Structural damage detection and localization using a hybrid method and artificial intelligence techniques

Hoshyar, Azadeh Noori; Samali, Bijan; Liyanapathirana, Ranjith; Houshyar, Afsaneh Nouri; Yu, Yang

doi:10.1177/1475921719887768

Cited by 18 publications

(8 citation statements)

References 54 publications

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“…These dynamic responses contain information regarding the locations and sizes of the crack(s). Many researchers have focused on the vibration characteristics of cracked beams [ 1 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Ostachowicz and Krawczuk (1991) modelled the crack as a torsional spring model and calculated the natural frequencies of single-sided and double-sided crack cantilever beams [ 46 ].…”

Section: Introductionmentioning

confidence: 99%

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Khan

Aldosari

2022

Polymers

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Acrylonitrile butadiene styrene (ABS) is the most commonly used thermoplastic printing material for fused deposition modelling (FDM). FDM ABS can be used in a variety of complex working environments. Notably, the thermo-mechanical coupled loads under complex operating conditions may lead to cracking and ultimately catastrophic structural failure. Therefore, it is crucial to determine the crack depth and location before a structural fracture occurs. As these parameters affect the dynamic response of the structure, in this study, the fundamental frequency and displacement amplitude response of a cracked 3D-printed ABS cantilever beam in a thermal environment were analytically and experimentally investigated. The existing analytical model, specifically the torsional spring model used to calculate the fundamental frequency change to determine the crack depth and location was enhanced by the proposed Khan-He model. The analytical relationship between the displacement amplitude and crack was established in Khan-He model and validated for the first time for FDM ABS. The results show that a reduced crack depth and location farther from the fixed end correspond to a higher fundamental frequency and displacement amplitude. An elevated ambient temperature decreases the global elastic modulus of the cracked beam and results in a lower fundamental frequency. Moreover, a non-monotonic relationship exists between the displacement amplitude and ambient temperature. The displacement amplitude is more sensitive to the crack change than the fundamental frequency in the initial stages of crack growth.

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Section: Introductionmentioning

confidence: 99%

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Khan

Aldosari

2022

Polymers

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“…Principle Component Analysis (PCA) and hierarchicalnonlinear PCA (h-NLPCA) (D. A. Tibaduiza et al, 2016), (Murta et al, 2018), (Arcos Jiménez et al, 2019), (Miorelli et al, 2019), (Ghrib et al, 2019), (Sen et al, 2019), (Sbarufatti et al, 2014), (D. Tibaduiza et al, 2018), (Jiménez et al, 2019), Continuous / Discrete wavelet transform (CWT/DWT) (Atashipour et al, 2012), (G. Yan, 2013), (Liew & Veidt, 2009), (D. Tibaduiza et al, 2018, , (Ewald et al, 2019), (Z. , (Hoshyar et al, 2019) Linear and nonlinear autoregressive models (Jiménez et al, 2018), (Arcos Jiménez et al, 2020), (Arcos Jiménez et al, 2019), (Jiménez et al, 2019), (Dackermann et al, 2014),…”

Section: Reference Examplesmentioning

confidence: 99%

Emergence of Machine Learning Techniques in Ultrasonic Guided Wave-based Structural Health Monitoring

Sattarifar¹,

Nestorović

2022

IJPHM

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Identification of damage in its early stage can have a great contribution in decreasing the maintenance costs and prolonging the life of valuable structures. Although conventional damage detection techniques have a mature background, their widespread application in industrial practice is still missing. In recent years the application of Machine Learning (ML) algorithms have been more and more exploited in structural health monitoring systems (SHM). Because of the superior capabilities of ML approaches in recognizing and classifying available patterns in a dataset, they have demonstrated a significant improvement in traditional damage identification algorithms. This review study focuses on the use of machine learning (ML) approaches in Ultrasonic Guided Wave (UGW)-based SHM, in which a structure is continually monitored using permanent sensors. Accordingly, multiple steps required for performing damage detection through UGWs are stated. Moreover, it is outlined that the employment of ML techniques for UGW-based damage detection can be subtended into two main phases: (1) extracting features from the data set, and reducing the dimension of the data space, (2) processing the patterns for revealing patterns, and classification of instances. With this regard, the most frequent techniques for the realization of those two phases are elaborated. This study shows the great potential of ML algorithms to assist and enhance UGW-based damage detection algorithms.

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“…Yan and Jia [36] optimized the stacked denoising auto-encoder through multi-domain indicators to indicate a rolling bearing's health condition. Hoshiyar et al [37] introduced the intelligent scheme for detecting and localizing the structural damage by implementing the hybrid method using the Hilbert-Huang transform and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

An effective health indicator for the Pelton wheel using a Levy flight mutated genetic algorithm

Vashishtha

Kumar

2021

Meas. Sci. Technol.

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Fluctuations in the head, discharge, and contaminants in the flow can damage parts of the Pelton wheel. An artificial intelligence technique has been investigated for the automatic detection of bucket faults in the Pelton wheel. Features sensitive to defect conditions are extracted from the raw vibration signal and its variational mode decomposition (VMD). The issue of slow convergence speed of the genetic algorithm during optimization is duly addressed by implementing a Levy flight mutated genetic algorithm (LFMGA) while finding the optimal parameters (regularization parameter and kernel function) of a support vector machine (SVM). The efficacy of the proposed LFMGA is tested against different optimization benchmark functions. The results indicate that the proposed algorithm is stable on the basis of the small standard deviation. Using optimized SVM parameters, the SVM model is trained to prepare a classification model with 10-fold cross-validation. After training, the SVM model is tested for fitness evaluation. The overall recognition rate of the SVM model for identification of defects is found to be 98.84% with training time 27.06 s per iteration. A healthy condition is also compared with splitter wear, added mass defect, and missing bucket conditions separately using the VMD–SVM model and shows a recognition rate of 99.17%, 98.33%, and 98.12%, respectively.

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Structural damage detection and localization using a hybrid method and artificial intelligence techniques

Cited by 18 publications

References 54 publications

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Emergence of Machine Learning Techniques in Ultrasonic Guided Wave-based Structural Health Monitoring

An effective health indicator for the Pelton wheel using a Levy flight mutated genetic algorithm

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