A Comprehensive Time Series Forecasting for Motor Winding Temperatures
Bibliographic record
Abstract
Predictive maintenance of electric motors in large refineries is a challenge. Electric motors supply the power to key process equipment such as pumps and compressors, and their failure can lead to large financial losses. As such, it is essential to predict future condition of these motors in advance. The failure of the electric motors has been linked to some key parameters such as weather-related factors (i.e., ambient temperature, precipitation level, etc.), a motor’s bearing and winding temperatures, as well as motor’s operating current, for which telemetry data can be collected on a minute level.This work studies and applies use of classic time series techniques such as statistical models as well as machine learning solutions, post intensive data engineering and exploratory data analytics. Several different algorithms were studied and deployed to address both data engineering and data science scope of the work in an automated fashion to be implemented in machine learning operationalization. Eventually, a hybrid model (combined statistics and machine learning) was developed, tested and deployed on several electric motors with versatile attributes, and the models showed very good precision on the hold-out test data, with a long predictive power.Selected models are used to generate long term (up to six months) forecast for the average daily highest winding temperature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".