# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
###### Abstract  
With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT. 1  
Dept. Sci. objectives with minimal losses. In a smart factory, most tasks are performed by machines rather than humans [2]. While machines can operate continuously, ensuring their efficiency requires careful attention to maintenance in order to avoid unnecessary unplanned downtime (UPDT). As shown in Figure 1, the majority of issues--up to 42%--are caused by aging equipment, while 21% are due to mechanical failures. Therefore, maintenance engineers must possess the skills or tools necessary to effectively manage these problems and reduce UPDT resulting from aging equipment. Figure 2 shows data from the 2018 Plant Engineering Maintenance Survey, sponsored by Advanced Technology Services (ATS), which identified the three most commonly used methods for smart factory management: preventive maintenance, the computerized maintenance management system (CMMS), and reactive maintenance. Reactive maintenance, which involves waiting for a machine to fail before repairing it, is the least efficient compared to the other two methods. Figure 3 illustrates that the top three most important strategies for reducing UPDT are equipment upgrades (56%), improved training (47%), and transitioning to a predictive maintenance strategy (45%). Based on this information, the concept for designing a maintenance system to address these issues was developed [3]. Therefore, this research proposes the implementation of the intelligent maintenance system (IMS), which integrates preventive maintenance methods with the CMMS. The primary goal of the CMMS is to optimize the maintenance process and enhance machine efficiency, thereby reducing costs and downtime in a smart factory [4]. It works in conjunction with the state space search (SSS) algorithm to present optimized solutions for equipment selection and replacement at the appropriate time, extending the lifespan of aging equipment, which is a key factor in UPDT [5]. Additionally, it recommends cost-effective maintenance  
Figure 1: Leading causes of unplanned downtime (UPDT). Figure 2: Advantages of the top three maintenance strategies and tools.