Systematic Product Development of Control and Diagnosis Functionalities
Bibliographic record
Abstract
In the scientific field of systematic product development a wide range of helpful methods, guidelines and tools were generated and published in recent years. Until now little special attention was given to design guidelines aiming at supporting product development engineers to design products that allow and support control or diagnosis functions. The general trend to ubiquitous computing and the first development steps towards cognitive systems as well as a general trend toward higher product safety, reliability and reduced total cost of ownership (TCO) in many engineering fields lead to a higher importance of control and diagnosis. In this paper a first attempt is made to formulate general valid guidelines how products can be developed in order to allow and to achieve effective and efficient control and diagnosis. The guidelines are elucidated on the example of an automated guided vehicle. One main concern of this paper is the integration of control and diagnosis functionalities into the development of complete systems which include mechanical, electrical and electronic subsystems. For the development of such systems the strategies, methods and tools of systematic product development have attracted significant attention during the last decades. Today, the functionality and safety of most products is to a large degree dependent on control and diagnosis functionalities. Still, there is comparatively little research concentrating on the integration of the development of these functionalities into the overall product development processes. The paper starts with a background describing Systematic Product Development. The second section deals with the product development of the sample product. The third part clarifies the notions monitoring, control and diagnosis. The following parts summarize some insights and formulate first hypotheses concerning control and diagnosis in Systematic Product Development.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".