Machine Fault Diagnosis Techniques

This cluster of papers focuses on machine fault diagnosis and prognostics using methods such as Empirical Mode Decomposition, wavelet transform, and deep learning. It covers topics like condition monitoring, vibration analysis, and remaining useful life estimation for rotating machinery. The research explores the application of machine learning techniques, neural networks, and signal processing in fault detection and health management of various mechanical systems.

How has Machine Fault Diagnosis Techniques's publication output changed over time?

Output grew0% over the shown period — from 1 works in 2006 to 1 in 2023.

What are the most-cited papers on Machine Fault Diagnosis Techniques?

Where is Machine Fault Diagnosis Techniques research published, and who funds it?

How much of the research on Machine Fault Diagnosis Techniques is open access?