Journal Article

Cloud-enhanced predictive maintenance

Schmidt, Bernard, Wang, Lihui

2018

Overview

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

• Publisher: Springer London,Springer Nature B.V
• Subject: CAE) and Design / Cloud computing / Cloud manufacturing / Computer-Aided Engineering (CAD / Condition based maintenance / Condition monitoring / Condition-monitoring data / Context awareness / Decision making / Engineering / Industrial and Production Engineering / Industrial applications / Information contents / Life cycle / Machinery condition monitoring / Maintenance / Maintenance management / Manufacture / Manufacturing equipment / Mechanical Engineering / Media Management / Operation and maintenance / Original Article / Population statistics / Predictive maintenance / Production and Automation Engineering / Produktion och automatiseringsteknik / Technology / Teknik / Working conditions