Towards proactive maintenance actions scheduling in the Semiconductor Industry (SI) using Bayesian approach

Abstract

The Semiconductor Industry (SI) is one of the fastest growing manufacturing domains challenged by the high-mix low-volume production and short product life cycles. This results an increase in unscheduled equipment breakdowns that often result in decreasing and unstable production capacities. The success in the SI depends on our ability to quickly recover from unplanned events. It is reported (Abu-Samah et al, 2014) that misdiagnosis is one of the key reason for the extended failure durations. This is because of the fact that existing procedures to support maintenance decisions for an equipment recovery are often based on FMEA approach that represents static experts’ knowledge. In this paper, we present a methodology based on Bayesian network (BN) to advise technicians on the choice of maintenance procedure in case of unscheduled breakdowns. We argue that the sequence of patterns and alarms as generated by the equipment during production can be associated to the choice of maintenance procedure; therefore, BN is learned as a function of these alarms and warnings to predict the choice of maintenance procedure from unscheduled breakdown historical data. The set of warnings and alarms are grouped together in the proposed methodology using hybrid approach where these are first clustered based on distribution similarity followed by an experts’ intervention to fine tune initial clusters. The main contribution of the proposed methodology is to support technicians with advise on the choice of most likely effective maintenance procedure that will reduce unscheduled breakdown period and help in improving and stabilizing the production capacities. The proposed methodology is validated with a case study, from the world reputed semiconductor manufacturer, using historical data. The results show 49% of gain in terms of productive time from unscheduled breakdown periods.