## 1 Introduction
Indeed, three components make up PdM, which allow technicians to monitor the condition of equipment and alert them to upcoming failures [7]:  
1. Sensors installed in the machine generate real-time data on its health and performance;
2. The Internet of Things (IoT) connects machines, software, and cloud solutions, enabling the collection and analysis of huge amounts of data;
3. All the processed data are fed into predictive models, which can predict failures based on that information. As technology has advanced throughout industrial revolutions, the impact on maintenance strategies and equipment performance has changed, as illustrated in the following table (See Table 1). Industry 4.0 is transforming how businesses create, enhance, and distribute their products. By enabling equipment intercommunication through the Internet of Things, big data, computer intelligence, and decision-making systems, it has fundamentally changed the in dustrial and manufacturing world [5]. Industry 3.0 has been disrupted by the introduction of computers due to new technologies [8]. Industry 4.0 enables autonomous decision-making by connecting and communicating with industrial machinery and computers today and in the future. The Industry 4.0 model suggests the use of multidisciplinary technologies to achieve these advantages and effective factories. Although some of these have been studied for a while, they have not yet reached the point where they can be widely used in industry [9] (p. 0). In fact, pieces Industry 4.0 equipment are capable of automatic communication with each other, enabling them to coordinate with other remote systems as well as with one another via the internet. As predictive maintenance aims to determine the probability of machine failure occurring, this study attempted to anticipate the failure and determine the type of possible failure by applying machine learning algorithms. This led us to predict the necessary maintenance to be carried out on TA-48 compressors to avoid stoppages and blocking production [10]. According to the literature [11], the most important costs come from downtime due to equipment failures, such as compressors. Thus, maintaining production equipment is a key issue for productivity as well as product quality. Equipment maintenance is a combination of technical, administrative, and managerial activities. To the best of the authors' knowledge, there is a lack of investigation into automatic solutions through the application of predictive maintenance approaches and Industry 4.0 concepts. The current study provides automatic solutions through the application of predictive maintenance approaches and Industry 4.0 concepts. The originality of this work lies in the construction of a failure prediction model based on machine learning methods. Indeed, the best solution to address this problem is to implement a machine learning model that aims to predict occasional downtime by searching and testing different algorithms through the preparation and processing of data collected by the sensors related to the compressor machine. Additionally, to help professional users to make decisions, the results \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Industrial Revolution** & **Industry 1.0** & **Industry 2.0** & **Industry 3.0** & **Industry 4.0** \\ \hline Technological Innovation & Mechanization, steam power & Mass production, electrical energy & Automatization, computer power & Digital solutions, IoT cloud systems \\ Maintenance Policy & Reactive maintenance & Preventive & Preventive & Predictive \\ Maintenance Policy & & maintenance maintenance & maintenance & maintenance \\ Technology & Visual Inspection & Instrumental Inspection & Sensor monitoring & Sensing data and predictive maintenance \\ Overall Equipment & \textless{}50\% & 50–70\% & 70–90\% & \textgreater{}90\% \\ Effectiveness & \hline \hline \end{tabular}
\end{table} 
Table 1: The industrial revolution. Figure 1: Advantages and disadvantages of predictive maintenance.