## 1 Introduction  
The increasing use of sensors in industries, driven by the rise of intelligent sensor systems in Industry 4.0, represents a significant transformation in production and manufacturing processes. These sensors connect devices and systems, enabling machine-to-machine communication to monitor systems and equipment in industrial facilities. Sensors improve product quality, reduce production costs, and increase operational efficiency by processing data locally and making fast, accurate decisions. Furthermore, the evolution of sensor technologies and their integration with other innovations, such as big data, artificial intelligence, and cloud computing, drives Industry 4.0 towards smarter and more automated production. This paradigm shift in the industry offers promising commercial opportunities as sensors become essential in driving innovation and market competitiveness [1]. In this scenario, machines and managers face daily challenges related to massive data entry and customization in the manufacturing process. Thus, predictive maintenance is a fundamental approach to anticipating failures through advanced analysis, optimizing process efficiency, and promoting proactive resource management, contributing to operational excellence in manufacturing operations [2; 3; 4; 5; 6]. The use of advanced techniques to develop prediction models has become crucial to taking advantage of the growing volume of data collected in industries. Machine learning techniques shine in this scenario, using algorithms to examine data in real time and predict output values [7]. It enables meticulous analysis and empowers strategic decision-making based on extensive data sets, addressing the challenges of variety, speed, and volume of information collected in industries [2]. Considering this, numerous studies have used artificial intelligence techniques to extract valuable insights, helping to make decisions related to industry machine failures. In [3], the authors have synthesized the most recent works on predictive maintenance, highlighting that 32 studies used actual data to develop predictive models related to maintenance, while two opted for synthetic data. The analysis reveals that 33% of researchers chose Random Forest (RF), 27% opted for Artificial Neural Networks (ANNs), 25% decided on Support Vector Machines (SVMs), and 13% used K-Means. Feature selection is a fundamental tool in data analysis, machine learning, and data mining, as it can identify the most relevant features, allowing a deeper understanding of the problem under study and helping data professionals and computer scientists understand which aspects of the data have the most significant impact on the model's predictions. Furthermore, by highlighting the most important features, it is possible to simplify the model, reducing its complexity and making it more computationally efficient [2; 3; 4; 5; 8]. This step involves choosing the most relevant or informative dataset characteristics to be used in model construction, improving model performance, and reducing dimensionality, interpretability, and computational savings, among other benefits [9; 10; 11; 12]. Choosing adequate techniques for feature selection and developing prediction models for machine failure classification are critical points in providing accurate information that assists decision-making processes. Furthermore, these techniques could be applied with the aim of identifying the most useful sensors and providing relevant information for analysis. There are still many other questions underexplored in the literature on automatic identification of industrial machine failures. In this context, this study is guided by the following research questions:  
1. Which feature selection techniques are most used for the machine failure prediction problem? 2. Which feature selection techniques will provide more accurate models for predicting machine failure? 3. Which machine learning techniques are most adequate for the machine failure prediction problem? 4.