PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Diego Andrés Aichele Figueroa Co-author(s): Lavínia Maria Mendes Araújo - lavinia.mendes@ufpe.br Thais Campos Lucas - thaiscamposlucas@hotmail.com Marcio das Chagas Moura - marcio.cmoura@ufpe.br Isis Didier Lins - isis.lins@ufpe.br Enrique Lopez Droguett - eald@g.ucla.edu

Diagnosis of Failure Modes from Bearing Data via Deep Learning Variational Autoencoder Method
Bearings and gears are indispensable equipment in complex machinery. Many studies developed analyses to improve the effectiveness of predictive maintenance for these components. Thus, Deep Learning (DL) models for the diagnosis and prognosis of equipment failure modes can be highlighted. For this purpose, many applications have used supervised learning methods, such as Support Vector Machine, Multilayer Perceptron, and Convolutional neural networks. However, in practice, labeled data connected to the conditions of real-time systems can be more complex and costly to obtain. In this sense, we highlight unsupervised learning models, where the algorithm discovers by itself through data exploration, the possible relationships between data points. Hence, this paper aims to apply the unsupervised Variational Autoencoder method to diagnose failure modes of bearings and gears. Six databases available in the literature will be used for analyses purposes. Nevertheless, optimization of the model's hyperparameters is aggregated to perform more efficient assessments. Finally, the results will be compared with other methods to validate the model's effectiveness.

Diagnosis of Failure Modes from Bearing Data via Deep Learning Variational Autoencoder Method

Bearings and gears are indispensable equipment in complex machinery. Many studies developed analyses to improve the effectiveness of predictive maintenance for these components. Thus, Deep Learning (DL) models for the diagnosis and prognosis of equipment failure modes can be highlighted. For this purpose, many applications have used supervised learning methods, such as Support Vector Machine, Multilayer Perceptron, and Convolutional neural networks. However, in practice, labeled data connected to the conditions of real-time systems can be more complex and costly to obtain. In this sense, we highlight unsupervised learning models, where the algorithm discovers by itself through data exploration, the possible relationships between data points. Hence, this paper aims to apply the unsupervised Variational Autoencoder method to diagnose failure modes of bearings and gears. Six databases available in the literature will be used for analyses purposes. Nevertheless, optimization of the model's hyperparameters is aggregated to perform more efficient assessments. Finally, the results will be compared with other methods to validate the model's effectiveness.

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

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