An adaptive remaining useful life prediction model for aeroengine based on multi-angle similarity

Abstract

Similarity-based aeroengine Remaining Useful Life (RUL) prediction methods have long been limited by similarity evaluation rules. Therefore, this article proposes an advanced multi-angle engine similarity evaluation criterion based on the similarity of degradation mechanisms and process data. Firstly, a convolutional autoencoder is used to calculate the Health Index (HI) of the engines. Then, the encoder part is used to ensure that the similarity calculation is performed under the same fault pattern. Dynamic time warping based on sequence length correction is used to compute the global similarity between different HIs, which overcomes the limitations of different length. Finally, Gaussian process regression is used to predict the remaining useful life, and the Akaike information criterion is used to select the similarity threshold of the training data adaptively. This method ensures that the prediction model fully learns the fault evolution mechanism and degradation data characteristics, greatly improving the RUL prediction accuracy.