EMaintenance Industrial Applications: Issues and Challenges

Abstract

AbstractAchieving business excellence within industries which utilize complex technical systems requires effective and efficient maintenance. Maintenance is an important enabler of business performance. An effective maintenance strategy creates additional values in an organization’s value-generating process. Establishing an effective and efficient maintenance process is highly dependent on supporting Information and Communication Technology (ICT) infrastructure for information logistics that facilitates maintenance decision support. eMaintenance solutions facilitate effective and efficient management and control of maintenance activities through an enhanced utilization of computing; like estimation of Remaining Useful Life (RUL); reduction of No-Fault Found (NFF); and prediction of fault. eMaintenance solutions enable a seamless integration and fusion of information services provided by information intensive systems with embedded components in order to manage increasing and extensively distributed real-time data collected via different data sources (e.g., sensors). Since, the emerging ‘Internet of Things’ is expecting to dramatically change information systems with inherent embedded components, eMaintenance solutions need to be adapted to this new context to fulfill the overall business requirements on an effective and efficient decision-making process; for e.g.,: real-time analysis based on real-time data and context-aware information logistics. However, development and establishment of proper eMaintenance solutions can be facilitated through utilization of an appropriate framework which deals with real-world challenges. This paper explores some of the issues and challenges pertaining to eMaintenance in industrial applications.KeywordsBusiness ProcessMaintenance StrategyInformation LogisticsRemain Useful LifeCondition Base MaintenanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.