Lightweight learning for adaptive predictive maintenance in industrial IOT

Current predictive maintenance methods in IIoT struggle with complex data, limited edge device resources, and real- time adaptation. This paper proposes a novel, lightweight machine learning framework designed specifically for IIoT environments. Our framework utilizes efficient algorithms for resource-constrained devices, enabling seamless integration and potentially reducing processing time and energy consumption. Deploying the framework on edge devices allows for real-time monitoring and decision- making. Rigorous evaluation, including simulations and real-world experiments, aims to quantify the framework's benefits, such as potentially reducing downtime and maintenance costs. Additionally, the research explores techniques like knowledge distillation for further model size reduction and federated learning for collaboration between devices, potentially enhancing adaptability. This framework has the potential to revolutionize IIoT predictive maintenance by offering a cost-effective and adaptable solution for optimized operational efficiency. Keywords: IIOT, Industrial IOT, Lightweight Learning, Federated Learning andPredictive Maintenance.