The PHM Research of Vessels Equipment Based on Condition

Chun Jian Wang; Yong Zhi Liu; Hong Jun Fan

doi:10.4028/www.scientific.net/AMR.605-607.750

Paper Titles

Fault Diagnosis of Valve Train of Internal Combustion Engine Based on the Artificial Neural Network and Support Vector Machine
p.729

A Note on the Early Failure Period for CNC Machining Tools
p.734

High Order Spectrum Characteristics of the Elevator Fault
p.739

Field Test of Wellbore Anti-Collision Monitoring System
p.744

The PHM Research of Vessels Equipment Based on Condition
p.750

Large Pendulum Shaft Failure Analysis and Countermeasure Research
p.756

Electromagnetic Nondestructive Testing in Cracked Defects of Oil-Gas Casing Based on Ant Colony Neural Network
p.760

The Research and Analysis on Valve Reliability of Reciprocating Compressor
p.764

The Reliability Estimation of Double-Parameter Exponential Distribution on Zero-Failure Data
p.769

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 605-607The PHM Research of Vessels Equipment Based on...

The PHM Research of Vessels Equipment Based on Condition

Abstract:

For a long time, the monitor and maintain of navy Vessels equipment is based on the traditional maintain theories and plan maintain. But with the development of electronical and digitization, this kind of traditional mode is more and more hard to satisfy the maintaining need of Vessels equipment, and restrict the formation and exaltation of troops fighting strength. Currently the technologies of fault prognostics and integrated health management are becoming regarded, especially on many Vessels equipment systems. The information about abnormal situation of Vessels equipment was received and estimated by fault diagnosis and prognostic module. The diagnostic and prognostic results were supplied to the analysis and decision module to make the conclusion. The paper talks about the structure and function of the prognostic and health system, which is for certain kind of Vessels equipment.

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Periodical:

Advanced Materials Research (Volumes 605-607)

Pages:

750-755

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.605-607.750

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Online since:

December 2012

Authors:

Chun Jian Wang, Yong Zhi Liu, Hong Jun Fan

Keywords:

Equipment, Fault Diagnosis, Gray Theory, PHM

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