Feature extraction and selection for defect classification of pulsed eddy current NDT

Chen, Tianlu; Tian, Gui Yun; Sophian, Ali; Que, Peiwen

doi:10.1016/j.ndteint.2008.02.002

Cited by 107 publications

(40 citation statements)

References 14 publications

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“…Different features reflect different aspects of the fault information of the bearing. Effective feature extraction and selection and preprocessing are critical for successful classification [29]. The increase in the number of features will inevitably lead to redundancy and curse of dimensionality while ensuring comprehensive and complete access to the fault information.…”

Section: Feature Extractionmentioning

confidence: 99%

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Yao

2017

Journal of Sensors

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Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models.

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Section: Feature Extractionmentioning

confidence: 99%

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Yao

2017

Journal of Sensors

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“…Defect classification is a general issue that focuses on the location and type of the defect. [1][2][3] This paper mainly discusses the classification of different defects in the bottom surface of metal plate. In most cases, two common types of defect, pits and cracks, correspond to a loss of efficacy in the metal structure.…”

Section: Introductionmentioning

confidence: 99%

An SVM approach with alternating current potential drop technique to classify pits and cracks on the bottom of a metal plate

Gan

Wan

et al. 2016

AIP Advances

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An SVM approach with alternating current potential drop technique to classify pits and cracks on the bottom of a metal plateThe alternating current potential drop (ACPD) is a nondestructive technique that is widely used to detect and size defects in conductive material. This paper describes a combined ACPD and support vector machine (SVM) approach to accurately recognize typical defects on the bottom surface of a metal plate, i.e., pits and cracks. We first conducted a simulation study, and then, based on ACPD, measured five voltage ratios between the test region and reference region. The analysis of finite simulation data enables the binary classification of two kinds of defects. To obtain an accurate separating hyperplane, key parameters of the SVM classifier were optimized using a genetic algorithm with training data from the simulations. Based on the optimized SVM classifier, reliable estimates of the defects in a metal plate were then obtained. The recognition results of the simulation dataset shows that the trained and optimized SVM model has a high classification accuracy, and the metal plate experiment also indicates that the model has good precision in actual defect classification. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

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“…Each of these methods has advantages for specific tasks. Several methods address the challenge of getting a higher image quality; therefore image processing/analysis have become a central issue within almost all thermal non-destructive testing (TNDT) problems [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Thermal non-destructive testing: Localization of hidden defects

Hatefipour¹,

Ahlberg²,

Wren³

et al. 2014

Proceedings of the 2014 International Conference on Quantitative InfraRed Thermography

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During the last decades, a substantial amount of research and practical work has been conducted on non-destructive testing of materials using thermography. The performed studies elucidate the potential of various types of thermal non-destructive testing (TNDT) for different materials and applications, including various types of defects. This paper presents a method for detecting in-depth defects in metallic materials and a simulation model for the heat transfer in the material. Experiments are performed on a test specimen with artificial defects (flatbottom holes). The detection method exploits spatiotemporal analysis in order to find deviations from a model of normality, and shows novel results. Thermal modelling is performed in order to have a base-line simulation model enable us to (a) investigate affecting parameters without repeating the experiments and (b) generalize the results and extend their validity to other cases. Results show that there is an acceptable compliance between simulated and measured thermal data.

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Feature extraction and selection for defect classification of pulsed eddy current NDT

Cited by 107 publications

References 14 publications

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

An SVM approach with alternating current potential drop technique to classify pits and cracks on the bottom of a metal plate

Thermal non-destructive testing: Localization of hidden defects

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