In Journal of intelligent manufacturing
This paper proposes an innovative method for time-series prediction in energy-intensive industrial systems characterized by highly dynamic non-linear operations. The proposed method can capture the true distributions of the inputs and outputs of such systems and map these distributions using polygon generation and video-to-video translation techniques. More specifically, the time-series data are represented as polygon streams (videos), then the video-to-video translation is used to transform the input polygon streams into the output ones. This transformation is tuned based on a model trustworthiness metric for optimal video synthesis. Finally, an image processing procedure is used for mapping the output polygon streams back to time-series outputs. The proposed method is based on cycle-consistent generative adversarial networks as an unsupervised approach. This does not need the heavy involvement of the human expert who devotes much effort to labeling the complex industrial data. The performance of the proposed method was validated successfully using a challenging industrial dataset collected from a complex heat exchanger network in a Canadian pulp mill. The results obtained using the proposed method demonstrate better performance than other comparable time-series prediction models. This allows process operators to accurately monitor process key performance indicators (KPIs) and to achieve a more energy-efficient operation.
Elhefnawy Mohamed, Ragab Ahmed, Ouali Mohamed-Salah
2023
Data streams, Deep learning (DL), Generative adversarial network (GAN), Polygon generation, Time-series prediction, Video-to-video translation
