NDLI: Crack fault identification in rotor shaft with artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Tao Yu Qingkai Han
Copyright Year	2010
Description	Author affiliation: School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China (Qingkai Han) \|\| School of Electromechanical Automobile Engineering, Yantai University, China (Tao Yu)
Abstract	Based on the truth of the change of the mode shapes of cracked structure and ANN's strong capability on nonlinear approximation, a novel method by combining modal analysis of cracked rotor system and artificial neural network (ANN) and is proposed for fast and precise identification of crack fault in rotor shaft, including single crack case and multi-crack case. To obtain the specific mode shapes of cracked rotor system, fracture mechanics theory and the energy principle of Paris are introduced into the modeling of finite element (FE) model of the cracked rotor system. Thus, a set of different mode shapes of a rotor system with one or several localized on-edge non-propagating open cracks in different positions and depths will be produced to be fed into the pre-designed ANN model with back-propagation learning algorithm. Then the validation of the method is verified by several selected crack cases. The results show that the trained ANN models have good performance to identify the crack location and depth, single crack and dual cracks, with higher accuracy and efficiency. The idea can be used in fast identification of crack fault in rotating machinery.
Starting Page	1629
Ending Page	1634
File Size	2132648
Page Count	6
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583771
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Shafts ANN component fault diagnosis Shape Rotors Training data Artificial neural networks Rotor system Data models Crack fault
Content Type	Text
Resource Type	Article

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