Specifications

using the J integral method are introduced in detail in X-RPIM. The impact for the computational
results of stress intensity factors using different integral domains of crack tip is discussed.
Analyses of numerical examples demonstrate that the enriched radial point interpolation
meshless method can effectively solve fracture problem, and has practical merits for modeling
crack growth problem.
Number of references: 15
Main heading: Problem solving
Controlled terms: Crack propagation - Crack tips - Fracture - Interpolation -
Stress intensity factors
Uncontrolled terms: Approximation function - Computational results - Crack
faces - Discontinuous displacement field - Governing equations - Growth problems -
Integral domains - J-integral method - Linear elastic fracture - Mesh-less methods -
Mixed-mode stress - Numerical example - Partition of unity - Radial point
interpolations - Shape functions - Stress singularities
Classification code: 421 Strength of Building Materials; Mechanical Properties - 921
Mathematics - 921.6 Numerical Methods
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
9.
Accession number: 20130315915213
Title: Process neural network based on EMD for fault fusion diagnosis of draft tube
Authors: Wang, Han1, 2 ; Zhang, Xinwei2 ; Luo, Xingqi2 ; Xu, Minghai3/王瀚;张欣伟;罗兴锜;
许明海
Author affiliation:
1 Hydro-China Xibei Engineering Corporation, Xi'an 710065, China
2 Department of Power Engineering, Xi'an University of Technology, Xi'an 710048, China
3 Gansu Jiuquan Power Supply Company, Jiuquan, Gansu 735000, China
Corresponding author: Luo, X. ([e-mail])
Source title: Shuili Fadian Xuebao/Journal of Hydroelectric Engineering
Abbreviated source title: Shuili Fadian Xuebao
Volume: 31
Issue: 6
Issue date: December 2012
Publication year: 2012
Pages: 282-287
Language: Chinese
ISSN: 10031243
Document type: Journal article (JA)
Publisher: Tsinghua University Press, Beijing, 100084, China
Abstract: To diagnose accurately vortex rope in the draft tube of hydraulic turbine, this
paper presents a new method of fault diagnosis based on empirical mode decomposition (EMD),
index energy and process neural network (PNN). This method adopts an EMD method to
decompose the monitored pressure pulsation signals of draft tube and constructs index energy
vectors of the signals. Then it takes those vectors as fault samples to train a three-layer discrete