Automated Machine Analysis Using Vibration Features and AI

The patent applies preliminary action by pre-processing vibration data through signal processing techniques (FFT, wavelet transform, envelope analysis) to extract meaningful features before feeding them to the deep learning model. This preprocessing step transforms raw vibration signals into characteristic features that capture machine health information, allowing the model to learn from fewer, more informative data samples rather than requiring large quantities of raw data.

The patent extracts key features from vibration signals using signal processing methods (frequency spectrum, time-frequency analysis, statistical parameters). By taking out and isolating the most discriminative features from the raw vibration data, the system reduces the dimensionality and information redundancy, enabling effective anomaly detection with smaller data sets that contain only the essential diagnostic information.

The patent implements self-service by employing deep learning models that automatically learn diagnostic rules and patterns from vibration data without requiring manual rule configuration. The system autonomously adapts to different machine types and failure modes by training on labeled data, eliminating the need for experts to manually create and maintain complex rule sets for each machine configuration, thereby achieving both simplicity and broad adaptability.

The patent achieves universality by developing a deep learning framework that can be applied across diverse machine types and monitoring scenarios. The same core architecture processes vibration data from different equipment, adapting to various failure modes (imbalance, misalignment, bearing defects, gear faults) through learned features rather than machine-specific rules, enabling a single system to serve multiple functions and machine types.