Machine Fault Diagnosis Techniques

Machine Fault Diagnosis Techniques is a research topic within Control and Systems Engineering. Science Explorer counts 31k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

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.

• Empirical Mode Decomposition
• Fault Diagnosis
• Machine Learning
• Condition Monitoring
• Vibration Analysis
• Deep Learning
• Remaining Useful Life Estimation
• Wavelet Transform
• Rotating Machinery
• Neural Networks