# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 7 Conclusions  
This work presents a modular architecture that exploits edge and cloud capabilities to deploy analytics techniques on the shop floor near industrial assets. Services running on edge devices were developed using a Docker containerization approach to perform tasks such as data collection, sensor selection, data flow, data preprocessing, and suggested maintenance based on the predictive models running on each edge device. The cloud allows  
Figure 13: The current mold’s working time between failure and unavailability caused by failure. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Failure** & \(wt_{i}\)**CM** & **Unavailable Time** & \(wt_{i}\)**PM** & **Unavailable Time** & \(wt_{i}\)**GFT** & **Unavailable Time** \\
**Number** & **(min)** & **(CM) (min)** & **(min)** & **(PM) (min)** & **(min)** & **(GFT) (min)** \\ \hline
1 & 2485 & 315 & - & 315 & **2155** & **247.5** \\
2 & 140 & 35 & - & 35 & - & 35 \\
3 & 300 & 180 & - & 180 & - & 180 \\
4 & 55 & 585 & - & 585 & **5** & **247.5** \\
5 & 2895 & 5 & - & 5 & - & 5 \\
6 & 1890 & 390 & - & 390 & - & 390 \\
7 & 850 & 725 & - & 725 & **680** & **247.5** \\
8 & 3030 & 3915 & **3025** & **247.5** & **2945** & **247.5** \\
9 & 2955 & 35 & - & 35 & - & 35 \\
10 & 170 & 95 & - & 95 & - & 95 \\ \hline \end{tabular}
\end{table} 
Table 3: Results for different maintenance strategies. services such as data storage, integration of sensor data with contextual data from the company's MES, and dashboards for data visualization and management of edge devices. The studied use case at OLI in Aveiro, Portugal shows the advantages of the proposed architecture, primarily in terms of being customizable and flexible, achieving all the features highlighted in [12] that an architecture based on edge computing should encompass. The edge devices can be remotely updated through the graphical interface and the services are container-based, allowing the architecture to be adjusted to new requirements. For example, in this work it was necessary to add new sensors to monitor humidity and temperature inside the feed hopper. In the future, new functionalities, such as analytics for quality assessment of injected parts or collecting new sensor data, could be added to the system without disrupting its current functionalities. Distributing data preprocessing over edge devices allows for a reduction of storage space, as only useful data are sent to the cloud. The results show that the processed and aggregated data require only 0.2% of the storage space required by the raw data. This alleviates network burden, as the data are sent in intervals of five minutes (the aggregation time window) instead of being sent with the 1 Hz sampling rate. Furthermore, because each mold may work on different IMMs, using a distributed approach allows for customization of different predictive models for each injection mold. Exploitation of anomaly detection with GFTs balances proactive and corrective actions in such a way that, when considering a maintenance strategy based on GFT suggestions, estimated reductions of 27.05% and 63.43% were achieved compared with the optimized PM and CM strategies, respectively. Furthermore, the combination of anomaly detection and GFTs enables the creation of monotonic features from raw data containing abnormal values distributed stochastically, which enables the degradation assessment of industrial assets that would be impossible with only raw sensor data. Our future work will focus on exploiting the proposed architecture to solve other data-related issues in other industrial scenarios. In the concrete case of IMMs, with sufficient data from the retrofitting sensors inside the feed hopper a study could be conducted analyzing the impact of the monitored parameters on the quality of the injected parts, which could allow development of other models to assess the quality of these parts.