Leveraging Directed Acyclic Graphs for Advanced Ensemble Learning in Predictive Maintenance for Industry 4.0

In Industry 4.0, Predictive maintenance is important because it helps to minimize operating time wastages due to equipment failures. This paper proposes a novel approach using Directed Acyclic Graphs (DAGs) to ensemble multiple machine learning models, enabling more robust and accurate failure predictions. We demonstrate how DAGs can be used to dynamically structure the flow of forecasts, with models feeding into each other to form a sophisticated prediction pipeline. This approach allows multiple complex model interactions, improving maintenance scheduling and anomaly detection in industrial settings. A comparative analysis of DAG-based ensemble learning is presented against traditional methods such as bagging and boosting.