Descriptors :
Big data analytics , Fault predictionShop , Shop floor , Scheduling
Abstract :
tThe current task scheduling mainly concerns the availability of machining resources, rather than thepotential errors after scheduling. To minimise such errors in advance, this paper presents a big dataanalytics based fault prediction approach for shop floor scheduling. Within the context, machining tasks,machining resources, and machining processes are represented by data attributes. Based on the availabledata on the shop floor, the potential fault/error patterns, referring to machining errors, machine faultsand maintenance states, are mined for unsuitable scheduling arrangements before machining as well asupcoming errors during machining. Comparing the data-represented tasks with the mined error patterns,their similarities or differences are calculated. Based on the calculated similarities, the fault probabilitiesof the scheduled tasks or the current machining tasks can be obtained, and they provide a referenceof decision making for scheduling and rescheduling the tasks. By rescheduling high-risk tasks carefully,the potential errors can be avoided. In this paper, the architecture of the approach consisting of threesteps in three levels is proposed. Furthermore, big data are considered in three levels, i.e. local data, localnetwork data and cloud data. In order to implement this idea, several key techniques are illustrated indetail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big dataanalytic algorithms. Finally, a simplified case study is described to show the prediction process of theproposed method.
