Artificial Intelligence Applications in Predictive Maintenance for Sustainable Logistics

The Synthetic intelligence (AI) software in Predictive Maintenance (PdM) transforms manufacturing, imparting solutions to lessen protection charges and promote sustainable logistics. By forecasting system failures, PdM allows maintenance to be scheduled proactively, keeping off the constraints of reactive and preventive techniques. This paper presents a sensible PdM version using statistics cleansing, normalization, most reliable feature selection, and prediction community selection-making. A hybrid Jaya and Sea Lion Optimization (SLnO) set of rules complements function selection, whilst an aid vector machine (SVM) aids in prediction community selection. Recurrent Neural Networks (RNN), optimized utilizing J-SLnO, carry out the very last predictions. The version is confirmed using plane engine and lithium-ion battery datasets, showcasing improved preservation planning and sustainability accuracy.