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