Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy

Serra, Roger; Lopez, Lautaro

doi:10.1051/meca/2018007

Cited by 10 publications

(6 citation statements)

References 31 publications

(41 reference statements)

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“…However, this technique depends on an experiment based on comparing modal strain energies at different degradation stages. More recently, Serra and Lopez [36] presented a combined modal wavelet strategy. They compared it with the most frequently used indicators and widely studied methods in order to identify the damages.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

2022

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Health surveillance in industries is an important prospect to ensure safety and prevent sudden collapses. Vibration Based Structure Health Monitoring (VBSHM) is being used continuously for structures and machine diagnostics in industry. Changes in natural frequencies are frequently used as an input parameter for VBSHM. In this paper, the Frequency Shift Coefficient (FSC) is used for the assessment of various numerical damaged cases. An FSC-based algorithm is employed in order to estimate the positions and severity of damages using only the natural frequencies of healthy and unknown (damaged) structures. The study focuses on cantilever beams. By considering the minimization of FSC, damage positions and severity are obtained. Artificially damaged cases are assessed by changes in its positions, the number of damages and the size of damages along with the various parts of the cantilever beam. The study is further investigated by considering the effect of uncertainty on natural frequencies (0.1%, 0.2% and 0.3%) in damaged cases, and the algorithm is used to estimate the position and severity of the damage. The outcomes and efficiency of the proposed FSC based method are evaluated in order to locate and quantify damages. The efficiency of the algorithm is demonstrated by locating and quantifying double damages in a real cantilever steel beam using vibration measurements.

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Section: Introductionmentioning

confidence: 99%

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

2022

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“…erefore, the presence of defects will cause the model parameters of the structure to change; these changes of undamaged and damaged structures can be utilized to diagnose the location and severity of the defect. Generally, the finite element model (FEM) updating method is employed to address such problems [3][4][5]. In this process, the fitness function is designed to minimize the discrepancy of the responses between model and measurement, which is a key factor in detecting the location and severity of defects.…”

Section: Introductionmentioning

confidence: 99%

Performance of Fitness Functions Based on Natural Frequencies in Defect Detection Using the Standard PSO‐FEM Approach

Serra

Olivier

2021

Shock and Vibration

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Structural defect detection based on finite element model (FEM) updating is an optimization problem by minimizing the discrepancy of responses between model and measurement. Researchers have introduced many methods to perform the FEM updating for defect detection of the structures. A popular approach is to adopt the particle swarm optimization (PSO) algorithm. In this process, the fitness function is a critical factor in the success of the PSO-FEM approach. Our objective is to compare the performances of four fitness functions based on natural frequencies using the standard PSO-FEM approach for defect detection. In this paper, the definition of the standard PSO algorithm is first presented. After constructing the finite element benchmark model of the beam structure, four commonly used fitness functions based on natural frequencies are outlined. Their performance in defect detection of beam structures will be evaluated using the standard PSO-FEM approach. Finally, in the numerical simulations, the population diversity, success rate, mean iterations, and CPU time of the four fitness functions for the algorithm are calculated. The simulation results comprehensively evaluate their performances for single defect and multidefect scenario, and the effectiveness and superiority of the fitness function S 4 will be demonstrated.

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“…Continuous wavelet transforms (CWTs) on the fundamental mode shapes are used to identify the multiple cracks in the plate structures. Roger Serra and Lopez (Serra and Lopez, 2017) used stationary wavelet transforms and CWTs on mode shape difference (with and without damage) in the cantilever beam to identify the damage (Cao and Qiao, 2008). Prasad and Sekhar (2015) used displacement and strain energy to locate and quantify the damage in noisy signals from ODS of the rotor.…”

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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Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy

Cited by 10 publications

References 31 publications

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

Performance of Fitness Functions Based on Natural Frequencies in Defect Detection Using the Standard PSO‐FEM Approach

Fault detection in rotor system by discrete wavelet neural network algorithm

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