Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar
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
This article presents an industrial implementation of a virtual sensor in the process of fault detection of an induction motor. An ensemble-learning soft-sensor is developed to detect broken rotor bar that is essential to prevent irreparable damage. Most of the existing diagnostic methods assume that the data distribution is static and that all data is available during the training, while in real applications, the data become available as data streams. The proposed method is inspired by the ensemble learning algorithm, which is combined with a new drift detection mechanism. The advantages of the proposed approach are three-fold. First, a fair comparison with other algorithms show the effectiveness of the soft sensor scheme. Second, the presented concept change detection algorithm is capable of detecting a new class in the data stream as well as data distribution change, and last but not least, the efficacy of the proposed algorithm is demonstrated using benchmark concept drift data streams.
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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 | 0.000 |
| 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