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Dataset of audio signals from brushless DC motors for predictive maintenance.
Prieto Estacio, Rommel Stiward; Bravo Montenegro, Diego Alberto; Rengifo Rodas, Carlos Felipe.
Afiliação
  • Prieto Estacio RS; Universidad del Cauca, Calle 5 Nro. 4-70, Popayán (Cauca), Colombia.
  • Bravo Montenegro DA; Universidad del Cauca, Calle 5 Nro. 4-70, Popayán (Cauca), Colombia.
  • Rengifo Rodas CF; Universidad del Cauca, Calle 5 Nro. 4-70, Popayán (Cauca), Colombia.
Data Brief ; 50: 109569, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37780463
Predictive Maintenance (PdM) has a main role in the Fourth Industrial Revolution; its goal is to design models that can safely detect failure in systems before they fail, aiming to reduce financial, environmental, and operational costs. A brushless DC (BLDC) electric motors have increasingly become more popular and been gaining popularity in industrial applications, so their analysis for PdM applications is only a natural progression; audio analysis proves to be a useful method to achieve this and rises as a very pragmatic case of study of the characteristics of the motors. The main goal of this paper is to showcase sound-based behavior of BLDC motors in different failure modes as result of an experiment led by researchers at Universidad del Cauca in Colombia. This dataset may provide researchers with useful information regarding signal processing and the development of Machine Learning applications that would achieve an improvement within Predictive Maintenance and I4.0.Predictive Maintenance (PdM) has a main role in the Fourth Industrial Revolution; its goal is to design models that can safely detect failure in systems before they fail, aiming to reduce financial, environmental, and operational costs. A brushless DC (BLDC) electric motors have increasingly become more popular and been gaining popularity in industrial applications, so their analysis for PdM applications is only a natural progression; audio analysis proves to be a useful method to achieve this and rises as a very pragmatic case of study of the characteristics of the motors. The main goal of this paper is to showcase sound-based behavior of BLDC motors in different failure modes as result of an experiment led by researchers at Universidad del Cauca in Colombia. This dataset may provide researchers with useful information regarding signal processing and the development of Machine Learning applications that would achieve an improvement within Predictive Maintenance and I4.0.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Colômbia País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Colômbia País de publicação: Holanda