## 5 Case Study
### Detailed Description of the SIRPM Model  
The SIRPM model was implemented at Alpha Company to optimize predictive maintenance for critical machinery using robotic process automation (RPA) and machine learning (ML) technologies. Figure 3: Schematic diagram of the operational flow of the predictive maintenance model applied to the company. Investigation duration: 12 months, including data collection, model training, and implementation. Working hours in Portugal: 8 h/day. Work shifts per day: two shifts of 8 h, totaling 16 h daily. The following sections detail the implementation process, technologies used, and results obtained:  
* Phase 1: Infrastructure Assessment  
To ensure the feasibility of the SIRPM model, the process began with an evaluation of the company's infrastructure, examining its ability to connect sensors and integrate necessary platforms. Alpha's infrastructure supported real-time data collection, enabling the installation of IoT sensors on selected machines. Machines Analyzed:  
Paper cutter--vibration sensor to monitor mechanical wear. Hydraulic press--pressure sensor to detect variations in hydraulic fluid. Flexographic printer--temperature sensor to predict overheating. * Phase 2: Data Collection and Processing  
During this phase, data collection was automated via smart sensors to ensure real-time information retrieval. Technologies Used:  
* RPA: UiPath was employed to automate data collection from sensors connected to the machines. RPA (robotic process automation) was used to automate the collection of data from sensors connected to industrial machines. The tool chosen was UiPath, which was configured on a virtual machine in the hybrid cloud environment. In this environment, RPA robots were programmed to interact with the machines' control systems and extract real-time data, such as vibration, pressure, and temperature values. UiPath was programmed to perform automatic logins to the machines' monitoring systems, access the sensor data, and organize it in a structured way. After collection, the RPA sent the data to a SQL database, where it was stored for later analysis. The programming was performed using the UiPath Studio graphical interface, which allows you to create automated flows through specific activities for each task, such as logging in, extracting, and sending data. * Python: Used for pre-processing and normalization, ensuring consistency and readiness for analysis. Python was used to access data from the SQL database using SQLAlchemy, clean missing values, and normalize variables for predictive analysis, with libraries like Pandas, NumPy, and Scikit-learn. Python was also used to feed a machine learning-based predictive model, which identified patterns and predicted possible machine failures. The Python scripts were executed on virtual machines within the same cloud environment, enabling integration with the data collected by the RPA, ensuring the continuity of the automated predictive maintenance process. Data collection (Table 13) was conducted over five months, with historical data stored on local servers and in the cloud for subsequent analysis. The data presented in this table correspond to the monthly averages of the data collected throughout the year. To build the model, the data recorded every hour by the sensors were used. However, to facilitate visualization and analysis in this work, it was decided to present the accumulated average values of each month, allowing for a clearer and more concise interpretation of the information throughout the analyzed period. \begin{tabular}{c c c c} \hline \hline
**Month** & \begin{tabular}{c} **Average Vibration** \\ **(Cutter) (mm/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Pressure (Press) (bar)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Temperature (Printer) (\({}^{\circ}\)C)** \\ \end{tabular} \\ \hline
1 & 1.2 & 150 & 75 \\ \hline
2 & 1.3 & 145 & 78 \\ \hline
3 & 1.4 & 140 & 80 \\ \hline
4 & 1.5 & 138 & 82 \\ \hline
5 & 1.3 & 142 & 77 \\ \hline \hline \end{tabular}