## 1 Introduction  
In an industrial world where competition is increasing, the need to adapt to any change at any time has become a major necessity for every company. The world today is rethinking its factories since the appearance of Industry 4.0. Whether termed factories of the future, cyber factories, connected factories, or Industry 4.0, this mutation of the sector proposes a revolution of the industrial process based on new technologies and innovations and characterized by a fusion between the internet and factories [1]. In fact, the latter requires companies to be more responsive and involved in a policy of continuous improvement in order to provide customers with quality products while adhering to competitive deadlines, and with total control of their costs. At every link in the production and supply chains, tools and workstations are constantly communicating via the internetand virtual networks. Machines, systems, and products exchange information with each other and with the outside world. By opting for production optimization, manufacturers hope to produce products faster, cheaper, and in more environmentally friendly ways [2]. However, production stoppages due to equipment failures are more costly today either in terms of time or money [3]. These stoppages can be caused by bad alerts of the indicators given by the sensors. It is therefore necessary to use a monitoring system that can predict the life of the machines and, consequently, the time remaining for the breakdowns to occur. Since some equipment is at a high level of criticality for certain industries, such as the TA-48 multi-stage compressor plant, which has a moderate frequency of breakdowns and a high impact of equipment downtime on the production line, the downtime of this equipment generates a loss for the industry. Therefore, failure prediction has several advantages for the advanced planning of maintenance teams. Moreover, the classification of the type of failure has advantages in terms of investigating the cause of the failure for the maintenance teams during machine repairs [4]. According to [5], this type of intelligent maintenance is expected to become more common worldwide between 2022 and 2030. Modern approaches to maintenance vary according to the learning model used and the problems encountered by the equipment/machinery. Rather than how maintenance tasks are performed, the three models differ in terms of when they are performed [6]. * Reactive maintenance: Doing nothing until something breaks is the essence of this practice. For obvious reasons, large companies do not typically practice this as a maintenance strategy. Certain parts and components may be excluded from traditional maintenance schedules without intention, however. There is always a need for reactive maintenance after an event has occurred. * Preventive maintenance: Historically, this has been based on the performance of the engineers and operators, along with their knowledge and experience. The term refers to routine maintenance, periodic maintenance, planned maintenance, or maintenance based on time. Nevertheless, it can be inexact and may result in unnecessary or unnoticed maintenance, which can lead to costly maintenance being performed before it is necessary. There are predetermined times for preventive maintenance, often many months in advance. * Predictive maintenance (PdM): All enterprise assets can be integrated into a live ecosystem when Internet of Things (IoT) networks are used. Real-time data transmission and analysis allow maintenance protocols to be based on live asset conditions, rather than calendars. When and where maintenance is needed, predictive maintenance occurs in real time. * Predictive maintenance benefits and drawbacks: Predictive maintenance, as with any other maintenance strategy, has advantages and disadvantages (See Figure 1):  
Data analysis is what differentiates preventive maintenance from predictive maintenance.