MACHINE LEARNING FOR PREDICTIVE IN-FLIGHT ALERTS

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MACHINE LEARNING FOR PREDICTIVE IN-FLIGHT ALERTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240282201 titled 'MACHINE LEARNING FOR PREDICTIVE IN-FLIGHT ALERTS

The present disclosure introduces techniques for machine learning-based anomaly prediction in flight data analysis. An autoencoder machine learning model is used to process the flight data and generate an embedding, which is then used to calculate a reconstruction error. An anomaly measure is produced by processing the embedding and reconstruction error using an anomaly machine learning model. If the anomaly measure meets certain criteria, an alert is triggered.

Flight data is analyzed using machine learning models.
An embedding is generated to represent the flight data.
Reconstruction error is calculated based on the embedding.
Anomaly measure is determined using the embedding and reconstruction error.
An alert is issued if the anomaly measure indicates an anomaly.

Potential Applications: - Aviation industry for predicting anomalies in flight data. - Aerospace engineering for improving aircraft safety. - Data analysis in other industries for anomaly detection.

Problems Solved: - Early detection of anomalies in flight data. - Enhancing safety measures in aviation. - Improving predictive maintenance for aircraft.

Benefits: - Increased safety in air travel. - Cost savings through proactive maintenance. - Enhanced data analysis capabilities.

Commercial Applications: Title: Machine Learning Anomaly Prediction for Flight Data Analysis This technology can be utilized by airlines, aircraft manufacturers, and aviation authorities to enhance safety protocols, optimize maintenance schedules, and improve overall operational efficiency in the aviation industry.

Questions about Machine Learning Anomaly Prediction for Flight Data Analysis: 1. How does this technology contribute to improving aircraft safety?

  This technology helps in early detection of anomalies in flight data, allowing for proactive maintenance and safety measures to be implemented.

2. What are the potential cost-saving benefits for airlines using this technology?

  Airlines can save costs by avoiding unexpected maintenance issues through predictive maintenance based on anomaly prediction in flight data.

Original Abstract Submitted

the present disclosure provides techniques for machine learning-based anomaly prediction. a set of flight data for a flight of an aircraft is accessed, and an embedding is generated by processing the set of flight data using an autoencoder machine learning model. a reconstruction error is generated based on the embedding using the autoencoder machine learning model. an anomaly measure is generated for the set of flight data by processing the embedding and the reconstruction error using an anomaly machine learning model. in response to determining that the anomaly measure satisfies one or more criteria, an alert is output.