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Fatigue Damage Detection and Risk Assessment via Wavelet Transform and Neural Network Analysis of Ultrasonic Signals
Taibah University

Corresponding Author:[email protected]

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This paper develops a data-driven autonomous method for detection of fatigue damage and classification of the associated damage risk in mechanical structures, based on ultrasonic signal energy. The underlying concept is built upon attenuation of the signal and stability of the attenuation process. The attenuation provides pertinent information for damage quantification, whereas the stability represents resistance towards the fatigue damage growth. The proposed neural network (NN) model has been trained using the scaled conjugate-gradient back-propagation method. The NN model is capable of damage detection and damage classification into five classes of increasing risk. The Daubechies wavelet transform has been used to reduce the noisy pattern of the ultrasonic signal energy by using the associated approximation coefficients. The results show that the proposed method of approximation signal energy can detect and classify the damage with an accuracy of up to ∼ 9 8 . 5 % .
21 Sep 2021Submitted to Fatigue & Fracture of Engineering Materials & Structures
21 Sep 2021Submission Checks Completed
21 Sep 2021Assigned to Editor
01 Oct 2021Reviewer(s) Assigned
08 Dec 2021Review(s) Completed, Editorial Evaluation Pending
15 Dec 2021Editorial Decision: Revise Major
03 Jan 20221st Revision Received
28 Jan 2022Submission Checks Completed
28 Jan 2022Assigned to Editor
03 Feb 2022Reviewer(s) Assigned
07 Feb 2022Review(s) Completed, Editorial Evaluation Pending
09 Feb 2022Editorial Decision: Accept