Development of a Decision Support System (DSS) for Fitter Mechanics on Bulldozer Power Failure Maintenance: Case Study of Komatsu and Cummins Engines
Notice bibliographique
Résumé
Maintenance plays an important role in the life span of equipment. The cost incurred during maintenance of machineries affects the service cost of the equipment. In other to reduce the cost and time wastage, effective troubleshooting tool is required. Power failure in earth moving equipment arises when the front-mounted “dozer” blade cannot push soil forward and create a level surface for construction site. This problem needs to be solved, so as to increase the equipment life span and productivity. This research has established that power failure in earthmoving equipment (bulldozer) results from a number of causes and the causes have been identified as faulty: torque converter, transmission system, steering system, air cleaner, turbo charger, fuel filter, injector, high water temperature and dust indicator. The maintenance service card (job card) for recording the maintenance carried out on the machine used as case study was visited. Through this, the historic data of frequency of occurrence of each problem throughout the year was collected as it affected the machine under study. Probability tree model was developed as predicting tool based on the historic information collected from the job card of the machine. Based on this probability tree, a logic was developed which lead to software algorithm development. Through this algorithm, a software was developed to enhance the speed of computation and making decision available speedingly using C # (pronounced as C sharp) computer language because of its versatility and friendly nature. The user interface of the Knowledge based system is basically divided into analyses and troubleshooting, the analyses ask the operator questions related to lack of power in the machine, and the troubleshooting tests were carried out as it affects: transmission, torque converter, steering, clutch and brake system problems. The system gave a thorough maintenance breakdown analysis of power failure of the engine under study. The decision support system has an advantage of providing the maintenance engineer a knowledge of the probable causes of problems and the required solution; however this depends on the response of the user to the questions asked in the user interface. The solutions proffered by this decision support system were evaluated by compared with the proffered solutions in the manual of the machine under study and it was found to be the same. In essence, it is recommended that this system should be used in situations where the service technicians are knowledgeable to answer the questions in the user interface of the system. The use of decision support system to solve power failure problem can result to reduction in manpower, increase assurance of project completion in time and is therefore applicable and recommended for use in manufacturing and construction industries where optimum profitable services are mostly expected.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».