Contrastive blind denoising autoencoder for real time denoising of industrial IoT sensor data

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. This work propose...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Engineering applications of artificial intelligence Ročník 120; s. 105838
Hlavní autoři:	Langarica, Saúl, Núñez, Felipe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2023
Témata:	Autoencoders Cyber–physical systems Noise contrastive estimation Self-supervised learning Cyber–physical systems Noise contrastive estimation Autoencoders Self-supervised learning
ISSN:	0952-1976, 1873-6769
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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. This work proposes a purely data-driven self-supervised learning-based approach, based on a blind denoising autoencoder, for real time denoising of industrial sensor data. The term blind stresses that no prior knowledge about the noise is required for denoising, in contrast to typical denoising autoencoders. 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 that can be exploited in other downstream tasks. Experimental evaluation in both a simulated system and a real industrial process shows that the proposed technique outperforms classical denoising methods. •Novel learning-based technique for time-series denoising.•Denoising is performed without prior knowledge of the noise characteristics.•Latent space of the network can be used for tasks as fault detection or control.•Comparison with other methods shows superiority of the proposed technique.
ISSN:	0952-1976 1873-6769
DOI:	10.1016/j.engappai.2023.105838