Using the Validated FMEA to Update Trouble Shooting Manuals: a Case Study of APU TSM Revision
Bibliographic record
Abstract
Trouble Shooting Manuals (TSMs) provide useful information and guidelines for machinery maintenance, in particular, for fault isolation given a failure mode. TSMs produced by OEMs are usually updated based on feedback or requests from end users. Performing such update is very demanding as it requires collecting information from maintenance practices and integrating the new findings into the troubleshooting procedures. The process is also not fully reliable as some uncertainty could be introduced when collecting user information. In this report, we propose to update or enhance TSM by using validated FMEA (Failure Mode and Effects Analysis), which is a standard method to characterize product and process problems. The proposed approach includes two steps. First, we validate key FMEA parameters such as Failure Rate and Failure Mode Probability through an automated analysis of historical maintenance and operational data. Then, we update the TSM using information from the validated FMEA. Preliminary results from the application of the proposed approach to update the TSM for a commercial APU suggest that the revised TSM provides more accurate information and reliable procedures for fault isolation.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".