Name
Abstract | ||
|---|---|---|
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BigData52589.2021.9671903 | IEEE BigData |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Huang | 1 | 56 | 19.96 |
| Zhang Chao | 2 | 87 | 24.26 |
| Jaswanth Yella | 3 | 0 | 2.03 |
| Sergei Petrov | 4 | 0 | 1.35 |
| Xiaoye Qian | 5 | 0 | 1.69 |
| Yufei Tang | 6 | 203 | 22.83 |
| Xingquan Zhu | 7 | 3086 | 181.95 |
| Sthitie Bom | 8 | 0 | 2.37 |