Health indicator construction for degradation assessment by embedded LSTM–CNN​ autoencoder and growing self-organized map

Abstract

Health indicator (HI) construction is the most significant task of degradation assessment (DA) that facilitates prognostic and health management of rotating machinery. Many stacked autoencoder (SAE) models represented by CNN-based and RNN-based SAE have been applied to the field of DA. However, the former has a small receptive vision which makes it weak in encoding time-series information, while the latter can easily encounter the problem of overfitting or parameter expansion. To solve these problems, this paper proposes an embedded LSTM–CNN​ autoencoder to extract trend features that contain both local characteristics and degradation trend information from vibration data. And, a transfer learning-based two-phase network training algorithm is designed to enhance the ability of noise filtering of the model. Then, HI is obtained by fusing the extracted trend features with a growing self-organized map. Finally, two case studies are implemented by using bearing datasets to verify the proposed method. The results show that HI gained by the proposed method is more effective than that by other existing methods. Moreover, the goodness-of-fit of polynomial degradation models with the HI is analyzed.