Application ofArtificial Neural Networking for Determining the Plane of Vibration in Rotating System

A, Sen; Majumder, M. C.; Mukhopadhyay, Sudipta; Biswas, Robin Kumar; Roy, Subhadeep

doi:10.9790/1684-1401052335

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O monitoramento de condição de máquinas rotativas pode ser realizado através de um vasto conjunto de técnicas invasivas, como análise de óleo e ferrografia, ou não invasivas, como inspeção visual e emissão acústica. Dentre essas e diversas outras técnicas, a análise de vibração, que constitui o monitoramento de condição baseado em vibração (VCM, do inglês Vibration-based Condition Monitoring), tem recebido grande destaque devido aos níveis satisfatórios de precisão que proporciona às tarefas de detecção de falhas (Sen et al, 2017).…”

Section: Lista De Abreviaturas E Siglasunclassified

“…Em contrapartida, possui capacidade de análise limitada, uma vez que não consegue prover informações suficientes a respeito do comportamento dos sinais e de lidar com sinais não estacionários (Lee et al, 2014). Por outro lado, a análise no domínio da frequência permite representar as componentes de frequência que descrevem um sinal, resultando, porém, na perda da sua informação temporal (Sen et al, 2017). Visando resolver ambas as deficiências, foram desenvolvidos métodos para a análise de sinais nos domínios do tempo e da frequência simultaneamente.…”

Section: Técnicas Tradicionaisunclassified

See 1 more Smart Citation

Proposta de arquitetura de autoencoder convolucional assimétrico para a análise de imagens de vibrações

Franco de Godói

View full text Add to dashboard Cite

In the recent years, several industry segments have benefited from the advantages offered by health monitoring of rotating machines. Given the pace at which technological advances have occurred and the operational and economic gains provided by modern computational methods, there is an evident trend to apply artificial intelligence techniques in the maintenance of such equipment. This work addresses the study of artificial neural networks methods applied to the condition monitoring of rotating machines under different learning paradigms. A denoising convolutional autoencoder neural network configuration for the diagnosis of mechanical components is proposed, aiming the main advantages of different supervised and unsupervised architectures to optimize performance in the analysis of images generated from normalized vibration signals. The training of the model is carried out in two distinct phases, a pre-training by an asymmetric autoencoder and a fine-tuning by a classifier network. Through the selection of hyperparameters, experiments based on data extracted from bearings help to determine the structure that offers the most satisfactory results and to validate the generality of the developed method. A comparison between the proposed architecture and other architectures applied for the same purposes, including models proposed in other published works, allows to evaluate its performance in terms of accuracy and robustness to noise. The objective is to assess the effectiveness of the method through the use of two public experimental datasets produced under different machine operating conditions. The results obtained in both case studies justify the options adopted in the definition of the diagnostic system and prove that the proposed configuration offers greater faults classification accuracy and greater robustness in comparison to the other addressed architectures.

show abstract

Section: Lista De Abreviaturas E Siglasunclassified

Section: Técnicas Tradicionaisunclassified

Proposta de arquitetura de autoencoder convolucional assimétrico para a análise de imagens de vibrações

Franco de Godói

View full text Add to dashboard Cite

show abstract

A Hybrid Approach: Rough Set, Artificial Neural Networking & Data Envelopment Analysis on patient's satisfaction in healthcare organization

Saxena

Sharmam²,

Panchal

2022

2022 6th International Conference on Computing, Communication, Control and Automation (ICCUBEA

View full text Add to dashboard Cite

Application ofArtificial Neural Networking for Determining the Plane of Vibration in Rotating System

Cited by 2 publications

References 6 publications

Proposta de arquitetura de autoencoder convolucional assimétrico para a análise de imagens de vibrações

Proposta de arquitetura de autoencoder convolucional assimétrico para a análise de imagens de vibrações

A Hybrid Approach: Rough Set, Artificial Neural Networking & Data Envelopment Analysis on patient's satisfaction in healthcare organization

Contact Info

Product

Resources

About