Remaining useful life (RUL) is the expected remaining operating life of an asset until it can no longer perform its intended function. The 2021 PHM Society Data Challenge posed the problem of estimating the RUL of aircraft engines with various competing failure modes and underlying degradation trajectories. In this work, we describe the approach and solution to the challenge where we map from the multivariate time series sensor readings to the remaining useful life, measured in the remaining number of flight cycles until failure. The proposed solution utilizes a deep convolutional neural network that can take inputs of variable length. Furthermore, we preprocess the data according to a normalization procedure that help reveal the degradation trend that is obfuscated by the continuously varying flight conditions. The normalization procedure involves training a feedforward neural network on a non-degraded subset of the data that maps from the flight conditions to the sensor outputs. The difference between the expected sensor readings and the actual observations is then interpreted as the extent of deviation from normal, i.e., degradation. Finally, we describe the sampling techniques which is designed to reduce the number of non-informative samples fed to the neural network.
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remaining useful life, data challenge, prognostics
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