Laser Projector Field Reliability Dashboard: An Effective Tool to Monitor Product Reliability
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Résumé
ABSTRACT While many products are designed and built for specific applications, there are some that are intended to be versatile. Digital projectors are a prime example. Rapid advancements in audio-visual technology have enabled even more diverse applications that were not considered in the original design. As a result, the field performance of a fleet of any model of projectors is highly non-homogenous, making it necessary for field reliability metrics to be capable of narrowing in on homogenous subsets of the population. Being able to quantify and understand the differences in reliability performance between various user profiles and customers, greatly enhances problem-solving and improvement initiatives. Moreover, with product development cycles getting shorter in order to stay competitive in a fast-paced market, the insights gained from a comprehensive analysis of field data are crucial to the success of future designs. Significant resources are being spent on investigating field failures, identifying root causes, and then finding and implementing solutions. Reliability metrics that are sensitive to shifts in appropriately-spaced time intervals are critical to evaluating the effectiveness of solutions and quantifying improvement. Non-parametric graphical models characterizing age dependent behavior, together with parametric modeling, provide a powerful combination of analytical tools for relevant field reliability metrics, for both internal and external stakeholders. Visualization capabilities have improved greatly in recent years, providing accessible tools for creating intuitive, nimble, yet complex dashboards. With careful consideration of the analytical needs of the stakeholders, an effective dashboard that incorporates multilayer drill-down features is a valuable investigative, as well as reporting tool. Two reliability models are described in this paper, having been successfully used in combination to evaluate and monitor the field performance of a fleet of laser projectors spanning four years of service. The non-parametric model of Mean Cumulative Function (MCF) is used to characterize age-based behavior and is displayed as Mean Cumulative Repairs/100 units. The shape and slope of the MCF plot provides a quick visual indication of performance stability over time. The Crow-AMSAA Reliability Growth model is used to quantify reliability improvement and predict future behavior. A case study of comparative analysis is presented, where the non-homogeneous fleet data is sliced in more homogeneous subsets by customer and year of deployment. This is a powerful graphical method of quantifying reliability growth between subsequent designs and highlighting differences between applications. Annualized Failure Rate (AFR) is calculated for each drill-down subset, for each year-in-service, providing another powerful metric of performance over time that complements the graphical models of MCF and Crow-AMSAA.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle