Steady-State Fault Detection with Full-Flight Data

Abstract

Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight.

Determining Steady-State Operation Criteria Using Transient Performance Modelling and Steady-State Diagnostics

“…When a robust diagnostic approach is needed, the data that are to be fed to a gas path diagnostic procedure need to have been obtained from steady-state operation. This is a well-recognized fact, as can be concluded from publications originating from the personnel of engine manufacturers [6][7][8]. A question that arises then is: how is it ensured that this requirement is fulfilled at the operating point the data come from?…”

“…The fact that steady-state operation constitutes the basis for reliable diagnosis using flight data has led the efforts aiming to characterize states as "quasi steady-state", as reported in [6,7]. The early method introduced in [10] was used as the basis for the very recent (2022) work of [8]. Excellent reviews on the usefulness of gas turbine steady-state detection and methods, developed until recently, are presented in [8,16].…”

“…The early method introduced in [10] was used as the basis for the very recent (2022) work of [8]. Excellent reviews on the usefulness of gas turbine steady-state detection and methods, developed until recently, are presented in [8,16].…”

Construction of Data-Driven Performance Digital Twin for a Real-World Gas Turbine Anomaly Detection Considering Uncertainty

“…Currently, most airlines adopted Quick Access Recorders (QAR) for data acquisition, providing flight data continuously sampled at frequencies of 1 Hz and more, which is also referred to as full-flight data covering the whole flight. The availability of these data obtained from a large variety of sensors enables the introduction of new methodologies to assess engine condition, which offers the chance to detect engine faults within one flight more reliably to support more efficient in-service operations and maintenance decisions [ 6 , 7 ]. An approach for fault detection based on steady-state flight regimes of full-flight data is demonstrated in [ 8 , 9 ].…”

“…An approach for fault detection based on steady-state flight regimes of full-flight data is demonstrated in [ 8 , 9 ]. Weiss et al proposed a steady-state fault detection framework with complete flight data using a one-class support vector machine, and high detection rates are demonstrated for various gas path component faults using synthesized datasets derived from full-flight data of commercially operated flights [ 6 ]. Hartwell et al propose a practical and computationally inexpensive method for in-flight real-time anomaly detection based on a convolutional neural network.…”