Industrial IoT for Predictive Maintenance: Addressing Key Research Gaps in Industrial Deployment

Many studies have already been conducted on the replacement of reactive and schedule maintenance with predictive maintenance in the context of the Industrial Internet of Things, and the reported results are very promising. However, after a careful inspection of this literature, several gaps emerged: most of the systems rely on a single sensor, all the data used are laboratory-based, the detection, diagnosis and prognosis phase do not communicate with each other, predictions in deep learning are not easy to explain, deployment is costly for small and medium sized industries, and action-oriented recommendations are still only a concept and not yet a reality. Rather than repeating these gaps, we are proposing an actual framework designed to address each gap. It combines vibration, thermal, acoustic, and oil-condition signals into a single edge sensor, executes detection, diagnosis and prognosis under one single pipeline and avoids needing to involve three individual models, provides explainable AI (XAI) layer to enable the prediction with a reason attached, and is based on low-cost, off-the-shelf hardware to ensure that small industrial users do not get priced out. The paper then describes the working process, the machine learning methods used, a case scenario, and the expected benefits and limitations, and concludes with the next steps towards fully prescriptive maintenance.