Condition Monitoring of Robust Damage of Cantilever Shaft Using Experimental and Adaptive Neuro-fuzzy Inference System (ANFIS)

Das, Sandeep Kumar; Nayak, Biswajit; Sarangi, Saroj Kumar; Biswal, Dillip Kumar

doi:10.1016/j.proeng.2016.05.140

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…The ANFIS is an integrated machine-learning technique of artificial neuro-network and fuzzy logic, which has exceptional capability in analysing non-linear fatigue data and offers additional reasoning power to the user to understand the behaviour of input parameters. The ANFIS modelling technique has been successfully applied to model highly non-linear fatigue data [ 35 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

et al. 2023

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This study proposed wavelet-based approaches to characterise random vibration road excitations for durability prediction of coil springs. Conventional strain-life approaches require long computational time, while the accuracy of the vibration fatigue methods is unsatisfactory. It is therefore a necessity to establish an accurate fatigue life prediction model based on vibrational features. Wavelet-based methods were applied to determine the low-frequency energy and multifractality of road excitations. Strain-life models were applied for fatigue life evaluation from strain histories. ANFIS modelling was subsequently adopted to associate the vibration features with the fatigue life of coil springs. Results showed that the proposed wavelet-based methods were effective to determine the signal energy and multifractality of vibration signals. The established vibration-based models showed good fatigue life conservativity with a data survivability of more than 90%. The highest Pearson coefficient of 0.955 associated with the lowest RMSE of 0.660 was obtained by the Morrow-based model. It is suggested that the low-frequency energy and multifractality of the vibration signals can be used as fatigue-related features in life predictions of coil springs under random loading. Finally, the proposed model is an acceptable fatigue life prediction method based on vibration features, and it can reduce the dependency on strain data measurement.

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Section: Methodsmentioning

confidence: 99%

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

et al. 2023

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“…Sandeep Das et al [24] investigated the effect of an open crack on the particular parameters of the cantilever beam subjected to free vibration. Adaptive Neuro-fuzzy Inference System (ANFIS) is a delicate processing method, appropriate for non-quick, boisterous and complicated issues like a shortcoming.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Method for Identification of Cracks in Different Beams using Fuzzy with Elephant Based Neural Network

Pitchaiah*,

Rao

2019

IJITEE

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Failure of Structures i.e., beams can be avoided by identifying the damage in the structure at its beginning and proper retrofitting. Recently, the researchers created a structure to recognize crack damage using a cracked beam component model that originates from the fracture mechanics and local flexibility rules. The present work exhibits the analysis of cracked beam with a machine learning model to assess the stiffness of the structure. Here Fuzzy Optimal Neural Network (FONN) is considered, in addition, the stiffness reduction technique, especially concerning thick beams, is featured with a survey of other crack models. The extricated model data are utilized to conversely recognize the cracks with the cracked beam component model through a model updating technique. The optimal Neural Network based stiffness computation utilizes a global searching procedure using Adaptive Elephant Herding Optimization (AEHO) to identify the number of cracks in various beams. From the proposed model, the attained results are compared with the existing research work, and other optimization and machine learning models.

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“…Condition monitoring and damage identification of a beam using both ANFIS and experimental modal analyses were investigated by Das et al [24]. In their study, a cantilever beam, which acted as a structural member, was considered and the modal parameters were measured for both undamaged and damaged states.…”

Section: Introductionmentioning

confidence: 99%

Implementing Fuzzy-Based Artificial Intelligence Approach for Location of Damage in Structures

Hakim¹,

Ibrahim²,

Mohammadhassani³

et al. 2022

cea

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Condition Monitoring of Robust Damage of Cantilever Shaft Using Experimental and Adaptive Neuro-fuzzy Inference System (ANFIS)

Cited by 11 publications

References 16 publications

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

Optimal Method for Identification of Cracks in Different Beams using Fuzzy with Elephant Based Neural Network

Implementing Fuzzy-Based Artificial Intelligence Approach for Location of Damage in Structures

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