Evaluasi Distributed Control System pada PLTU dengan Failure Mode, Effect and Criticaly Analysis (FMECA)
Bibliographic record
Abstract
Steam power plants (PLTU) are power plants that have the largest percentage of power plants owned by PLN. The main controller in a PLTU usually uses a Distributed Control System (DCS). The reliability of DCS in a PLTU must be maintained properly so that the power plant does not experience a control failure that causes the PLTU to stop suddenly. DCS PLTU Sebalang Unit 1 has used the FMEA method to determine its maintenance strategy, but the FMEA has not defined the function of the equipment, functional failure, the effect of failure, and criticality analysis (CA) of the equipment. failure mode prioritization has not been carried out. With the FMECA conducted in this study, the priority of the failure mode can be carried out effectively so that the priority determination of DCS maintenance can be carried out. Results of the FMECA that have been carried out, it is known that the failure that occurs in the DCS CPU has the highest RPN value (140). The maintenance strategy obtained from the FMECA results is preventive maintenance (PM), namely: 1) checking the power supply voltage, 2) checking the communication status, idle time status, load and CPU status, and maintenance run to failure (RTF) CPU replacement if there is damage to the CPU. FCS CPUs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".