# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 4 Proposed Architecture  
The proposed architecture comprises edge and cloud capabilities built on flexible Docker microservices, as shown in Figure 3. The edge devices use the Balena operating system (OS), which is a lightweight Linux-based OS specially developed to run on low-powered edge devices, while the cloud layer is composed of the OpenBalena [14] microservices platform, which allows for remote management of the edge devices and for microservices to enable features such as data hubs, storage, and dashboards. \begin{table}
\begin{tabular}{l l l} \hline \hline
**EUROMAP Parameter** & **Description** & **Unit** \\ \hline ActStrCsh [1] & Injection completion position & mm \\ ActPrsXfrSpec [1] & Pressure at the time of injection with 1 st pressure & MPa \\ ActStrXfr [1] & Transfer stroke actual for each injection unit. & mm \\ ActTimCyc & Cycle time & s \\ ActTimFill [1] & Time spent for 1 st injection & s \\ ActTimPlst [1][1] & Time spent for plasticizing & s \\ ActPrsXfrSpec [1] & Pressure at the time of injection with 1 st pressure & MPa \\ ActVolPlst [1] & Actual plasticization volume for each injection unit & mm\({}^{3}\) \\ @ActFrClp\_PstInj [1] & Clamping force at injection completion & MPa \\ ActTmppBrIZn [1;1] & Measured heater 1 temperature & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IMM sensor descriptions. The OLI shop floor comprises several IMMs, each of which is equipped with an edge device that executes the following tasks:  
* Data Collection: Data are collected from each IMM according to the EUROMAP63 protocol [52]. Each device receives 123 parameters from the injection process at a sampling rate of 1 Hz. * Preprocessing Rules: Of the 123 process variables, most are not considered in our analysis as they do not have variance over time, while others are not considered based on insights provided by the enterprise's technicians and engineers, mostly because these variables do not present abnormal values between mold failures. In addition to sensor selection, the aggregation of data in time windows and the creation of additional features are performed on edge devices. Note that these rules are easily configurable, allowing customization of the pipeline. * Intermediate Storage: The edge devices in this use case are Raspberry Pi 4 boards with storage capacity, allowing temporary storage of raw data for preprocessing, aggregation, and enrichment before being sent to a central broker. * Data Flow: The aggregated and processed data are sent to a topic in the mosquito broker on the cloud, where consumer applications can use the data stored in the cloud for several purposes. Because only preprocessed data are sent, the volume of generated data is reduced compared to the raw data. * Prediction: The edge devices run predictive models such as GFT and anomaly detection. These models are trained on the cloud and pushed to the edge devices using the OpenBalena microservices running on the cloud. When an impending failure or abnormal behavior is detected, the edge devices can trigger alerts and send them to operators and maintenance teams. Figure 3: Architecture of the proposed approach. The fact that the mentioned services are built using Docker containers enables several features; isolation at the application level makes it easier for microservices to be managed and updated without affecting others; flexibility and portability mean that microservices can be deployed across different infrastructures that support Docker and be easily customized for other use cases; and ease of deployment is achieved thanks to the fact that all applications and dependencies are packed in the same container, making them less dependent on the particular device on which they are running. Management of the devices relies on the OpenBalena platform installed on the cloud, which is an open-source platform for managing and deploying fleets of devices running the Balena OS. It is composed of several microservices, with the registry and API being the most important ones.