# Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data

arXiv: https://arxiv.org/abs/2004.06806 · HTML: https://mlsanity.com/paper/2004.06806/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://mlsanity.com/paper/2504.16972/) (2025)
- [RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems](https://mlsanity.com/paper/2510.17396/) (2025)
- [Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals](https://mlsanity.com/paper/2407.18759/) (2024)
- [Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing](https://mlsanity.com/paper/2601.11794/) (2026)
- [F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2607.04698/) (2026)

## Abstract (arXiv)

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 textitblind 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.