Motif Analysis in Internet of the Things Platform for Wind Turbine Maintenance Management

Ramírez, Isaac Segovia; Urioso, Eduardo Cruz; Peco, Ana María; Kotorov, Rado; Chi, Lianhua; Padhye, Raunaq G.; Bhatia, Amanjeet Singh; Muñoz, Carlos Quiterio Gómez; Márquez, Fausto Pedro Garcı́a

doi:10.1007/978-3-030-79203-9_7

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2022

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“…The industry is currently able to access a growing amount of data, which can have many implications, including scheduling [2], maintenance J. Vives DOI: 10.4236/jcc.2022. 107001 2 Journal of Computer and Communications management [3], and quality improvement [4].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Vives¹

2022

JCC

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Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.

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Section: Introductionmentioning

confidence: 99%