Performance evaluation of MVI for fault detection in automated assembly machines
Bibliographic record
Abstract
Automated assembly machines are designed to be operated round-the-clock to maintain high production rates. Continuous operation of an assembly machine results in wear of its various mechanisms that in turn lead to machine faults and subsequent machine downtime. The goal of this research project is to develop and validate a Machine Vision Inspection (MVI) system to detect and classify multiple faults using a single camera as a sensor. The results are experimental in nature, with an industrial O-ring assembly machine used for test purposes. The machine places O-rings on to continuously moving plastic carriers at a rate of over 100 assemblies per minute. The machine is controlled by a PLC with an HMI used to introduce the controlled faults such as transfer track jams, air knife jams, and hopper supply failures. Three MVI methods based on computer vision techniques are developed for this application: 1) Gaussian Mixture Models with blob analysis, 2) optical flow and 3) running average. In order to provide a measure of performance that can be used to better differentiate relative performance, a novel Machine Vision Performance Index (MVPI) was developed. The MVPI is based on five measures of performance: accuracy, processing time, speed of response, robustness against noise, and ease of tuning. The MVPI for the three MVI methods is reported and the significance of the results is discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".