Análise de Relevância de Atributos para Classificação de Falhas de Ferramentas de Corte Utilizando TSFRESH

Matheus A. M. Ferreira, Thiago E. Fernandes, Guilherme P. C. Miranda, Marcos V. G. Silva, Eduardo Pestana de Aguiar

DOI: 10.14209/sbrt.2022.1570823732

Evento: XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2022)

Keywords: Processamento de Sinais Análise de Dados Aprendizagem Computacional

Abstract

The majority of mechanical components went through a machining process during their manufacturing. Therefore, manufacturing processes with inadequate condition tools are likely to induce unexpected operational interruptions, accidents, product quality, and economic losses. Accordingly, the ability to classify fault imminences can result in cost reduction, along with productivity and safety increase. This paper applies digital signal processing and machine learning techniques, to solve this problem. The model proposed in this work achieves satisfactory performances in all cases and prevents fault occurrences.

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