Machine Learning for Predictive Maintenance in Network Systems

Abstract

The increasing complexity and criticality of network arrangements in industries such as telecommunications, production, and IoT have created maintenance a important challenge. Predictive maintenance (PdM) offers a resolution by utilizing machine learning (ML) methods to forecast potential deteriorations and optimize perpetuation schedules, with reducing free time and functional costs. This item explores the request of machine learning algorithms in predicting maintenance for network orders. We consider various machine learning models, such as decision trees, support vector machines (SVM), and deep learning, and their influence in identifying patterns and concluding failures within network foundation. Through a inclusive review of existent literature and original-realm case studies, we climax the challenges and opportunities guide executing PdM in network systems. Furthermore, we present a framework for merging machine learning models with existent network administration arrangements to enhance the veracity and efficiency of fault detection and maintenance planning. The findings display that machine learning-located predictive sustenance can considerably correct operational dependability, minimize resource usage, and longer the old age of network components. This article aims to support valuable insights into the future of network maintenance, advancing the acceptance of data-compelled resolutions for more adept and cost-effective network management.