## 2 Data and Methods  
When planning a predictive maintenance project, a life cycle must be followed. Data science and machine learning constitute the process of structuring the solution of a problem using data. An example of predictive maintenance is shown in Figure 2[5]:  
Step 1. Analyzing the project's requirements: To begin, one must understand the business elements, problems, and constraints of a project. This step requires a thorough understanding of both the equipment and systems that are relevant to the project, as well as how they operate. It involves defining the physical quantities to be measured, selecting the sensors, and installing them if necessary. The next step involves defining a list of failure types that may occur.  
Step 2. Understanding, collecting, and preparing data:  
* Data collection: Data can be collected and stored in databases using the sensors in the equipment.
* Understanding: During this phase, the data to be analyzed are determined, the quality of the data is identified, and its meaning is related to the data.
* Preparation: In this sub-step, related data are selected; data are integrated by merging datasets; missing values are cleaned and managed by deleting them or by calculating them from related datasets; erroneous data are managed by deleting them; outliers are checked and processed; updated data and enhanced features can be obtained by feature engineering, the data can be formatted in the desired structure, and unnecessary columns and features can be removed. Data preparation takes a lot of time and work, typically between 70% and 90% of the project's total time.  
Step 3. Data modeling: In data analysis, data modeling plays a pivotal role. The model receives input data (preparation), and output data are provided. Whether it is a classification problem, a regression problem, or a clustering problem, the first step is to select an algorithm to solve the problem. Different algorithms must be evaluated and parameterized in order to create a model.  
Step 4. Evaluation and deployment:  
* The developed model should not be biased and should be both generalized and have good performance.
* During the deployment of the model, each step must be given proper attention, time, and energy.  
Figure 2: Predictive maintenance workflow.  
Step 5. Decision-making: Generally, operators use decision-making processes to decide how to resolve problems. Taking a step-by-step approach to making decisions can assist in making informed, thoughtful decisions that will make a positive difference in the short- and long-term.  
In the current study, the steps of the methodology of predictive maintenance workflow were followed, allowing us to explore and process the data intended for the training of the model. Our dataset is a combination of several merged csv files. Indeed, for each variable or attribute recorded by the sensors, we had a csv file with two columns, the first being reserved for the dates and the second for the recorded values of the variable. Thus, a comparative study of the various prediction algorithms was carried out to determine the final choice, which is based on the implementation of the LSTM neural networks. In addition, its performance was improved as the data sets were fed and incremented. Any solution is expected to be improved to maintain its usefulness and performance. Finally, the deployment of the model will allow operators to know the fault times of the compressor and subsequently ensure a minimum downtime rate by making decisions before failures occur.