Design of MIDA, a Web-Based Diagnostic Application for Hydroelectric Generators
Bibliographic record
Abstract
Implementing a cost-effective general maintenance program for generators is of utmost importance at hydro-Quebec. This paper presents the design of MIDA (integrated generator diagnostic methodology), a Web-based application for diagnosing hydroelectric generators. MIDA allows hydro-Quebec maintenance personnel to better establish maintenance priorities based on analysis and trending of data from several diagnostic instruments. This Web-based application represents just the software component of the MIDA project, an eightyear, multi-million dollar research and development project that involved 20 people. The project aimed to develop a simple and rational way to combine measured data from different diagnostic tools in order to determine the general state of each generator. Following the introduction, this paper provides an overview of the MIDA application and its architecture. It then presents the development process, including the evolutionary prototyping methodology which is very well suited to this type of research and development project and the programming language used: MDI, an object-oriented knowledge-based prototyping tool.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".