## 2 Literature Review
### Machine Learning Techniques Used to Predict Industrial Machine Failures  
The advancement of high-performance technologies has resulted in an exponential increase in the amount of data collected, primarily through the implementation of Internet of Things (IoT) devices, in which sensors play a crucial role in capturing information from the environment. They have been fundamental for collecting data in several areas, such as industrial manufacturing, transportation and mobility, energy, retail, smart cities, health, the supply chain, and agriculture, among others [1; 8; 23]. Therefore, these devices bring significant opportunities for companies; the acquisition of IoT devices and the choice of appropriate sensors require careful analysis and a strategic approach to ensure success and the expected return on investment [1; 8; 23]. \begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Techniques** & **Purpose** \\ \hline Filter & Pearson Correlation & Quantifies the linear relationship between two variables and can aid in selecting features highly correlated with the target variable [18]. \\ \hline Wrapper & \begin{tabular}{c} Minimum Redundancy \\ Maximum Relevance \\ Recursive Feature Elimination \\ Greedy Forward Selection \\ \end{tabular} & \begin{tabular}{c} Evaluates each feature’s relevance to the target variable while also seeking to minimize redundancy between the selected features [10; 16]. \\ Removes less important features based on the importance assigned by a learning model [14]. \\ Starts with an empty set of features and iteratively adds the best feature that improves model performance [22]. \\ \hline Embedded & \begin{tabular}{c} Lasso Regression \\ Denoising Autoencoder \\ Neighborhood Component \\ Analysis \\ Principal Component Analysis \\ \end{tabular} & \begin{tabular}{c} Incorporates an L1 penalty into the model cost function, leading to automatic feature selection [13]. \\ Reconstructs the input data and converts them into its output. During this process, it can learn compact and informative representations of the data, effectively performing implicit feature selection [21]. \\ Creates a projection matrix to maximize the probability of correctly classifying data, which results in the automatic selection of the most discriminative features during model training [11; 17]. \\ \end{tabular} \\ \
\begin{tabular}{c} Principal Component Analysis \\ \end{tabular} &
\begin{tabular}{c} Performs dimensionality reduction by projecting the data into a new feature space composed of the most significant principal components [18; 19; 20]. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of some feature selection techniques found in the literature, their purposes and categories. In recent years, several studies have tackled the challenge of predicting and diagnosing industry failures, underscoring the pivotal role of advanced techniques like machine learning and neural networks. The study of [24] innovates with their approach to diagnosing bearing failures, introducing the novel Logistic-ELM (Extreme Learning Machine). In [9; 25], the authors developed models to estimate the state of rotating components. The study of [25] used an SVM-based system, and the authors of [9] used SVM and feature selection applying Relieff and PCA, highlighting the applicability of machine learning and feature selection methods in this scenario. In [11], the authors proposed a hybrid approach for fault classification in power transmission networks. They employed feature selection using NCA, adding complexity to diagnostic strategies. In [26], the authors created a model to predict failure in beam-column junctions, employing machine learning techniques including K-Nearest Neighbor (KNN), Linear Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), AdaBoost (AB), Light Gradient Boosting Machines (GBDTs), and Extreme Gradient Boost (XGBoost). This broad spectrum of techniques showcases the adaptability of machine learning methods.