Prediction of Fatigue Crack Growth in Gas Turbine Engine Blades Using Acoustic Emission

Zhang, Zhiheng; Yang, Guoan; Hu, Kun

doi:10.3390/s18051321

Cited by 32 publications

(20 citation statements)

References 35 publications

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“…In aircraft components, the AE technology was used to detect fatigue cracks under a load [ 28 , 29 , 30 ]. In a storage tank, AE testing was used to monitor the storage tank health and to detect and locate corrosion and leakage in the bottom plate of storage tank [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Suwansin

Phasukkit

2021

Sensors

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This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Suwansin

Phasukkit

2021

Sensors

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“…The acoustic emission (AE) technique is a promising non-destructive surveillance technology for materials study and in situ supervision of structures [ 3 ]. The acoustic emission detection technique has been widely used in damage detection of metallic materials [ 4 , 5 , 6 ], composite materials [ 7 , 8 , 9 ], and rock materials [ 10 , 11 , 12 , 13 ]. Ennaceur et al [ 6 ] studied the damage and cracking of two metallic materials, 304L and P265GH steel, the using acoustic emission technique, which found that acoustic emission parameters such as count and amplitude can be used to characterize different crack propagation mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Tensile Damage Study of Wind Turbine Tower Material Q345 Based on an Acoustic Emission Method

et al. 2021

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As essential load-bearing equipment to support the nacelle and blades, the tower is subjected to the whole wind turbine loading. This study proposes a new method of combining acoustic emission and normalized accumulation parameters to characterize wind turbine towers Q345 steel damage. First of all, tendency analysis of the acoustic emission signal parameter was conducted to determine damage degree during the damage stage. Secondly, we normalized the accumulation of amplitude and other parameters to compare the proportion of each accumulation parameter at different stages, while studying the spectra of common acoustic emission signals. Finally, comparing the differences and similarities of the normalized accumulation parameters between three different rates, we analyze the effect of rate on the normalized accumulation parameters. These results indicate that the normalized cumulative duration parameter is suitable for characterizing the yield damage occurrence, the normalized cumulative energy parameter is very sensitive to the fracture stage, the normalized cumulative energy parameter is least influenced by the loading rate, and the energy parameter is a sensitivity factor for normalized expression, which to realizes the stage of damage judgment.

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“…Accurately identifying the location of microscopic fractures and damage is the scientific basis for researching the failure mechanism of materials, predicting rock bursts, and safely operating large industrial equipment [10][11][12][13][14][15][16][17]. Therefore, it is highly worthwhile to develop a high-precision AE source location method [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Linear Least Squares Location Method of an Acoustic Emission Source without Measuring Wave Velocity

Zhou

Rui

Cai

et al. 2020

Sensors

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The location of an acoustic emission (AE) source is crucial for predicting and controlling potential hazards. In this paper, a novel weighted linear least squares location method for AE sources without measuring wave velocity is proposed. First, the governing equations of each sensor are established according to the sensor coordinates and arrival times. Second, a mean reference equation is established by taking the mean of the squared governing equations. Third, the system of linear equations can be obtained based on the mean reference equation, and their residuals are estimated to obtain their weights. Finally, the AE source coordinate is obtained by weighting the linear equations and inserting the parameter constraint. The AE location method is verified by a pencil lead break experiment, and the results show that the locating accuracy of the proposed method is significantly higher than that of traditional methods. Furthermore, the simulation test proves that the proposed method also has a better performance (location accuracy and stability) than the traditional methods under any given scale of arrival errors.

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Prediction of Fatigue Crack Growth in Gas Turbine Engine Blades Using Acoustic Emission

Cited by 32 publications

References 35 publications

Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Tensile Damage Study of Wind Turbine Tower Material Q345 Based on an Acoustic Emission Method

A Weighted Linear Least Squares Location Method of an Acoustic Emission Source without Measuring Wave Velocity

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