AI-Enhanced Predictive Maintenance in Intelligent Systems for Industries

Abstract

A innovative approach to AI-enhanced predictive maintenance in AI-powered industrial systems is presented here. The suggested technique creates an all-encompassing predictive maintenance framework by fusing three powerful algorithms: Random Forest, Long Short-Term Memory (LSTM) networks, and XGBoost. The goal is to improve equipment failure prediction accuracy while simultaneously optimizing maintenance schedules to facilitate productive and economical manufacturing processes. Random Forest is the first step in the process, and it is responsible for feature selection and the first failure predictions. Using an ensemble learning strategy, Random Forest can efficiently process high-dimensional, complicated information. Following this, long short-term memory (LSTM) networks are presented as a means of capturing temporal relationships in the data, making them well-suited for the investigation of failures in equipment over time. Finally, XGBoost improves predictive accuracy by decreasing the number of incorrect predictions. Each algorithm has its own merits, and by combining them, we may create a more robust framework. The suggested technique excels over the status quo because of its flexibility to adapt to new circumstances, high accuracy in failure prediction, low cost, and high sustainability. It enables real-time data-driven optimization of maintenance plans, cutting down on unplanned downtime and wasted resources. The suggested method for AI-enhanced predictive maintenance is a state-of-the-art solution for businesses, as it replaces outdated methods with cutting-edge AI and machine learning strategies. In addition to improving the accuracy and efficiency of intelligent industrial systems, it also helps create a greener and more sustainable industrial landscape.

Topics & Concepts