Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review

Abstract

Rotating machines are extensively utilized in diverse industries, and their malfunctions can result in significant financial consequences and safety risks. Consequently, there has been growing research interest in the intelligent fault diagnosis of rotating machines, particularly through the utilization of multi-sensor condition monitoring data. However, a comprehensive review focusing on multi-sensor data fusion methods is lacking. To bridge this gap, this paper provides a comprehensive analysis of the existing literature on the application of multi-sensor data fusion techniques to diagnose faults in rotating machines. Basic concepts of multi-sensor data fusion are first provided, establishing a robust foundation for subsequent discussions. The review then provides an in-depth analysis of the applications of multi-sensor data fusion in intelligent diagnosis for rotating machines. Furthermore, this review paper highlights the current challenges encountered in multi-sensor data fusion for intelligent fault diagnosis of rotating machines. By considering these challenges and consolidating knowledge from various sources, this paper proposes future research directions in this field. This review article serves as a valuable resource for researchers, practitioners, and decision-makers in the domain of intelligent fault diagnosis of rotating machines. The review provides comprehensive insights into the latest advancements of multi-sensor data fusion techniques and guiding future research directions in the measurement sciences.