Remaining useful life prediction of electronic power components based on stacked denoising autoencoders and temporal fusion networks.
TL;DR
A lifespan prediction method for Electronic components based on a stacked denoising autoencoder-temporal fusion transformer (SDAE-TFT) that reduces the mean squared error (MSE) by 34.1%.
Abstract
Electronic components are prone to degradation or even failure due to the influence of environmental factors during their wide application. Accurate prediction of their remaining useful life (RUL) is therefore crucial for implementing early fault warning systems. However, traditional lifespan prediction methods face challenges in simultaneously capturing both short-term fluctuations and long-term trends in the degradation process of devices. To address this issue, this paper proposes a lifespan prediction method for Electronic components based on a stacked denoising autoencoder-temporal fusion transformer (SDAE-TFT). Firstly, 19 features are extracted from raw degradation data, and these features are subjected to dimensionality reduction and fusion through a SDAE to construct a health index (HI). Secondly, a TFT model is built to output the root mean square (RMS) voltage and power characteristics of the device, thereby predicting its degradation trend. In this study, a data acquisition system for DC-DC modules is established to carry out accelerated degradation experiments. The SDAE-TFT model is used to predict the RUL of the modules, and its accuracy is further verified using the NASA public dataset. Comparative experiments show that, compared with existing models, the proposed model reduces the mean squared error (MSE) by 34.1%.