Damage Detection on a Beam with Multiple Cracks: A Simplified Method Based on Relative Frequency Shifts

Gillich, Gilbert-Rainer; Maia, N. M. M.; Wahab, Magd Abdel; Tufiși, Cristian; Korka, Zoltan-Iosif; Gillich, Nicoleta; Pop, Marius-Vasile

doi:10.3390/s21155215

Cited by 21 publications

(17 citation statements)

References 40 publications

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“…If the fixing is nonideal, the frequency drop is bigger. In prior research, we demonstrated that the superposition principle is valid [26]. For a beam with two cracks, which have position 1 x , and depths 1 a and 2 a , respectively, the frequency drop is calculated as the sum of the two RFS, that is…”

Section:  mentioning

confidence: 97%

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Gillich¹,

Tufiși²,

Săcărea³

et al. 2022

Preprint

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Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

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Section:  mentioning

confidence: 97%

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Gillich¹,

Tufiși²,

Săcărea³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…If the fixing is non-ideal, the frequency drop is bigger. In prior research, we demonstrated that the superposition principle is valid [ 33 ]. For a beam with two cracks, which have position

and

, and depths

and

, respectively, the frequency drop is calculated as the sum of the two RFS, that is

…”

Section: Creating the Database For Machine Learning (Ml)mentioning

confidence: 99%

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Gillich

Tufiși

Săcărea

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Damage detection based on modal parameter changes has become popular in the last few decades. Nowadays, there are robust and reliable mathematical relations available to predict natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF), and the artificial neural network (ANN), as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment was made in two steps. First, a coarse damage location was found from the networks trained for scenarios comprising the whole beam. Afterwards, the assessment was made involving a particular network trained for the segment of the beam on which the crack was previously found. Using the two machine learning methods, we succeeded in estimating the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which was the main goal of the practitioners, the errors were less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

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“…By using the method described in the current paper, by employing Equation (11), we have generated training data for developing a damage detection neural network similar to the one presented in [13]. The training data consist of the RFS values for the six transverse vibration modes.…”

Section: Tests Performed Involving Laboratory Experimentsmentioning

confidence: 99%

“…When examining the methods proposed in the literature, we observed that the dependency between the reduction in the size of the crack and the stiffness is rarely considered. In general, the authors limit the study to finding the element in the beam with the lower Young modulus value [13]. When damage detection is applied to trusses, finding the member with the lower Young modulus is usually the target.…”

Section: Introductionmentioning

confidence: 99%

Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method

et al. 2022

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Evaluating the integrity of structures is an important issue in engineering applications. The use of vibration-based techniques has become a common approach to assessing cracks, which are the most frequently occurring damage in structures. When involving an inverse method, it is necessary to know the influence of the position and the geometry of the crack on the modal parameter changes. The geometry of the crack, both in size and shape, defines the damage severity (DS). In this study, we present a method (DS-SHC) used for estimating the DS for closed and open transverse cracks in beam-like structures using the intact and damaged beam deflections under its weight and a Stochastic Hill Climbing (SHC) algorithm. After describing the procedure of applying DS-SHC, we calculate for a prismatic cantilever beam the severities for different crack types and depths. The results are tested by comparing the DS obtained with DS-SHC with those acquired from dynamic tests made using professional simulation software. We obtained a good fit between the severities determined in these two ways. Subsequently, we performed laboratory experiments and found that the severities obtained with the DS-SHC method can accurately predict the frequency changes due to the crack. Hence, these severities are a valuable tool for damage detection.

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Damage Detection on a Beam with Multiple Cracks: A Simplified Method Based on Relative Frequency Shifts

Cited by 21 publications

References 40 publications

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method

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