## 5 Case Study
### Application of the Model to the Case Study  
To address Alpha's recurring machine breakdowns and maintenance inefficiencies, the SIRPM model is applied to optimize predictive maintenance through the integration of robotic process automation (RPA) and machine learning (ML). Below is the step-by-step implementation tailored to Alpha's context. * Phase 1: Infrastructure Assessment The evaluation of Alpha's infrastructure confirms the readiness for data collection and connectivity. Assessment Outcomes: 1. Smart sensors: install IoT-enabled sensors on critical machines to collect data on temperature, vibration, and pressure. 2. Connectivity: ensure a robust connection between sensors and a central data platform using Wi-Fi or industrial connectors for reliable real-time data flow. * Phase 2: Automation of Data Collection and Processing Automation is introduced to handle data collection, preprocessing, and validation. Actions Implemented: 3. Deploy RPA bots to collect data directly from the installed sensors and Alpha's existing systems. 4. Automate preprocessing tasks like cleaning and normalizing data to make it analysis-ready. The preprocessing of the captured data was carried out in several steps to ensure its quality and usability. Initially, missing values were filled using linear interpolation to preserve data integrity. Then, outliers were identified and removed based on statistical metrics such as standard deviation. The data were then normalized, facilitating the predictive analysis and training of machine learning models. This transformation was essential to ensure comparability between different variables and reduce potential biases during analysis. 5. Validate data quality, ensuring only consistent and complete data are processed. Data quality validation is ensured through a rigorous pre-processing process, where automated actions are performed to ensure that the data collected from the sensors are consistent and complete. Initially, missing values are filled with linear interpolation, maintaining data integrity. Outliers are identified and removed based on statistical metrics, such as standard deviation, to avoid bias in the analyses. The data are then normalized to ensure comparability between different variables, facilitating the training of machine learning models. The final quality of the data is validated by automated checks, where it is compared with reference values and machine operating conditions. Inconsistent data or data outside the expected parameters are flagged or discarded, ensuring that only valid data ready for analysis are used, guaranteeing the effectiveness of predictive analysis and the accuracy of decisions based on these data. For data validation, the following steps were performed:  
Rigorous pre-processing: automated actions were implemented to ensure the consistency and completeness of sensor-collected data. Filling of missing values: linear interpolation was used to maintain data integrity. Outlier identification and removal: outliers were detected and eliminated based on statistical metrics, such as standard deviation, to avoid bias in the analyses. Data normalization: variables were normalized to ensure comparability, facilitating the training of machine learning models. Automated quality checks: data were compared against reference values and machine operating conditions to verify its validity. Flagging or discarding inconsistent data: data outside expected parameters were either flagged or discarded, ensuring only valid data were used for analysis. This process ensures effective predictive analysis and enhances the accuracy of decisions based on the validated data. Phase 3: Application of Machine Learning  
Predictive models are developed and trained to forecast failures. Steps Executed:  
Algorithm selection: use decision trees for initial predictions due to the small dataset and ease of interpretation. Develop and train ML models. Generate failure forecasts, identifying patterns indicative of imminent breakdowns.