Sensor Data Acquisition Techniques for Fault Detection in Mechanical Systems: A Review of Accuracy and Timeliness

Abstract

The reliability of mechanical systems in industrial environments relies heavily on accurate and timely fault detection, which is made possible through efficient sensor data acquisition techniques. This review investigates the role of various sensors and acquisition methods in detecting faults in mechanical components, highlighting recent advances and applications. The study categorizes key sensor types such as temperature, motion, proximity, and chemical sensors and explores their roles in real-time monitoring and diagnostics. Further emphasis is placed on modern data acquisition techniques, including synchronization methods, signal preprocessing, and intelligent systems that enhance decision-making accuracy. It also explores the integration of machine learning (ML) and deep learning (DL) models in fault detection frameworks, which improve diagnostic efficiency and reduce dependency on manual inspection. Additionally, the review presents comparative findings from recent open-access studies to evaluate strengths, challenges, and future directions. The importance of accuracy and timeliness is discussed, emphasizing how delayed or incorrect detection can affect industrial productivity and safety. Overall, this review serves as a foundation for researchers and engineers seeking to develop or improve sensor-based fault detection systems in mechanical applications.