Intelligent Maintenance Scheduling and Decision Support Model for Industry 4.0 System Based on Severity Factors and Priority Weights

Predictive maintenance (PdM) plays a crucial role in ensuring the reliability and efficiency of industrial system and its components. However, existing research often overlooks the critical consideration of uncertainty and variability in scheduling maintenance tasks, leading to sub-optimal decisions. Efficiently planning maintenance tasks is paramount, and this paper ad-dresses these challenges by introducing an algorithm based on several factors. These severity factors are degradation rate, remaining useful life (RUL), criticality, and failure consequences, each contributing to the overall priority of maintenance tasks. This paper presents a comprehensive approach i.e. Intelligent Maintenance Scheduling and Decision Support (IMS&DS) model for predictive maintenance scheduling in industry 4.0. Moreover, it also considers how resources are assigned and the time needed for repair to avoid doing maintenance tasks too early and to handle uncertainties. The proposed method ensures that maintenance schedules are optimized to minimize the risk of component failure while efficiently utilizing available resources. It also demonstrates the applicability of the proposed algorithm through case studies and severity analysis. This complete framework can be used to enhance maintenance planning and can be readily adopted for any field, revolutionizing the asset management paradigm.