A Review of Modern Maintenance Techniques for Improving Mechanical System Reliability

Abstract

The functional effectiveness of major components usually determines the operational reliability of large mechanical equipment. Therefore, to guarantee the dependability of mechanical equipment, timely maintenance is required before failure. This paper is a critical discussion of the reliable maintenance and care of mechanical systems to achieve the fullest life of equipment and efficiency. It discusses the shift in the past from reactive and preventative maintenance to smart, data-driven predictive and reliability-focused operations that Industry 4.0 technologies provide. The principles of reliability optimization design are discussed, which focus on combining the methods of probabilistic approach and dependability on a system level. Performance comparison is drawn between various maintenance strategies, and common mechanical failures of rotating machines, including bearing, gearbox, and misalignment faults, are discussed in terms of causes and consequences. Also, the paper addresses predictive maintenance and the significance of the Internet of Things (IoT)-based sensor technologies in real-time condition monitoring as part of lifecycle management. The literature review has indicated new trends related to machine learning (ML), deep reinforcement learning, and predictive maintenance optimization models. The paper ends with defining strategic research gaps and showing the necessity of a single, AI- and IoT-based maintenance system that will allow making the industry more reliable, cost-effective, and sustainable.