Aircraft Engine Sensor Analysis for Real-Time Anomaly Detection

The patent replaces manual mechanical analysis with an automated computer-based system that uses machine learning algorithms (specifically autoencoders) to analyze sensor data. This substitution enables real-time anomaly detection without the time constraints of manual review while maintaining or improving detection accuracy through sophisticated pattern recognition capabilities.

The patent introduces an intermediary layer consisting of autoencoder neural networks that learn normal sensor correlation patterns and automatically detect deviations. This intermediary system processes data between raw sensors and human operators, providing pre-analyzed anomaly information that speeds up response time while preserving detection accuracy.

The patent merges data from multiple sensors and combines individual sensor analysis with cross-sensor correlation analysis. The autoencoder model learns relationships between multiple sensors simultaneously, enabling detection of anomalies that manifest as subtle correlation changes rather than individual sensor deviations, thus improving reliability while maintaining operational simplicity.

The patent transforms the detection approach by changing from fixed threshold parameters to dynamic learned parameters. The autoencoder model learns optimal detection parameters from training data, adapting to normal operating patterns and enabling reliable anomaly detection that accounts for varying operational conditions without requiring manual threshold adjustment.

The patent segments the complex analysis task into distinct functional components: data collection from multiple sensors, normalization and preprocessing, autoencoder-based anomaly detection, and result visualization. This segmentation allows each component to be optimized independently and simplifies the overall system architecture, making comprehensive real-time monitoring manageable despite the large number of sensors involved.

The patent uses autoencoders to create compressed representations (copies) of normal sensor data patterns. During training, the model learns to copy normal operating patterns and store them in an encoded form. During operation, actual sensor data is compared against these learned copies, enabling comprehensive monitoring with reduced computational complexity by working with compressed representations rather than raw high-dimensional data.