Hybrid RPI-MCDM Approach for FMEA: A Case Study on Belt Conveyor in Bir El Ater Mine, Algeria
Bibliographic record
Abstract
Failure Modes and Effects Analysis (FMEA) is a widely-used technique for enhancing dependability by ranking failure modes according to their Risk Priority Number (RPN).However, RPN has limitations, such as non-injectivity, non-surjectivity, and difficulties in weighing risk variables.The Risk Prioritization Index (RPI) model offers an alternative, addressing some of these limitations and providing user-friendly prioritization of failure modes.This study proposes an integrated risk assessment model that combines the RPI model with Multiple Criteria Decision-Making (MCDM) methods, specifically the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).The first approach uses entropy, average weight, and scenarios to estimate the impact of risk variables and identify key elements.The second approach combines rankings of failure modes from five RPI models using the integrated MCDM-TOPSIS method.The proposed methods are applied to a case study of a belt conveyor system in a mining company in Bir El Ater, Algeria, demonstrating their effectiveness and dependability.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".