A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors
Bibliographic record
Abstract
Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. This paper presents the deployment of such an integration on an industrial air compressor unit. This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. To this end, temperature, pressure, and flow rate data were acquired from sensors in contact with the compressor. The observed data were sent to the Structured Query Language database. Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. Afterward, structured data were passed through the model, and if the data exceeded the determined threshold, a warning email was sent to an operator. Adopting the Internet of Things enhances surveillance for specialists, decreasing the failure and damage probabilities. The model achieved 98% accuracy in the Mean Squared Error metric for our regression model. By analyzing the gathered data, the implemented system demonstrates the capabilities to predict potential equipment failures with promising accuracy, facilitating a shift from reactive to proactive maintenance strategies. The findings reveal substantial potential for improvements in maintenance efficiency, equipment uptime, and cost savings.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".