Feature extraction of rolling bearing fault signal based on adaptive feature mode decomposition algorithm

Key Points

• Extracting features from rolling bearing fault signals significantly enhances detection accuracy, and reduces detection time.
• The adaptive feature mode decomposition algorithm improves the signal-to-noise ratio, leading to clearer fault identification.
• Analysis includes the application of machine learning techniques on vibration data from bearings, demonstrating effective fault recognition.
• Results highlight the potential for advanced monitoring systems in industrial settings, enhancing predictive maintenance strategies.