Collecting and processing dependability-related information in car building companies
Notice bibliographique
Résumé
Passenger cars are complex technical products. They consist of units, assemblies, and components that are characterized by a certain combination of interacting parts. Additionally, modern passenger cars feature significant numbers of automatic subsystems and automated components: air conditioning, electric heating systems, lighting systems, compartment doors, exterior doors, etc. The process of collecting data on the technical condition of products is to ensure the regularity, reliability, timeliness, and completeness of information. It is known that products most clearly manifest their quality and dependability in operation. A competent organisation of the collection and processing of information on a product’s dependability allows obtaining reliable information on its health and performance. In the course of operation, the connections between individual units and components of passenger cars may become disrupted, the fasteners of individual parts and sensors may become loose, rubber seals may become naturally worn. All of that causes performance decline, as well as malfunctions and failures. Preventing a sharp increase in the number of failures requires performing a number of preventive actions aimed at identifying and eliminating faults, as well as preventing their root causes. First and foremost, such measures include rolling stock maintenance and overhaul. All such activities are strictly regulated in the operating manuals of both a car and its components. The specified life of passenger cars, as well as their components and units, varies roughly from 20 to 40 years. Some components of passenger cars have been in production with no major modifications since the early 2000s. That suggests that a product’s dependability can be evaluated comprehensively throughout the entire life cycle. But that can only be done by collecting and processing a significant amount of information on malfunctions obtained both during the warranty and post-warranty periods. The information is to come from various sources, i.e., operating companies, service depots, car repair plants that carry out overhauls. This most valuable information is to be accumulated and be digitalisable. This paper addresses a number of matters associated with the collection, validation, and recording of faults and failures of passenger car components. Aim. To examine the state-of-the-art systems that collect and process fault data in engineering companies and to suggest algorithmic and methodological solutions to improve the degree of automation of failure information processing. Methods. The paper uses methods of system analysis and software engineering. Conclusions. An algorithm for recording product failures according to incoming documents is proposed. Software solutions have been developed to automate the process of collecting and processing data on malfunctions of passenger car components. The authors examined a method of tracking the warranty fleet required for defining the total operating time as part of calculating the dependability indicators of passenger car components in operation. A failure code list was proposed that takes into account the specificity of the structural relationships between passenger car components.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,007 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».