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A cost-effective solution for predictive maintenance in industrial pumping systems
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Orientador(es)
Resumo(s)
This paper presents a cost-effective, Internet of Things (IoT)-based solution for predictive maintenance (PdM) in industrial pumping systems. The proposed system integrates custom-built hardware with machine learning (ML) algorithms to monitor and detect anomalies in real-time. The innovation of the system lies in its non-intrusive design, ease of installation, and adaptability to a variety of industrial environments, providing a practical, low-cost alternative to traditional PdM solutions. Detailed discussion is provided on the hardware component selection, which prioritizes affordability without sacrificing performance, as well as the machine learning strategies used for anomaly detection. Preliminary results from laboratory and field testing demonstrate the system’s potential for reducing downtime and maintenance costs, with a focus on extending the application to broader industrial contexts.
Descrição
Palavras-chave
Predictive Maintenance Internet of things Machine learning Industrial monitoring Anomaly detection
Contexto Educativo
Citação
Editora
IEEE
Licença CC
Sem licença CC