A Dataset for Training the Denoising Approach Based on CEEMDAN-nlDAE

To address the challenges of complex noise during data acquisition in real shop-floor environments, we propose a denoising approach that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a noise-learning-based denoising autoencoder (nlDAE). Gauge block measurements are used to train the denoising model.This dataset provides the data used to train the CEEMDAN-nlDAE model in this study, including data of simulation experiment and gauge block measurement.