Sustainable maintainability management practices for offshore assets: A data-driven decision strategy. (10th November 2019)

• Record Type: Journal Article
• Authors: Zhang, Shengyue; Yan, Yifei; Wang, Peng; Xu, Zhiqian; Yan, Xiangzhen
• Is Part Of: Journal of cleaner production. Volume 237(2019)
• Publication Date: 2019-11-10
• Subjects: Offshore assets -- Sustainable practices -- Maintainability management -- Data mining -- Decision-making; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.117730
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Efficient and reliable maintainability management is a basic element behind ensuring sustainable practices for offshore assets. A method for easily implementing offshore asset maintainability management is proposed and described in terms of the maintainability decision-making process based on data-mining technology. A dataset of historical logs from operational systems drawn from offshore oil and gas service firms that have adopted sustainable maintenance management information system practices, including 12 main systems and 91 auxiliary systems, is used to inform a case study describing maintainability management and the decision-making process. The method uses the optimal values from a multicore classification model that adopts a goal-oriented data mining decision-making approach. Based on maintenance feature attributes, fault duration, fault loss, and frequency of occurrence in maintenance management are the three most important predictors of decision objectives. The decision tree classification results also indicate that the total average maintainability of assets in the key assets component is 53.4%, and the total average maintainability of noncritical assets is 37.9%. The five most important characteristic events found during maintenance and configuration processes were flaws in the tubing for A-annulus communication, leakage in the closed position, external leakage, failure to close on demand, and hydraulic failures that cause safety loss. The results provide unique insights into how offshore enterprise operators can improve maintainability management and decision-making performance using a data-driven decision strategy perspective. Furthermore, it provides a solution for visual proactive maintenance management and decision making under a data-driven framework, making it easier to implement maintenance management and decision-making tasks. Graphical abstract: Image 1 Highlights: Proposed a maintainability management method for offshore assets. Devised a multicore algorithm architecture using a data mining model. Illustrated the visual mapping of sensitive quantification based on a heat map. Implemented a clustering process for industrial application scenarios. Executed a realistic decision-making process for expected maintainability.