  The common practice of quality monitoring in industry relies on manual
inspection well-known to be slow, error-prone and operator-dependent. This
issue raises strong demand for automated real-time quality monitoring developed
from data-driven approaches thus alleviating from operator dependence and
adapting to various process uncertainties. Nonetheless, current approaches do
not take into account the streaming nature of sensory information while relying
heavily on hand-crafted features making them application-specific. This paper
proposes the online quality monitoring methodology developed from recently
developed deep learning algorithms for data streams, Neural Networks with
Dynamically Evolved Capacity (NADINE), namely NADINE++. It features the
integration of 1-D and 2-D convolutional layers to extract natural features of
time-series and visual data streams captured from sensors and cameras of the
injection molding machines from our own project. Real-time experiments have
been conducted where the online quality monitoring task is simulated on the fly
under the prequential test-then-train fashion - the prominent data stream
evaluation protocol. Comparison with the state-of-the-art techniques clearly
exhibits the advantage of NADINE++ with 4.68\% improvement on average for the
quality monitoring task in streaming environments. To support the reproducible
research initiative, codes, results of NADINE++ along with supplementary
materials and injection molding dataset are made available in
\url{https://github.com/ContinualAL/NADINE-IJCNN2021}.
