  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.
