# Autonomous Deep Quality Monitoring in Streaming Environments

arXiv: https://arxiv.org/abs/2106.13955 · HTML: https://mlsanity.com/paper/2106.13955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Greedy Layerwise Learning Can Scale to ImageNet](https://mlsanity.com/paper/1812.11446/) (2018)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning](https://mlsanity.com/paper/1803.02099/) (2018)
- [Automatic Construction of Multi-layer Perceptron Network from Streaming Examples](https://mlsanity.com/paper/1910.03437/) (2019)
- [Transferring Rich Feature Hierarchies for Robust Visual Tracking](https://mlsanity.com/paper/1501.04587/) (2015)
- [Evolving Ensemble Fuzzy Classifier](https://mlsanity.com/paper/1705.06460/) (2017)
- [An Information-Theoretic View for Deep Learning](https://mlsanity.com/paper/1804.09060/) (2018)
- [Online Tool Condition Monitoring Based on Parsimonious Ensemble+](https://mlsanity.com/paper/1711.01843/) (2017)

## Built on by (most cited first, 2 of 2)

- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering](https://mlsanity.com/paper/2402.09167/) (2024)

## Abstract (arXiv)

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 urlhttps://github.com/ContinualAL/NADINE-IJCNN2021.