Literature DB >> 23978617

Material damage diagnosis and characterization for turbine rotors using three-dimensional adaptive ultrasonic NDE data reconstruction techniques.

Xuefei Guan1, Jingdan Zhang, El Mahjoub Rasselkorde, Waheed A Abbasi, S Kevin Zhou.   

Abstract

Damage diagnosis for turbine rotors plays an essential role in power plant management. Ultrasonic non-destructive examinations (NDEs) have increasingly been utilized as an effective tool to provide comprehensive information for damage diagnosis. This study presents a general methodology of damage diagnosis for turbine rotors using three-dimensional adaptive ultrasonic NDE data reconstruction techniques. Volume reconstruction algorithms and data fusion schemes are proposed to map raw ultrasonic NDE data back to the structural model of the object being examined. The reconstructed volume is used for automatic damage identification and quantification using region-growing algorithms and the method of distance-gain-size. Key reconstruction parameters are discussed and suggested based on industrial experiences. A software tool called AutoNDE Rotor is developed to automate the overall analysis workflow. Effectiveness of the proposed methods and AutoNDE Rotor are explored using realistic ultrasonic NDE data.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Damage diagnosis; Data reconstruction; Non-destructive examination; Rotor; Ultrasonic inspection

Mesh:

Year:  2013        PMID: 23978617     DOI: 10.1016/j.ultras.2013.07.019

Source DB:  PubMed          Journal:  Ultrasonics        ISSN: 0041-624X            Impact factor:   2.890


  2 in total

1.  A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data.

Authors:  Jingjing He; Yunmeng Ran; Bin Liu; Jinsong Yang; Xuefei Guan
Journal:  Sensors (Basel)       Date:  2017-09-13       Impact factor: 3.576

2.  Application of Ultrasonic Array Method for the Inspection of TC18 Addictive Manufacturing Titanium Alloy.

Authors:  Wentao Li; Zhenggan Zhou; Yang Li
Journal:  Sensors (Basel)       Date:  2019-10-10       Impact factor: 3.576

  2 in total

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