Convergence Of Artificial Intelligence And Internet Of Things In Predictive Maintenance Systems – A Review
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Abstract
Operations and Maintenance costs have always posed a heavy burden in wind turbines and the main aspects in spending are on unplanned unscheduled breakdowns, repairs and down time costs. Technology enhancements with connectivity between wind farms and operations control center would reduce risk and improve efficiency during maintenance by continuously analysing the data acquired. Digital solutions of industrial internet of things and machine learning have made inroads and are the real game changers with the potential to supervise, predict and prevent catastrophic failures. Generating the insights from the data to understand the wear pattern and to formulate replacement strategies for reducing frequent maintenance costs and to increase the production. This paper shall discuss and review about the prognostics and diagnostics of the wind turbines, machine learning algorithms, identifying their inter-dependency within the subsystems and the available digital solutions for effective handling of data in predictive maintenance schedules.
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