# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 2 Machine Learning Process Workflow and Techniques  
One of the main difficulties of applying a Machine Learning process to maintenance data is the choice of the right workflow, as in literature there are many different approaches to this problem, depending on the origin of the data and the objectives of the analysis [10; 11; 12; 13]. As the different applications are difficult to compare, in this work it was decided to explore a simple but complete framework and to use a set of data that can be used by other researchers, as the data set is publicly available. In the present work, the workflow presented in [14], represented in Figure 1, will be followed. From our point of view, a Machine Learning project must always start with the establishment of rigorous and clear definition of the objectives, since such a system fulfills a very specific task and the definition of vague objectives can mean that the model developed is not able to predict exactly what it is intended to. Quite possibly, the most important part of a Machine Learning project is the ability to understand the data used and how it relates to the task we want to solve. It will not be effective to randomly choose an algorithm, use the data set we have available and expect good results [15]. It is necessary to understand what is happening in the data set before starting to build a model. When building a Machine Learning solution, we must answer or at least keep in mind the following questions: What questions are we trying to answer? Does the available data set allow you to answer these questions? What is the best way to paraphrase my question as a Machine Learning problem? Is the available data set sufficient to represent the problem we are trying to solve? What features (or attributes) have been extracted and will they be able to lead to the correct predictions? How to measure the success of the application of Machine Learning? How will the Machine Learning solution interact with the rest of the process? Machine Learning algorithms and methods are only part of a larger process for solving a specific problem, and it is important to keep that in mind. Sometimes, a lot of time is spent building complex Machine Learning solutions, only to discover in the end that they do not solve the problem that we were waiting for [16]. By deepening the technical aspects of Machine Learning, it is easy to lose sight of the final goals. It is important to keep in mind all the assumptions created, either explicitly or implicitly, when building Machine Learning models. Typical machine learning algorithms, such as the hidden Markov models [17], hidden semi-Markov models [18], self-organizing neural network [19], SVM [20], multimodal deep support vector classification [21], deep random forest [22], genetic algorithms [23], blind source separation [24], fuzzy logic [25], k-nearest neighbor algorithms [26] and Bayesian algorithms [27], etc., have been applied in fault diagnosis of dynamic equipment. To the best of our knowledge, there are two main categories of approach for fault diagnosis of gearboxes: Data-driven and physical model-based methods. Although these methods have been successfully applied in many applications, it is very difficult to know what is the best algorithm to apply to a particular data set. A systematic review of the scientific literature was carried out in [28], from which it is possible to draw several conclusions. Predictive maintenance strategies are being applied to the most diverse equipment, in multiple areas. The equipment where these methods are applied include, but are not limited to, turbines, engines, compressors, pumps. About 89% of the published papers use a set of real data, with 11% using synthetic data. Regarding the use of Machine Learning algorithms in scientific publications, the most used is Random Forest (RF)--33%, followed by methods based on Neural Networks (NN), such as Artificial  
Figure 1: Machine Learning process workflow [14].