SMART SENSORS AND DATA ANALYTICS FOR REAL-TIME MONITORING OF MECHANICAL COMPONENTS

Abstract

Modern industries have mechanical systems that must be monitored continuously and in real-time to guarantee the reliability of mechanical operations, minimize the downtime, and guarantee the improved safety. Other conventional maintenance methods such as reactive/scheduled maintenance check are inadequate in complex industrial environment. This combination of smart sensors, advanced data analytics, and artificial intelligence offers the predictive maintenance capability, early fault detection, and intelligent decision-making capability. This literature review presents a synthesis of the recent trends in the smart sensor technology like vibration, strain, temperature, MEMS, fibre-optic and nanocarbon based sensors, and AI based data analysis methods like machine learning, deep learning, and soft sensor models. The review goes further to discuss techniques of combining sensors with cloud-edge computing, Internet of Things platforms and digital twin frameworks to attain real-time monitoring and automatic maintenance. The main issues such as the inability to combine multi-sensors, low interpretability of AI models, energy-inefficiency, the absence of unified frameworks, and interoperability problems are discussed. The research gaps and future directions in the study include explainable AI, sustainable low-power sensor design, cross-domain validation, and scalable cyber-physical architectures. The review is a good generalization of the state of capabilities, limitations, and opportunities that can be used in the design of intelligent, reliable, and sustainable mechanical monitoring systems by researchers and practitioners in accordance with Industry 4.0 and Industry 5.0 goals.