Distributed IoT-Based Predictive Maintenance Framework for Solar Panels Using Cloud Machine Learning in Industry 4.0

Abstract

Renewable energy systems in the Industry 4.0 era have maintenance and production maximization as their central element, depending on the type of source. For solar panels, achieving these goals requires periodic cleaning of dust deposits. This research integrates the detection of dust particles on solar panels using classification models based on machine learning models integrated into the Azure platform. However, the main contribution of the work does not lie in the development or improvement of a classification model, but in the design and implementation of an Internet of Things (IoT) hardware–software infrastructure that integrates these models into a complete predictive maintenance workflow for photovoltaic parks. The second objective focuses on how the identification of dust particles further generates alerts through a centralized platform that meets the needs of Industry 4.0. The methodology involves analyzing how the Azure Custom Vision tool is suitable for solving such a problem, while also focusing on how the resulting system allows for integration into an industrial workflow, providing real-time alerts when excessive dust is generated on the panels. The paper fits within the theme of the Special Issue by combining digital technologies from Industry 4.0 with sustainability goals. The novelty of this work lies in the proposed architecture, which, unlike traditional IoT approaches where the decision is centralized at the level of a single application, the authors propose a distributed logic where the local processing unit (Raspberry Pi) makes the decision to trigger cleaning based on the response received from the cloud infrastructure. This decentralization is directly reflected in the reduction in operational costs, given that the process is not a rapid one that requires a high speed of reaction from the system.