# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 6 Results and Discussion  
The methodology described in the previous section was deployed on OLI's shop floor. In this section, we present the results obtained for analysis of the mold that showed the highest number of mechanical failures over a period of one month. Figure 8 depicts the prototype of the data acquisition system in a real industrial environment, Figure 8a shows a Raspberry Pi 4 board inside an electric cabinet near the IMM, and Figure 9 depicts the implementation of the bme280 sensor on the feed hopper to monitor parameters such as temperature and humidity. The sensor is wired-connected to the Raspberry Pi 4 board,  
Figure 7: Flowchart of the GFT training process. which collects data through the I2C protocol. Finally, Figure 8c shows the interface used to monitor the variables obtained from the IMM's built-in sensors, obtained through the EUROMAP63 protocol. Data from the built-in sensors are preprocessed on the edge devices, and only aggregated and relevant data are sent to the cloud, which represents a colossal storage savings. For example, for a period of one month the raw data for the studied mold require 976.16 MB of disc space, while the preprocessed data only require 1.93 MB; this represents less than 0.2% of the initial storage requirements. This is relevant when considering the burden on the network, as data must be sent to the central broker within a period of five minutes, which is the aggregation time window, instead of sending it at 1 Hz rate, which is the sampling rate. Note that because the energy consumption of edge devices is very low when compared to the IMMs, this parameter was not studied exhaustively; however, in cases where the energy of low-power devices is relevant, its optimization should be accounted for [55]. The data from the retrofitting sensors were not used to assess the health state of injection molds; however, collecting these data was a requirement of the enterprise, as in addition to the optimization of maintenance actions with molds, improving of the quality of injected parts is crucial for the success of the company. Thus, monitoring parameters such as humidity and temperature inside the feed hopper are important indicators to assess the quality of the final product. New parameters may be calculated From the temperature and humidity as well; for example, the dew point (the temperature at which air becomes saturated with water vapor, leading to the formation of dew) can be calculated using Equation (2), where \(T\) is the temperature in Celsius degrees, \(H\) is the humidity in percentage, and \(a\) and \(b\) are constants, with \(a=17.62\) and \(b=243.12\). Figure 9a,b shows the humidity and the dew point, respectively, inside the feed hopper for a given period, which shows the ability of the proposed architecture to be used for several purposes and its flexibility to adapt to new requirements of the enterprise. \[DP=\frac{b\times T}{c+T}+log(H) \tag{2}\]  
Figure 8: Data acquisition apparatus: (**a**) edge device (Raspberry Pi 4 board) on shop floor; (**b**) retrofitted bme280 sensor on the feed hopper; (**c**) monitoring variables through the EUROMAP63 protocol. The management of edge devices is carried out through a dashboard which communicates with the openBalena platform API. Figure 10 shows the dashboard, which is an open-source project [56]. It allows end users such as technicians and engineers at the enterprise to easily manage API keys and access to the platform, manage device health, and monitor their logs and microservices. Note that all the platforms and dashboards are self-installed in order to allow greater flexibility and control over all the applications. Assessing the degradation of industrial assets from data obtained in a real industrial environment is a challenge, as unexpected events may occur, such as malfunctions from several origins, production stops due to production changes, and planned interventions, among others.