The Role of Neural Networks in Improving Predictive Maintenance Across Industries

Abstract

Predictive maintenance has emerged as a critical strategy for enhancing operational efficiency and reducing downtime across various industries. Neural networks, with their ability to model complex, nonlinear relationships in large datasets, have significantly advanced predictive maintenance practices. This research explores the application of different neural network architectures in predictive maintenance, analyzing their effectiveness in diverse industrial settings such as manufacturing, energy, transportation, and aerospace. Through a comprehensive literature review and case study analysis, we identify key neural network models, including feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), that have demonstrated superior performance in predicting equipment failures and optimizing maintenance schedules. Additionally, we examine the challenges associated with data quality, model interpretability, and computational requirements. Our findings highlight the transformative impact of neural networks on predictive maintenance, offering insights into best practices and future research directions to further enhance their applicability and effectiveness across industries.