Integrated Deep Learning and Statistical Process Control for Online Monitoring of Manufacturing Processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advancements in online sensing technologies and wireless networking has reshaped the competitive landscape of manufacturing systems, leading to exponential growth of data. Among various data types, high-dimensional data sources such as images and videos play an important role in process monitoring. Efficient utilization of such sources can help systems reach high accuracy in fault diagnosis. On the other hand, while the researches on statistical process control (SPC) tools are tremendous, the application of SPC tools considering high-dimensional data sets has received less attention due to their complexity. In this paper, we try to address this gap by designing and developing a hybrid model based on deep learning (DL) and SPC models to monitor the manufacturing process in presence of high-dimensional data. In particular, we first apply a Fast Region-based Convolutional Network method referred to Fast R-CNN in order to monitor the image sequences over time. Then, some statistical features are derived and plotted on the multivariate exponentially weighted moving average (EWMA) control chart. The effectiveness of proposed hybrid model is illustrated through a numerical example.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it