Countering Minor Failure Behaviors in Automated Manufacturing Systems Through Opportunistic Preventive Maintenance Interventions

Abstract

Maintaining continuous operation in high-throughput manufacturing systems with minor failure (MF) problems is challenging to achieve the target production rate by a specific time horizon. The effective use of the dynamic opportunistic maintenance (OM) approach mitigates interference between the continuous schedule production operation and maintenance tasks. The study suggests a simulation-based model combining active and passive maintenance opportunity windows (AMOW, PMOW), Long-Duration Failure Modes (LDFM), and A and B types of downtimes. We aim to determine optimal maintenance policies to enhance system performance and minimize the impact of minor stoppage MF behaviors that take less than 15 minutes. To implement the OM approach, we chose the water bottling factory as a case study, investigated the system's distinctive behavior, and derived appropriate policies for implementing OM actions. The real-time information on machine failure conditions revealed a significant frequency and duration of MF events. Consequently, a discrete-event simulation DES model was developed and validated using the Simio simulation software to achieve the randomness of MF occurrences and derive its impact on the system’s performance. The system was exposed to 113 FM modes in various machines and components. To evaluate the solutions to MF's maintenance problem, we compared the production line's performance with the simulation-based optimization of OptQuest. Two functions were defined: (1) to minimize the occurrences of MF for each machine and (2) to maximize the percentage of throughput rate. Finally, the proposed framework was examined against a benchmark of the conceptual model of the current case. The proposed approach was practical as it outperformed the existing benchmark and showed that it can be practically implemented. The experimental results illustrated an improvement in system throughput of up to 10.73% and a reduction in the impact of MF phenomenon frequency of bottlenecks, machine BM and LB machine by 86% and 92%, respectively. The suggested support framework's implementation entails expenses for simulated modeling, continuous evaluation, and optimization programs such as Simio and OptQuest. Cost savings are achieved, however, since it improves productivity, decreases unscheduled interruptions, and minimizes loss of production. Optimization gains, fewer service interruptions, and higher overall equipment effectiveness (OEE) balance the initial expenditure.