# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 1 Introduction
It is not a specific maintenance strategy, instead being a decision-making process based on reliability analysis that attempts to ensure efficient, cost-effective, and reliable maintenance [10]. RCM uses proactive actions while combining several maintenance policies to optimize its process; in this way different decisions may be taken for different failures. Proactive maintenance actions require end-to-end infrastructure to handle data collection, storage, and processing, which represents a considerable challenge [11]. Although centralized cloud solutions may provide high computational resources and storage capacities, the increasing number of industrial assets and high volumes of data require modular solutions capable of handling analytical tasks near the shop floor to alleviate the burden on the cloud [12]. Using intelligent edge devices for PdM distributes the computing requirements, enabling more efficient data processing, which in turn reduces latency. This approach can enhance security as well, as sensible data can be processed and stored locally in an isolated network, reducing both network latency and exposure to potential cybersecurity threats. In our previous work, presented at the 4 th International Conference on Industry 4.0 and Smart Manufacturing [13], we proposed a combination of the statistical technique of generalized fault trees (GFTs) and the anomaly detection technique of an isolation forest (IF) to optimize maintenance actions on injection molds at OLI, a Portuguese company known for its expertise in the injection molding process. The above-mentioned approach suggests proactive actions prior to severe failures, while allowing non-severe failures to occur. This work extends our previous paper [13] by: (1) presenting a smart distributed architecture based on Docker microservices deployed on intelligent edge devices; (2) describing in detail the deployment of the predictive models in the proposed architecture and presenting new services and functionalities, such as retrofitting of IMMs with new sensors; and (3) providing additional experimental results. The main contributions of this work are as follows:  
1. We introduce a novel modular architecture centered around intelligent edge devices, offering significant advantages in data preprocessing and analytics capabilities in close proximity to the shop floor. By harnessing the potential of edge devices, the proposed architecture alleviates network congestion and diminishes storage demands. The aggregated and preprocessed data occupy less than 0.2% of the storage space needed for the raw data, enhancing efficiency and resource utilization, which demonstrates the potential of intelligent edge devices in optimizing data management and analysis within industrial settings. 2. The proposed architecture is presented for deployment of highly adaptable services on edge devices and on the cloud. This approach empowers seamless customization of services developed through Docker containers, allowing for effortless modification without disruption to ongoing operations. To validate this capability, we successfully implemented retrofitting sensors on the feed hopper of an IMM in response to a new specific requirement from the company. Our findings demonstrate the practicality and effectiveness of the proposed solution in meeting evolving industrial needs while ensuring uninterrupted service functionality. 3. We exploit OpenBalena [14], an open-source platform, in conjunction with a graphical interface to efficiently manage device fleets and remotely update the microservices deployed on edge devices. Through this approach, we demonstrate the practical implementation of a comprehensive solution for streamlined device management and seamless microservice updates, showcasing the potential of open-source technologies in optimizing fleet operations. 4.