Autonomous Deep Quality Monitoring in Streaming Environments
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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 <https://github.com/ContinualAL/NADINE-IJCNN2021>.
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