A Review of Sensor Data Acquisition Methods for Accurate and Timely Detection of Faults in Mechanical Systems

Abstract

The fault detection of mechanical systems is essential to the reliability of such systems in the industrial environment and it is enabled by the efficient method of sensor data acquisition. This review research studies the importance of different sensors and acquisition techniques to detect defects of mechanical parts with reference to current developments and applications. The paper identifies some of the important sensor types including temperature, motion, proximity, and chemical sensors and discusses the application of these sensors in real-time monitoring and diagnostics. More emphasis is put on the contemporary techniques of data acquisition such as synchronization, signal preprocessing, and smart systems, which are able to improve the accuracy of decision-making. It also examines how machine learning and deep learning models can be integrated into fault detection models that enhance efficiency and minimize the need to rely on manual inspection in diagnostic procedures. The review also provides the comparative results of recent open-access research to assess the strengths, issues, and opportunities of the future. The significance of the accuracy and timeliness is described with references to the impact of the late or wrongful detection on the productivity and safety of industry. In general, the review can be considered as a reference point for researchers and engineers who are interested in creating or enhancing sensor-based fault detection systems in mechanical systems.