# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 1 Introduction  
Maintenance is a relevant factor for the competitiveness of an organization, since the actions carried out at this level have a direct impact on aspects such as the cost, deadlines and quality of the products produced or services provided [1, 2]. Maintenance is a support to the operational area of a company and cannot be dissociated from it, given the implication it has in terms of the efficiency of productive assets. These two areas, operation and maintenance, must operate in parallel in order to guarantee the availability and the rapid response of human and material resources to operational problems, thus ensuring the achievement of objectives with the maximization of available resources. Thus, it becomes important not only to achieve the proposed objectives, but also to achieve them with the minimum consumption or use of resources [2].  
It is in this context of constant transformation that Industry 4.0 arises [1]. Industry 4.0 implements the tools provided by advances in information and communication technologies in order to increase the levels of automation and digitalization in industrial and production processes [1, 3]. The objective is to manage the entire value chain process, improving production efficiency and creating superior products and services. One of the key points of this technological evolution is the data, which is now more easily read, processed, stored, analyzed and shared between machines and human beings [4]. Additionally, the Internet of Things (IoT) is defined as an ecosystem in which the objects and equipment inserted in it are equipped with sensors and other digital devices, thus being able to gather and exchange information with each other, in a networked system [1].  
In recent years, a drop in cost and an increase in the reliability of sensors, data transmission and storage devices have promoted the emergence of condition monitoringsystems for industrial equipment. Simultaneously, the IoT allows real-time transmission of this information about the conditions of the systems captured by different monitoring devices. This development offers an excellent opportunity to use condition monitoring data intelligently within predictive maintenance, combining the ability to collect data with an effective and integrated analysis of it [5].  
In this sense, the potential of Artificial Intelligence tools, more specifically Machine Learning, allows us to aim for an improvement in the availability of systems, reducing maintenance costs, increasing operational performance and safety, and the ability to support decision making in relation to the ideal time and the ideal action for carrying out the maintenance intervention [4; 5; 6; 7].  
Machine Learning can be defined as \"the field of study that gives the computer the ability to learn without being explicitly programmed\". It can be said that \"Machine Learning algorithms use computational methods to learn information directly from data without using predefined equations as a model\" [8].  
The main objective of this work is to apply Artificial Intelligence tools, more specifically Machine Learning, to a set of data, coming from different sources, available online [9]. Furthermore, we seek to analyze the specificities of this implementation and the definition of methodologies, in order to provide information and tools to the maintenance area.