Trabalho em anais de eventoOpen Access
Bearing fault prognosis based on evolving granular models
MARTINIANO, ALEXANDRE; VITÓRIO, ANDEVALDO; AYRES, FLORINDO; OTANI, MARIO; MEDEIROS, JOSÉ; NUNES, RIVELINO; SILVA, RENAN; VARELA, JERRY; MOURA, DAVIDSON; SANTOS, KENNY; COSTA, JEFERSON; MENDONÇA, RAFAEL; BESSA, IURY
Resumo
Resumo
Predictive maintenance is crucial to ensure operational continuity, serving as one of the pillars of contemporary industry, where sensors and cyber-physical systems enable continuous monitoring of equipment. In this regard, estimating the remaining useful life of equipment based on physical, statistical, or computational intelligence models has become a pressing task. However, traditional methods face limitations, such as high data demand, computational cost, and low interpretability. To overcome these challenges, this research proposes Preditor AutoCloud, a granular, evolving, and autonomous model that provides interpretable predictions, even in scenarios with data scarcity.
Autoria
Autores do grupo (4)
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palavras-chave
Palavras-chave
remaining useful life predictionpredictive maintenanceinterpretable predictiondata scarcity
Status
Published
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Trabalho em anais de evento