APPLICATIONS OF PREDICTIVE MAINTENANCE IN MECHANICAL ENGINEERING: TECHNOLOGIES, BENEFITS, AND CHALLENGES IN MODERN INDUSTRY

Abstract

Predictive maintenance has become one of the main strategies of Industry 4.0, promoting significant advances in operational reliability, cost reduction, and optimization of industrial processes. In this context, the present study aimed to analyze, through a systematic literature review, the main applications of predictive maintenance in Mechanical Engineering, identifying the technologies used, operational benefits, and implementation challenges in modern industry. The research was conducted according to the PRISMA protocol using the Scopus, Web of Science, ScienceDirect, Springer, and Google Scholar databases. Inclusion and exclusion criteria related to publication period, language, full-text availability, and thematic adherence to Mechanical Engineering and industrial applications were adopted. The results demonstrated the extensive use of technologies such as the Internet of Things (IoT), artificial intelligence, smart sensors, machine learning, and Deep Learning in the continuous monitoring of industrial equipment. The main benefits identified include reduction of unexpected failures, downtime minimization, increased asset lifespan, and improved operational planning. However, challenges related to implementation costs, technological infrastructure, system integration, and shortage of qualified professionals were also identified. Furthermore, future trends such as prescriptive maintenance, digital twins, edge computing, and generative artificial intelligence indicate significant advances toward intelligent automation of industrial maintenance. It is concluded that predictive maintenance represents an important technological pillar of contemporary Mechanical Engineering, directly contributing to greater efficiency, competitiveness, and sustainability of industrial production systems.