Estimating in-flight engine performance

An emerging approach in the field of aircraft engine controls and health management is the inclusion of real-time, on-board models for the in-flight estimation of engine performance variations

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In-flight estimation of unmeasurable turbofan engine outputs, such as thrust, is difficult because the values depend on the degradation level of the engine, which is often not known accurately. Degradation is generally defined in terms of off-nominal values of health parameters, such as efficiency and flow capacity, related to each major engine component. While these engines have many sensors, there are typically fewer sensors than health parameters, making accurate estimation impossible. In such situations where known values are fewer than the unknown, Kalman filtering can be employed. The filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone.  This is done by estimating a joint probability distribution over the variables for each timeframe. When a Kalman filter is used to estimate the subset of engine health parameters, the estimates of measured outputs will usually be good, but good estimation of sensed outputs does not guarantee that the estimation of unmeasured outputs will be accurate.

A new approach developed by the Army is to derive a set of tuning parameters (not necessarily a subset of health parameters) that is smaller in dimension than the set of health parameters, but retains as much information as possible from that original set. The solution to both of these minimization problems is obtained using singular value decomposition (SVD). The SVD equation captures the overall effect of the larger set of health parameters on the engine variables as closely as possible.

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