Fault Diagnosis of Beam-Like Structure Using Modified Fuzzy Technique

Thatoi, Dhirendra Nath; Choudhury, Sasanka; Jena, Prabir Kumar

doi:10.1155/2014/491510

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…They have also analyzed the beam numerically by using ANSYS. In Thatoi, Choudhury, Jena and Kumar (2014), the authors have used modified fuzzy technique to analyze the crack in a fixed-fixed beam. They have also validated the results numerically with ANSYS and also validated through experimental results.…”

Section: Introductionmentioning

confidence: 99%

Predicting crack through a well generalized and optimal tree-based regressor

Choudhury¹,

Thatoi

Hota³

et al. 2019

IJSI

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Purpose To avoid the structural defect, early crack detection is oneof the important aspects in the recent area of research. The purpose of this paper is to detect the crack before its failure by means of its position and severity. Design/methodology/approach This paper uses two trees based regressors, namely, decision tree (DT) regressor and random forest (RF) regressor for their capabilities to adopt different types of parameter and generate simple rules by which the method can predict the crack parameters with better accuracy, making it possible to effectively predict the crack parameters such as its location and depth before failure of the beam. Findings The predicted parameters can be achieved, if the relationship between vibration and crack parameters can be attained. The relationship yields the results of beam natural frequencies, which is used as the input value for the regression techniques. It is observed that the RF regressor predicts the parameters with better accuracy as compared to DT regressor. Originality/value The idea is used the developed regression techniques to identify the crack parameters which are more effective as compared to other developed methods because the alternate name of prediction is called regression. The authors have used DT regressor and RF regressor to achieve the target. In this paper care has been given to the generalization of the model, so that the adaptability of the model can be ensured. The robustness of proposed methods has been verified in support of numerical and experimental analysis.

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Section: Introductionmentioning

confidence: 99%

Predicting crack through a well generalized and optimal tree-based regressor

Choudhury¹,

Thatoi

Hota³

et al. 2019

IJSI

Self Cite

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“…The MFs are defined by the parameters based on the author's experience. Some of the researchers have used genetic algorithms to fuzzy logic together, but in this case the results from the genetic algorithm layer is trained again in the fuzzy logic layer for refinement (Dash and Parhi, 2014), some have used hybridized membership functions to be applied in the fuzzy inference system (Thatoi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements

Sahu

Kumar

Parhi

2017

jtam

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Fuzzy logic has been used in different research fields for more than three decades. It has become a robust method to solve complex and intricate problems which are otherwise difficult to solve by traditional methods. But it still requires some human experience and knowledge. In the present study, an attempt is made to design a hybrid optimization technique for automatic formation of the fuzzy knowledge based rules using an evolutionary algorithm. This hybridization technique has been applied in the field of damage detection and location of cracks in cracked structural elements. In this paper, a robust fault diagnostic tool based on a differential evolution algorithm and fuzzy logic has been proposed. Theoretical and Finite Element analyzes are done to model the crack and to find the effect of the presence of cracks on changes of vibrational characteristic (natural frequencies) of a fixed-fixed beam. The inputs to DEA-FL system are the first three relative natural frequencies, and the outputs from the system are the relative crack depth and relative crack location. For the validation of the results obtained from the proposed method and to check the robustness of the controller, experimental analysis is performed. To find average error rates, the bootstrap method has been adopted.

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“…From the literature survey, it has been found that less work has been done hybridizing CSA and FLS and few of them are applied to the fault detection of cracked structures. Dash and Parhi (2014) and Thatoi et al. (2014) have proposed two different types of hybrid methods using FL for fault diagnosis of beam-like structure.…”

Section: Introductionmentioning

confidence: 99%

Clonal fuzzy intelligent system for fault diagnosis of cracked beam

Parhi

Sahu

2017

International Journal of Damage Mechanics

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Cracks, faults, or damages are serious threat to the current and future performance of the system. For decades, research work is being carried out on the dynamic behavior of the structural elements for fault diagnosis. The modern types of damage detection methods use the dynamic response from the signals of the beams. In this article, a robust fault diagnostic tool based on clonal selection algorithm and fuzzy logic has been proposed. Theoretical and finite element analyses are done to model the crack and to find the effect of the presence of cracks changes on the vibrational characteristic (natural frequencies) of a fixed–fixed beam. The inputs to (Clonal Selection Algorithm-Fuzzy Logic) system are the first three relative natural frequencies and the outputs from the system are the relative crack depth and relative crack location. Experimental validations with the theoretical results have been carried out to check the robustness of the predicted algorithm.

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Fault Diagnosis of Beam-Like Structure Using Modified Fuzzy Technique

Cited by 7 publications

References 25 publications

Predicting crack through a well generalized and optimal tree-based regressor

Predicting crack through a well generalized and optimal tree-based regressor

Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements

Clonal fuzzy intelligent system for fault diagnosis of cracked beam

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