Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals

Abstract

Blade tip-timing (BTT) signals are always seriously sub-sampled, so reconstruction is much necessary for vibration analysis. Blade vibration responses are sparse in order domain and classical compressed sensing (CS) algorithms are difficult to reconstruct vibration orders due to lack of prior sparse information under variable speeds. In order to deal with this issue, this paper proposes an end-to-end deep compressed sensing (DCS) method. Firstly, a Multi-coset BTT measurement model is built under variable speeds and the DCS model is derived in order domain, where a specific convolutional neural network (CNN) is designed. Next, a Simulink model is built to generate training and testing samples. The simulation results show that the convolution layer with the ReLU layer placed after the BN layer can improve the reconstruction performance and the proposed method has better reconstruction accuracy and speed than classical CS algorithms. In the end, experiments are done and the results demonstrate that blade vibration orders can be recovered accurately by the proposed method, which will provide a novel way of BTT signal analysis.