Specifications

In the actual operation of hydropower unit, there are few fault samples for shaft centerline orbits.
Hence, the intelligent fault diagnosis cannot be performed accurately, and this problem must be
solved with the combination of the corresponding spectral characteristics. Aimed at this problem,
based on the improved support vector machine, a multi-fault classification algorithm was
presented, the Hu invariant moment data of shaft centerline orbit graph were selected as training
sample of the classification system, the error threshold level was inducted to effectively control
category interference phenomenon, and a multi-fault shaft centerline orbits classifier was set up.
Furthermore, it was applied to carry out the fault diagnosis of hydropower units. Results of the
fault diagnosis application showed that just a few measured samples of shaft centerline orbits
and a certain number of stimulated samples were needed in order to establish a fault classifier
with superior performance, when the number of samples was 16 and 50, the classification
accuracy was up to 96.3% and 91.2%, and the four different shapes of shaft centerline orbit
graphs such as double ring-shaped, eight-shaped, ellipse-shaped and banana-shaped can be
clearly distinguished. Meanwhile, the classification accuracy increased with an increase in the
number of classification and the classification accuracy decreased rapidly with an increase in the
number of sample, that is to say, the number of classification and the number of sample had an
important influence on the classification accuracy. In addition, the optimum classification surface
of invariant line moment can be obtained by adjustment of kernel function coefficient, the ability
of multi-category classification can be obviously improved by introduction of distinct matrix, and
it has been successfully verified in four different classifications. This fault classifier can realize the
identification and diagnosis of multi-faults. And it has both the advantages of simple algorithm
and strong capacity in pattern classification for multi-fault shaft centerline orbits. So the result
provides a reference for the intelligent fault diagnosis of shaft centerline orbits of hydropower
units with few fault samples.
Number of references: 28
Main heading: Orbits
Controlled terms: Algorithms - Classifiers - Experiments - Failure analysis - Fault
detection - Hydroelectric power - Support vector machines
Uncontrolled terms: Centerlines - Classification algorithm - Fault diagnosis applications
- Hydropower units - Intelligent fault diagnosis - Linear moments - Multi-category
classification - Spectral characteristics
Classification code: 901.3 Engineering Research - 802.1 Chemical Plants and Equipment -
723 Computer Software, Data Handling and Applications - 921 Mathematics - 655.2
Satellites - 422 Strength of Building Materials; Test Equipment and Methods - 421
Strength of Building Materials; Mechanical Properties - 611.1 Hydroelectric Power Plants
DOI: 10.3969/j.issn.1002-6819.2013.15.009
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
3.
Accession number: 20133616692382
Title: Terahertz radiation generated by laser induced plasma in photoconductive antenna
Authors: Hou, Lei1 ; Chen, Suguo1 ; Yan, Zhijin1 ; Shi, Wei1/侯磊;陈素果;;施卫
Author affiliation: 1 Applied Physics Department, Xi'An University of Technology, Xi'an 710048,