  In an industrial IoT setting, ensuring the quality of sensor data is a must
when data-driven algorithms operate on the upper layers of the control system.
Unfortunately, the common place in industrial facilities is to find sensor time
series heavily corrupted by noise and outliers. In this work, a purely
data-driven self-supervised learning-based approach based on a blind denoising
autoencoder is proposed for real time denoising of industrial sensor data. The
term \textit{blind} stresses that no prior knowledge about the noise is
required for denoising, in contrast to typical denoising autoencoders where
prior knowledge about the noise is required. Blind denoising is achieved by
using a noise contrastive estimation (NCE) regularization on the latent space
of the autoencoder, which not only helps to denoise but also induces a
meaningful and smooth latent space. Experimental evaluation in both a simulated
system and a real industrial process shows that the proposed technique
outperforms classical denoising methods.
