Crack Detection in Cantilever Shaft Beam Using Natural Frequency

Satpute, Dinesh; Baviskar, Prasad Ramchandra; Gandhi, Pritesh J.; Chavanke, Mayur; Aher, Tejas

doi:10.1016/j.matpr.2017.01.158

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…These findings are identical with what was found by Hamidi, et al [18], who was mentioned that the rate of change in the natural frequencies could be detectable only when the crack depth ratio gets more significant than 0.3. However, in Figure 7, the changes in the natural frequencies become apparent when the crack depth ratios are higher than 0.25 that can be related due to reduce the shaft stiffness and thus its natural frequencies as more material is removed from it when the crack depth gets increased [19].…”

Section: Resultsmentioning

confidence: 98%

Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network

Flaieh

Hamdoon

Jaber

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The early detection of faults in rotating systems considers an integral approach that has received considerable attention from the industrial sector, as it contributes to preventing catastrophic failures in machines. In this research, the natural frequencies of a shaft, when it is healthy and when cracks with different depths are introduced, have been calculated. The deviation of the computed natural frequencies from the healthy ones is counted as a sign of the presence of an abnormality in the system. For this intention, the finite element analysis (FEA) method based on ANSYS software has been utilized to obtain the first five natural frequencies of the shaft when there is a crack of different severity at different positions. The results of the FEA are used for designing an artificial neural network (ANN) model that can be easily used to predict the first five natural frequencies of the shaft based on just the crack's position and depth. Finally, the predicted natural frequencies by the deigned ANN have been compared to their peers that were computed using the FEA method. The absolute error percentage has then been calculated and used to get an indication of how close the result of both techniques is. The recorded highest error percentage was 0.67%, which is quite small and referring to that the designed ANN can accurately predict the natural frequencies of rotating systems.

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Section: Resultsmentioning

confidence: 98%

Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network

Flaieh

Hamdoon

Jaber

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In fact, wear, fatigue and cracks are the most common defects in mechanical structures, and their opportune detection represents a major issue to anticipate failures [15]. In this regard, damage detection (cracks) in cantilever shaft beams has been carried out using information about natural frequencies of transverse vibrations [16]. Here, it has been concluded that changes on natural frequencies depend on the crack location and dimensions [16].…”

Section: Introductionmentioning

confidence: 99%

Online Estimation Techniques for Natural and Excitation Frequencies on MDOF Vibrating Mechanical Systems

Silva-Navarro¹,

Beltrán-Carbajal

Trujillo-Franco

et al. 2021

Actuators

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An online algebraic estimation technique for natural and forcing frequencies for a class of uncertain and lumped-parameter vibrating mechanical systems with n degrees of freedom is described. In general, realistic vibrating systems can be affected by unknown exogenous excitation forces with multiple and independent frequency harmonic components. Hence, natural frequencies as well as excitation force frequencies can be simultaneously computed from an algebraic approach into a small interval of time during online operation of the mechanical system. Measurements of an available output signal, associated with some specific degree of freedom, are only required for frequency estimation in time-domain. Information on mass, stiffness and damping matrices are not necessary for multifrequency estimation algorithms. Some analytical, numerical and experimental results on a cantilever Euler–Bernoulli beam are described to show and validate the acceptable estimation of multiple frequencies in forced multiple degrees of freedom vibrating systems.

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“…Therefore, it is feasible to anticipate the crack characteristics by determining changes in the vibration parameters (Vakil-Baghmisheh et al, 2008). Its development in a beam leads to sudden failure of a system and machines without any prior indication or warning (Satpute et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…artificial neural networks. The behavior of the undamaged and cracked beam is compared according to the FEA results and it was found that natural frequency of transverse vibrations can be used to detect a crack in cantilever beam by Satpute et al (2017). Sutar et al (2015) investigated transverse crack in cantilever beam by proposing a neural network based controller.…”

Section: Introductionmentioning

confidence: 99%

Detection of crack in structure using dynamic analysis and artificial neural network

Maurya¹,

Sadarang²,

Panigrahi³

2020

10.5267/j.esm

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Cracks are one of the main causes of structural failure and they develop in the structures due to various reasons such as fatigue, temperature variation, excessive load, cyclic load, environmental effects, impact loading etc. Thus, structural health monitoring is necessary to avoid risks, damages and failures. So, in order to avoid an extensive failure or accident, the early prognosis of crack in structures is necessary. Visual inspection and some non-destructive testing (NDT) methods for detection of crack are difficult as it requires time, expenses and are quite inefficient. So the alternative methods are motivated to be developed. In this study, vibration analysis, finite element analysis (FEA) and an alternative way which is artificial neural network (ANN) is used to predict, detect and identify the damages in structures. It is found that the theoretical, experimental, finite element analysis and artificial neural network have good accuracy in predicting the crack characteristics.

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Crack Detection in Cantilever Shaft Beam Using Natural Frequency

Cited by 16 publications

References 6 publications

Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network

Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network

Online Estimation Techniques for Natural and Excitation Frequencies on MDOF Vibrating Mechanical Systems

Detection of crack in structure using dynamic analysis and artificial neural network

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