A Machine Learning Approach to Pitot Static Error Detection and Airspeed Prediction

Aircraft guidance is dependent on various sensors which provide information on speed, altitude and location with respect to both the ground and the surrounding air. The pitot static system, global positioning system (GPS) and inertial navigation system (INS) are the main sources of information. The pitot static system measures total and static pressures to provide airspeed information. This system includes two ports located outside of the aircraft making them vulnerable to interference and failures. Autonomous aircraft software has not yet been developed to handle failures in this system. If an aircraft has access to redundant data streams, then it can be trained to autonomously recognize errors in the pitot static system and learn to correct them. In this work, we develop a novel machine learning approach to detecting pitot static system failures, identifying types of failures and predicting airspeed with the use of redundant flight data. Two running estimates of airspeed are kept during flight and major discrepancies between the two triggers an error identification system. This identification system computes the autocorrelation of the incoming pressure data to classify the state of the pitot static system. Exploratory dimensionality reduction and feature selection techniques are performed on the redundant data to create a library of selected sensor output from previous flights. This library is used to train a k-nearest neighbors regression model to make online airspeed predictions in the event of a pitot static system failure. We demonstrate our methodology on sample flight data from a four engine commercial jet. Having this fault resistant guidance system for an aircraft makes it possible to remain in flight and continue critical missions

Physics-based machine learning and data-driven reduced-order modeling

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 123-128).This thesis considers the task of learning efficient low-dimensional models for dynamical systems. To be effective in an engineering setting, these models must be predictive -- that is, they must yield reliable predictions for conditions outside the data used to train them. These models must also be able to make predictions that enforce physical constraints. Achieving these tasks is particularly challenging for the case of systems governed by partial differential equations, where generating data (either from high-fidelity simulations or from physical experiments) is expensive. We address this challenge by developing learning approaches that embed physical constraints. We propose two physics-based approaches for generating low-dimensional predictive models. The first leverages the proper orthogonal decomposition (POD) to represent high-dimensional simulation data with a low-dimensional physics-based parameterization in combination with machine learning methods to construct a map from model inputs to POD coefficients. A comparison of four machine learning methods is provided through an application of predicting flow around an airfoil. This framework also provides a way to enforce a number of linear constraints by modifying the data with a particular solution. The results help to highlight the importance of including physics knowledge when learning from small amounts of data. We also apply a data-driven approach to learning the operators of low-dimensional models. This method provides an avenue for constructing low-dimensional models of systems where the operators of discretized governing equations are unknown or too complex, while also having the ability to enforce physical constraints. The methodology is applied to a two-dimensional combustion problem, where discretized model operators are unavailable. The results show that the method is able to accurately make predictions and enforce important physical constraints.by Renee C. Swischuk.S.M.S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Progra