## 2 Literature Review
### Machine Learning Techniques Used to Predict Industrial Machine Failures
This research has also extended to other areas, such as predicting failures of the high-pressure fuel system [10] and failure prediction in forced blowers [27]. The work [10] applied LR, RF, XGBoost, SVM, and MLP NN with feature selection using mRMR, while in [27], LR, SVM, KNN, XGBoost, and RF techniques were used without feature selection. A highlight is the work of [28], who applied joint reserve intelligence and feature selection techniques Classifier Attribute Evaluation (CAE), Correlation Attribute Evaluation (COAE), Infogain Subset Evaluation (ISE), and Classifier Subset Evaluation (CSE) to predict machine failures. This indicates a trend towards incorporating innovative approaches to improve predictive effectiveness. In [29], the authors developed an approach based on an ensemble of convolution-based methods for fault detection using vibration signals. Table 2 summarizes machine learning techniques for predicting machine failure, including the objective of each work, the machine learning techniques used, and the performance metrics used to evaluate the models. The literature on machine failure prediction covers a wide variety of approaches and techniques. Many studies focus on developing machine learning algorithms [9; 10; 11; 24; 25; 26; 27; 28; 29; 30]. Others explore methods of processing signals acquired from machines through sensors [25; 31]. There is also research investigating the use of image processing techniques to identify visual anomalies in mechanical components. These studies aim to improve the efficiency, reliability, and safety of industrial operations, contributing to predictive maintenance and reducing unplanned downtime. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Main Objective** & **Techniques Employed** & **Performance Evaluation** \\ \hline
[6] & Failure prediction in tube filling machine & RF, LR & Accuracy, MSE \\
[9] & Fault diagnosis in rotating bearings & PCA, SVM & Accuracy \\
[10] & Predict high-pressure fuel system failures based on machine & mRMR, LR, RF, XGBoost, SVM, MLP & Precision, Recall, F-Measure \\  & learning techniques and a multilayer neural network & & NCA, SVM, KNN, MLP & Accuracy \\
[11] & Classification of failures in transmission networks & & NCA, SVM, KNN, MLP & Accuracy \\
[24] & Diagnosis of bearing failures using Logistic-ELM & & LELM & Accuracy \\
[25] & Estimate the state of rotating components & SVM-based system & Accuracy \\  & & KNN, LR, SVM, ANN, DT, RF, EI, AB, & Accuracy, Precision, Recall, \\
[26] & Prediction of failure modes in beam–column joints & & GBDT, XGBoost & and AUC \\  & & & [PERSON]’s correlation \\
[27] & Prediction of failures in forced blowers & LR, SV, KNN, XGBoost, RF & Coefficient (MCC) and AUC \\
[28] & Predicting machine failures with joint reserve intelligence & DT, BN, LR, JRI & Accuracy, Precision, Recall, \\
[29] & Multivariate time series of vibration signals of a machine & Ensemble, LSTM, Mini Rocket, Resnet & Accuracy \\
[30] & Resource selection and SVM for maintenance failure prediction & RF, SVM, SMOTE & Accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of techniques, objectives, and metrics employed by some works addressing machine failure prediction.