Paper Title:
ANN-Based Crack Identification in Rotor System with Multi-Crack in Shaft
  Abstract

Rotating machinery, such as steam turbo, compressor, and aeroengine etc., are widely used in many industrial fields. Among the important rotor faults, the fatigue crack fault, which can lead to catastrophic failure and cause injuries and severe damage to machinery if undetected in its early stages, is most difficult to detect efficiently with traditional methods. In the paper, based on the truth of the change of the mode shapes of the cracked structure, a new method by combining accurate finite element model of rotor with multi-crack in shaft and artificial neural network (ANN) is proposed to identify the location and depth of cracks in rotating machinery. First, based on fracture mechanics and the energy principle of Paris, the accurate FE model of the rotor system considering several localized on-edge non-propagating open cracks with different depth, is built to produce the specific mode shapes. Then a set of different mode shapes of a rotor system with localized cracks in several different positions and depths, which will be treated as the input of the designed ANN model, can be obtained by repeating the above step. At last, with several selected crack cases, the errors between the results obtained by using the trained ANN model and FEM ones are compared and illustrated. Meanwhile, the influences of crack in the different position on the identification success are analyzed. The method is validated on the test-rig and proved to have good effectiveness in identification process.

  Info
Periodical
Key Engineering Materials (Volumes 353-358)
Edited by
Yu Zhou, Shan-Tung Tu and Xishan Xie
Pages
2463-2466
DOI
10.4028/www.scientific.net/KEM.353-358.2463
Citation
T. Yu, Y. Yang, Q. K. Han, H. L. Yao, B. C. Wen, "ANN-Based Crack Identification in Rotor System with Multi-Crack in Shaft", Key Engineering Materials, Vols. 353-358, pp. 2463-2466, 2007
Online since
September 2007
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Goh Lyn Dee, Norhisham Bakhary, Azlan Abdul Rahman, Baderul Hisham Ahmad
Abstract:This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The...
2756
Authors: K. Zarrabi, W.W. Lu, A.K. Hellier
Abstract:This paper proposes a new three-layer artificial neural network (ANN) to predict the fatigue crack length under constant amplitude mode I...
3
Authors: Christos Katsikeros, Claudio Sbarufatti, George Lampeas, Ioannis Diamantakos
Abstract:In the present work a Structural Health Monitoring (SHM) system based on the use of Artificial Neural Network (ANN) method is presented that...
129
Authors: Ling Hua Xiong, Fan Wang
Chapter 12: Monitoring and Control of Structures
Abstract:Fatigue failure is the major cause of malignant accident of gantry crane. In this paper fatigue analysis of gantry crane is studied . Based...
3351
Authors: Yang Lei, Jing Ma
Chapter 6: Information Technologies, WEB and Networks Engineering, Information Security, Software Application and Development
Abstract:The issue of intrusion detection has been an active topic in both military and civilian areas, and a great many relevant algorithms and...
2519