Artificial Intelligence Application for Just in Time Maintenance
Bibliographic record
Abstract
Abstract In the present digital era, artificial intelligence (AI) backed decision-process can transform the way asset-failures could be managed. A Proof-of-concept of scalable AI algorithm has been developed and field-verified, to demonstrate potential to implement "Just-in-time" (JIT) maintenance. The model has ability to absorb configuration variations; it is run on periodic basis, to reassess the findings, and identify changes in the operating behavior of the asset. It is a robust tool, for the field engineers. The objective of this research paper is to establish fundamentally different thought process for maintenance decision-makers for dynamic-diagnosis of faults using normal operating data. Each category of equipment has unique behavior, and hence must have customized solution. When linked through IIoT, the automated business decision for maintenance cost reduction can be applied on mass scale. The experimental results & field validations show that AI based diagnostics methodology outperforms traditional maintenance cost-management practices. It is a system which is self-learning, and therefore, as the model matures, dynamic strategy will evolve; and maintenance cost saving can extend beyond targeted 4% of net operating cost. The concept can be equally applied for any data-intensive-complex machineries and static-assets (Heat exchanger, Pipeline, Rotating equipment, etc.) across process industries.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".