1 research outputs found
Dual Blind Denoising Autoencoders for Industrial Process Data Filtering
In an industrial internet setting, ensuring the trustworthiness of process
data is a must when data-driven algorithms operate in the upper layers of the
control system. Unfortunately, the common place in an industrial setting is to
find time series heavily corrupted by noise and outliers. Typical methods for
cleaning the data include the use of smoothing filters or model-based
observers. In this work, a purely data-driven learning-based approach is
proposed based on a combination of convolutional and recurrent neural networks,
in an auto-encoder configuration. Results show that the proposed technique
outperforms classical methods in both a simulated example and an application
using real process data from an industrial facility.Comment: 9 pages, 12 figure