Identification of Crack Location in Beam Structures Using Wavelet Transform and Fractal Dimension

Jiang, Yongying; Li, Bing; Zhang, Zhou-Suo; Chen, Xuefeng

doi:10.1155/2015/832763

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Wavelet scales and coefficients can be used to estimate damage severity (Janeliukstis et al, 2016). Factor dimension of the detailed signals of DWTs can be used to locate the cracks accurately (Jiang et al, 2015). Wavelet transform of beat signals is used to estimate the damping of the rotor system (Chandra and Sekhar, 2014).…”

Section: Introductionmentioning

confidence: 99%

Fault detection in rotor system by discrete wavelet neural network algorithm

Rao

Reddy

2021

Journal of Vibration and Control

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This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

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Section: Introductionmentioning

confidence: 99%

Fault detection in rotor system by discrete wavelet neural network algorithm

Rao

Reddy

2021

Journal of Vibration and Control

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“…The wavelet coefficients will reflect with peaks when any changes or discontinuities occur in data. By the use of such ridges and peaks in wavelet analysis, we could exactly locate the cracks and damages present in the micro cantilever beam (6,7) . Dynamic characteristics of the beam will reflect with respect to the damage or cracks over the surface.…”

Section: Introductionmentioning

confidence: 99%

Identification of multiple crack locations in micro cantilever beam by wavelet transforms

Ravikumar¹,

Priya²

2020

IJST

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Background/Objectives: Presence of cracks in micro cantilever beams badly influence the performance of the device. This study is to propose suitable method utilizing wavelet transform to detect the cracks present over the structure of micro cantilever beams. Methods: The proposed device structure was designed and modeled using COMSOL Multi-physics simulation software. The signals obtained are analyzed using wavelet transform. Findings: Micro cantilevers shows difference in its vibrational frequency, stress and deflection over various input conditions. The location of the multiple cracks which appears in micro cantilever structure due to various working conditions were perfectly identified using wavelet transform analysis. Novelty/Applications: We report the accurate locations of the crack present in micro structured devices. This ideal method can be applied on micro cantilever devices which found its applications as Transducers, Sensors, Switches, Actuators and Probes.

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“…They used a wavelet method to analyze the mode shape of the crack and found that small cracks can't be detected and require the use of fractal dimension. Also they performed their study on the cantilever beam and validated their method by simulating the effectiveness of the method [12]. Abdulkareem et al, investigated a diagnosis of damage in a steel plate.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Crack Detection in Steel Cantilever Beam Using Wavelet Analysis by Combination Mode Shapes

Pour¹,

Marnani²,

Tabatabei³

2018

IJIGSP

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The first step in Structures Health Monitoring (SHM), are determining the location, intensity and type of damage in structures. Crack is a damage that often occurs in structural elements and may cause serious ruptures in the structure. One of the important approaches is the wavelet analysis of vibration modes structures. In this study, it has been performed the crack detection method in steel cantilever beam structure, using an optimized wavelet-based model. The wavelet analysis has been performed based on the higher orders of the structure's mode shapes. The results show that the proposed method is able to accurately detect all kinds of cracks, in which the cracks location are variable. In this study also, cracks with length of 20%, 10%, 5% and 2% of the beam's depth have been considered and one of the most prominent results is introducing a method for detecting robust and environmental noisy cracks. The proposed method is capable of accurately detecting crack in the cantilever beams in noisy conditions about 20 dB of SNR.

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Identification of Crack Location in Beam Structures Using Wavelet Transform and Fractal Dimension

Cited by 7 publications

References 37 publications

Fault detection in rotor system by discrete wavelet neural network algorithm

Fault detection in rotor system by discrete wavelet neural network algorithm

Identification of multiple crack locations in micro cantilever beam by wavelet transforms

A Novel Method for Crack Detection in Steel Cantilever Beam Using Wavelet Analysis by Combination Mode Shapes

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