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Software Simulation Platform and Fault Diagnosis Method for an Underwater Equipment System

Author: ChouYanKai
Tutor: TangGuoYuan
School: Huazhong University of Science and Technology
Course: Marine Engineering
Keywords: Underwater equipment Simulation of software Wavelet analysis Neural networks Fault diagnosis
CLC: U676.42
Type: Master's thesis
Year: 2011
Downloads: 33
Quote: 0
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Abstract


In this paper, A 3D molding method for simulation system based on OpenGL and SolidWorks is introduced in details firstly. The technique route of modeling is put forward in the paper. The 3D model of a certain underwater equipment system is built, and some key technologies of 3D modeling are discussed.Firstly, the 3D virtual model estabished jointly by SolidWorks and OpenGL is transformed into the entire network model, then the real-time 3D movement simulation of the underwater equipment is realized by 3D animation technology. The man-machine interface is built by Visual C++ 6.0 software,MATLAB engines provide backend calculation service,and the data are stored into SQL Server 2005. Some important real-time parameters of the curve is displayed by Tee Chart. An underwater equipment system virtual reality platform was built by implemented three interface smoothing, seamless interconnection. The simulation platform system includes movement simulation module, database and data analysis module, fault diagnosis and analysis module, etc. The simulation platform system can monitor the motion of underwater equipment, the data storage function lay the foundation for fault diagnosis.The fault mechanism of rolling bearing is analyzed in this paper. The fault diagnosis method based on wavelet analysis is studied in the paper. The method is validated by use of experimental data of the university of CWRU in American. The study indicates that fault feature spectrum is not easily judgment in some conditions.In this paper,the vibration signal of normal and fault state of rolling bearing is analyzed in further. according to the parameter of processing. An intelligent rolling bearing fault diagnosis system is built according to the parameters characteristic of time domain and frequency domain features. The BP neural network algorithm is used to build normal and fault state training samples based on the experimental data of bearing vibration characteristic parameters from the United States CWUR university. The results show that the method can accurately identify fault characteristics and fault types of the rolling bearing according to actual data and the effect is well.

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CLC: > Transportation > Waterway transport > Marine Engineering > Navigation technology management and salvage > Engineering Management > Failed inspection and processing
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