Role of Empirical Mode Decomposition in Fault Diagnosis of Industrial Components

Background: Rotating machinery is critical in industrial applications but prone to malfunctions due to harsh operating conditions. Signal processing techniques, especially Empirical Mode Decomposition (EMD), are effective for fault detection and diagnosis. EMD has been widely researched and applied. Purpose: This study aims to provide a comprehensive review of the latest developments in EMD applications for rotating machinery fault diagnosis. It serves as a valuable resource for researchers in this area and helps to identify potential future research directions. Methods: The review begins with a brief introduction to EMD, highlighting its benefits, and addressing common issues with the proposed solutions. It then summarizes recent EMD applications in fault diagnosis for critical components, such as bearings, gears, and rotors. Conclusion: The paper concludes by discussing unresolved challenges and suggesting avenues for future research. This review is designed to serve as both an introduction to EMD for newcomers and a state-of-the-art summary for experienced researchers in the field of fault diagnosis.