# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 6 Results and Discussion
Several approaches for PdM try to forecast the degradation of industrial assets by determining how many working cycles they can perform until a failure occurs. Such approaches achieve very good results when the sensor signals have an almost monotonic  
Figure 10: Dashboard for the openBalena platform: (**a**) API keys management and (**b**) management of microservices running on edge devices. Figure 9: Measurements obtained with the retrofitted bme280 sensor in the feed hopper: (**a**) humidity in the feed hopper and (**b**) dew point in the feed hopper. trend that describes the asset's degradation, which is often the case when using data from simulation models, such as the ones in [57]. However, real data sensor signals typically do not follow a monotonic trend, while the component's degradation increases. Instead, there are abnormal data points stochastically distributed over time, and their cumulative occurrence may describe the degradation process. Figure 11a shows the maximum time for the first injection (the time that it takes to fill 95-99% of the mold) in each 5 min time window aggregation. The data were collected in the period between two mold malfunctions, with the first point collected just after the mold was repaired and started working again and the last point representing the moment when another mold failure occurred. As can be seen, the sensor values do not follow a monotonic trend. Instead, there are data points that clearly differ from the remaining ones. Furthermore, the failure occurs a considerable time after the last spike occurred, which makes it challenging to determine the exact moment when a failure may occur. Figure 11b shows the CDF curve of a BE that results from anomaly detection for this variable within the same period. While the sensor's signal follows a stochastic trend of abnormal events, the CDF curve of the BE obtained from it has an increasing monotonic trend that describes the mold's degradation much better. Note that the anomalies detected when there are other planned events, such as a setup change, are not accounted for, as they are not expected to cause degradation of the mold. While we present only the example of the first injection time in Figure 11a, the remaining variables show similar stochastic behavior. After sensor selection, a total of ten variables regarding the mold's operation were selected. After the aggregation process and data enrichment, a total of 80 different BEs were generated from the initial variables. The GFT approach was used with all these variables to build the tree that best described the mold's mechanical failures. Note that all 123 initial variables are available for consideration, as the approach is not dependent on expert knowledge; however, if such knowledge is available, as it was in the case under study, it can be exploited to speed up the training process. The GFT structure obtained from the training process is represented in Figure 12, and the description of BEs is presented in Table 2. An issue that often hinders the implementation of PdM is the fact that complex ML or deep learning (DL) methods are seen by maintenance practitioners as \"black boxes\" [58]. One of the main features of GFT models is that their graphical representation is easily interpretable by humans, as it is possible to perform a qualitative analysis of the failure modes from the tree structure by determining and analyzing the MCS. In the concrete case of the considered injection mold, the following four MCS were obtained:  
* \(C_{1}=\{E\_1,E\_3,E\_4\}\),
* \(C_{2}=\{E\_1,E\_3,E\_5,E\_6\}\),
* \(C_{3}=\{E\_2,E\_3,E\_4\}\),
* \(C_{4}=\{E\_2,E\_3,E\_5,E\_6\}\). Figure 11: Sensor signal and feature based on the BE probability: (**a**) maximum first injection time in each 5 min time window and (**b**) CDF curve for the detected anomalies in the maximum first injection time.