Proactive maintenance strategy for harbour crane operation improvement

Robotica ◽  
2003 ◽  
Vol 21 (3) ◽  
pp. 313-324 ◽  
Author(s):  
B. Iung ◽  
G. Morel ◽  
J.B. Léger

Quay cranes are particular transportation devices for which operation's safety and CRAMP parameters (Cost, Reliability, Availability, Maintainability, and Productivity) should be fulfilled with regard to a harbor maintenance strategy. The maintenance process is first considered within a holistic modeling framework in order to cope with the current practices of treating strategic, operational and engineering maintenance issues independently without taking into account their interactions within an entire Enterprise System. Proactive maintenance is then highlighted as a new model aiming to globally optimize the components operation parameters throughout three interacting prognosis, diagnosis and monitoring processes. Technical issues related to Intelligent Maintenance System are finally proposed in order to support proactive maintenance operations at the enterprise field level and applied to quay cranes in a particular site within the frame of the European Eureka ‘Robcrane' project.

2017 ◽  
Vol 107 (07-08) ◽  
pp. 530-535
Author(s):  
T. Miebach ◽  
M. Schmidt ◽  
P. Prof. Nyhuis

Der Fachbeitrag stellt eine Methode vor, mit der sich Bibliotheken von Instandhaltungsmaßnahmen selbstlernend gestalten lassen. Die „Intelligenz“ solcher Systeme bietet mehrfachen Nutzen, einerseits durch die Auswahl der passenden Instandhaltungsmethode zum richtigen Zeitpunkt, andererseits durch die damit verbundene Erhöhung des kompletten Abnutzungsvorrates. Die Ergebnisse sind im Sonderforschungsbereich 653 „Gentelligente Bauteile im Lebenszyklus – Nutzung vererbbarer, bauteilinhärenter Informationen in der Produktionstechnik“ entstanden.   This article describes a method to design a self-learning maintenance library. The benefit derived from the intelligence of those systems refers to the right choice of maintenance measures at the right time and the enhancement of the whole wear margin. The results are part of the Collaborative Research Centre 653: Gentelligent components in their lifecycle – Utilization of inheritable component information in product engineering.


2014 ◽  
Vol 536-537 ◽  
pp. 454-460
Author(s):  
Zhi Xin Yang ◽  
Chen Lei

With the emerging of RFID technology and increasing pressure on maintenance, higher request is posed on the maintenance action. This paper introduces a combined intelligent system to complete the maintenance task. SVM and SVR model has been trained to classify machine fault types and predict the degradation. The proposed system can carry out maintenance action with the staff position information form RFID tags and the machine condition information. Genetic algorithm will be used to search the best maintenance sequence, then, the combined information will help make most efficient maintenance decision.


2014 ◽  
Vol 47 (3) ◽  
pp. 7116-7121 ◽  
Author(s):  
Marcos Zuccolotto ◽  
Luca Fasanotti ◽  
Sergio Cavalieri ◽  
Carlos Eduardo Pereira

2004 ◽  
Vol 55 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Enrique A. Sierra ◽  
Juan J. Quiroga ◽  
Roberto Fernández ◽  
Gustavo E. Monte

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