## 4 Model (SIRPM)
### Characteristics and Benefits of the Model (SIRPM)
This integration results in more accurate and effective real-time monitoring in addition to an improvement in energy efficiency and sustainability, aspects that are often addressed in a limited way in existing models. Artificial intelligence (AI) is a broad field encompassing techniques to simulate human intelligence in machines. Machine learning (ML) is a subset of AI focused on systems capable of learning and improving from data without the need for explicit programming. Deep learning (DL), in turn, represents an advanced approach within ML, utilizing deep neural networks to solve complex problems such as pattern recognition in large volumes of data. In this model, ML is primarily used to identify patterns and predict failures, while DL can be applied to the analysis of complex data, such as images or time series. Although DL is a promising field, the decision was made to adopt ML due to the nature of the problem and the available data. DL is particularly effective in contexts where large volumes of unstructured data, such as images or videos, are common. However, in the present study, the amount of tabular data available for analysis does not justify the use of DL models, which require considerable computational resources. ML, on the other hand, offers an efficient, accessible, and suitable approach to address predictive maintenance, providing satisfactory performance with a more agile implementation. The distinction between AI, ML, and DL, as presented in Table 12, can be observed based on the approaches adopted in studies related to artificial intelligence as follows:  
* AI (artificial intelligence) encompasses various techniques to simulate human intelligence. Several articles mentioned in Table 12 utilize AI through diverse approaches, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Technology** \\ **Integration** \\ **(RPA, Machine** \\ **Learning, IoT)** \\ \end{tabular} & \begin{tabular}{c} **Method/** \\ **Algorithm** \\ **Algorithm** \\ \end{tabular} & \begin{tabular}{c} **Intelligent** \\ **Automation** \\ **Learning** \\ \end{tabular} & \begin{tabular}{c} **Continuous** \\ **and** \\ **Scalability** \\ \end{tabular} & \
\begin{tabular}{c} **Flexibility** \\ **and** \\ **Monitoring** \\ \end{tabular} &
\begin{tabular}{c} **Real-Time** \\ **Efficiency and** \\ **Sustainability** \\ \end{tabular} \\ \hline
[59] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON], [PERSON] & Rule-Based Automation & x & High & x & Moderate \\ [PERSON]; [PERSON]; [PERSON]; [PERSON] (2023) & & & & & & \\ \hline
[60] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] (2024) & RPA & Rule-Based Automation & x & High & x & Moderate \\ \hline
[61] [PERSON], [PERSON], [PERSON]; [PERSON] (2023) & RPA & Rule-Based Automation & x & High & x & Moderate \\ \hline
[62] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] (2023) & RPA & Rule-Based Automation & x & High & x & Moderate \\ \hline
[This work] & RPA, Machine Learning &
\begin{tabular}{c} Rule-Based Automation, Decision Trees \\ \end{tabular} & x & x & High & x & High \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._including machine learning algorithms, genetic algorithms, and dynamic programming. * ML (machine learning), a subset of AI, focuses on systems that learn from data. Articles that explicitly use ML techniques, such as reinforcement learning algorithms, search algorithms, and Bayesian networks, aim to identify patterns and make predictions based on data. * DL (deep learning) represents an advanced approach within ML, utilizing deep neural networks. The use of deep neural networks is more relevant in contexts involving large volumes of unstructured data, such as images or videos, which are not directly addressed in the studies in the table.