关键词: state maintenance| technical-physical effect| signal-behavior-structure mapping| graph

CLC Number:

X 913.4

ZHANG Wei1 (张伟), HOU Yuemin1,2* (侯悦民). Graph Based Signal-Behavior-Structure Mapping for State Maintenance of Equipment[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(3): 349-354.

References 10

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

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