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

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

doi:10.1016/j.ultras.2013.07.019

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…The ultrasound data, particularly the pulse-echo data, are usually corrupted with various noise components such as speckle noise, electrical noise, interface noise, and the geometric echo noise, significantly limiting the visual identification of the flaws [34,35]. In addition, the near-surface region cannot be inspected effectively due to the near-field effect of the ultrasound [36].…”

Section: Methodology Developmentmentioning

confidence: 99%

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

Zhang

Wang

Guan

2020

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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Abstract This study presents a method of ultrasonic flaw identification using phased array ultrasonic inspection data. Raw data from each individual channel of the phased array ultrasonic inspection are obtained. The data trimming and de-noising are employed to retain the data within the boundary of the inspected object and remove the speckle noise components from the raw data, respectively. The resulting data are passed into a sequence of signal processing operations to identify embedded flaws. A shape-based filtering method is proposed to reduce the intensity of geometric noise components due to the non-uniform microstructures introduced in the manufacturing process. The resulting data matrices are integrated to obtain the intensity matrix of the possible flaw regions. Thresholding is applied to the intensity matrix to obtain the potential flaw regions, followed by a connected component analysis to identify the flaws. The overall method is demonstrated and validated using realistic phased array experimental data.

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Section: Methodology Developmentmentioning

confidence: 99%

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

Zhang

Wang

Guan

2020

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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“…Structural health monitoring (SHM) is an integrated approach combining data acquisition and interpretation. It provides a means for damage status assessment and remaining useful life prediction [ 1 , 2 , 3 , 4 ]. In recent years, guided ultrasonic waves have gained widespread applications in the field of SHM as a method of non-destructive evaluations (NDE) due to its ability of traveling large distances in structures with little energy loss [ 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Ran

Liu

et al. 2017

Sensors

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This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions.

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“…Several non-destructive evaluation methods such as ultrasound inspection [7][8][9][10][11][12][13], eddy-current testing [14,15], and Lamb wave-based methods [16][17][18][19][20][21] can effectively identify local damages in a component. To identify the component damage on a global basis, capturing the changes in vibration characteristics of the entire structure is one of the few methods that can be applied in practice [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Damage Identification Method for Shear Structure Components Based on Cross-Entropy Optimizations

Guan

Wang

2017

Entropy

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A probabilistic damage identification method for shear structure components is presented. The method uses the extracted modal frequencies from the measured dynamical responses in conjunction with a representative finite element model. The damage of each component is modeled using a stiffness multiplier in the finite element model. By coupling the extracted features and the probabilistic structural model, the damage identification problem is recast to an equivalent optimization problem, which is iteratively solved using the cross-entropy optimization technique. An application example is used to demonstrate the proposed method and validate its effectiveness. Influencing factors such as the location of damaged components, measurement location, measurement noise level, and damage severity are studied. The detection reliability under different measurement noise levels is also discussed in detail.

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

Cited by 17 publications

References 22 publications

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

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

A Probabilistic Damage Identification Method for Shear Structure Components Based on Cross-Entropy Optimizations

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