# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 2 Literature Review
### Architectural Perspective  
Approaches for developing and deploying predictive models in real scenarios have changed over time. The first were centralized, and relied mainly on the computational power of the cloud [44]. With the increasing number of assets being monitored and the high volumes of generated data, these approaches evolved into decentralized and modular solutions, exploiting the capabilities of edge devices to reduce the network latency and the storage requirements of the cloud [45]. For example, in [46], the authors preprocessed data and ran ML algorithms on edge devices, integrating these features within a modular platform with computational cloud capabilities. In [34; 47], the authors exploited the  
Figure 2: Skeleton of a generic fault tree. computational capabilities of edge devices to run complex algorithms for fault detection, while in [48] the authors used edge computing for anomaly detection. In addition to the edge layer, architectures may use a fog layer, which is an intermediate layer between edge devices and the cloud that enables cloud services such as data storage near the shop floor. In [49], the edge layer was used mainly for data collection, data transmission, and low-complexity analytics, while the fog layer was used to run more complex analytics and then send the processed and aggregated data to the cloud. In [11] and [50], the authors explored cloud capabilities to train complex models and push them as Docker microservices to the edge devices through a fog network. In addition to recent architectures for PdM, there is a trend of exploring the capabilities of edge devices [12] for either data collection and extracting field-level data from legacy built-in sensors [51], or for preprocessing the data to run more complex algorithms. Because data produced in industrial environments are noisy and typically collected at a high rate, sending raw data to the cloud requires high network and storage capabilities; thus, carrying out preprocessing locally on edge devices can alleviate the burden on the network, as only relevant data need to be transmitted. One aspect that should be accounted for when developing an architecture for PdM is the flexibility to customize several services, as production requirements and an enterprise's needs may change over time; therefore, the applications running on edge devices may have to be adapted according to new requirements. Using containerization approaches, as proposed in [11; 50], is one path that might be followed to achieve flexibility and customization. However, a suitable management platform is required in order to easily customize and deploy services on edge devices on a large scale, and should support different types of devices, allow remote updates, enable the addition or removal of devices, be able to manage permissions, and have a graphical interface that allows for easy used by maintenance practitioners and stakeholders. In this work, we propose an architecture that exploits the capabilities of edge devices by deploying microservices built as Docker containers. These services can preprocess data, run predictive models near the shop floor where the data are produced, and retrofit industrial settings with additional sensors. We exploited the OpenBalena [14] open-source platform to manage fleets of edge devices deployed on a large scale in a real manufacturing environment at OLI. This platform supports different types of devices, allowing their management by enabling services to be remotely updated securely, managing permissions, and removing or adding devices, among other functionalities described in the remainder of this document. Furthermore, a graphical interface allows for easy use of the platform. To the best of our knowledge, this work represents the first time the open-source OpenBanela platform has been used in a real industrial environment.