Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine

The existing fault prognosis techniques of aeroengine mostly focus on a single monitoring parameter under stable condition, and have low adaptability to new prognosis scenes. To boost the fault prognosis capability cross aeroengines, modified deep autoencoder (MDAE) driven by multi-source parameters is proposed in this article. First, the sensitive multi-source parameters are selected and fused using linear local tangent space alignment to define a fused health index (FHI) to characterize performance degradation of aeroengine. Second, MDAE model is constructed with adaptive Morlet wavelet to flexibly establish accurate mapping hidden in the FHI under analysis. Third, parameter transfer learning is used to provide good initial parameters for enabling the constructed MDAE to have cross-domain fault prognosis capability. The proposed method is used to analyze both the simulation multisource performance degradation parameters of aeroengines (system level) and experiment run-to-failure bearing datasets (component level). The results confirm the feasibility of the proposed method in cross-domain fault prognosis of aeroengines, which outperforms the existing methods.

Automated summary

This article proposes a modified deep autoencoder method driven by multi-source parameters for fault prognosis of aeroengines. The method utilizes a fused health index to characterize performance degradation and establishes accurate mapping hidden in the health index using adaptive Morlet wavelet. Parameter transfer learning is used to enable the model to have cross-domain fault prognosis capability.