GraSSNet: Graph Soft Sensing Neural Networks

TL;DR

GraSSNet is a novel graph-based neural network designed for multivariate time-series classification in soft sensing, effectively capturing dependencies, exploiting label correlations, and handling noisy, imbalanced data with semi-supervised learning.

Contribution

Introduces GraSSNet, a graph neural network that jointly models inter- and intra-series dependencies, exploits label correlations, and incorporates semi-supervised learning for soft sensing data.

Findings

Outperforms traditional classifiers on Seagate soft sensing data

Effectively captures complex dependencies in noisy, imbalanced data

Leverages unlabeled data to improve classification accuracy

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.