# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 5 Predictive Maintenance Workflow  
In order to properly plan a predictive maintenance project, a life cycle is followed containing five (5) essential steps. These are the approaches to structure the solution of problems that require data science and machine learning. Figure 8 illustrates the life cycle and workflow of a predictive maintenance project [55; 56]:  
Figure 8: Workflow of predictive maintenance. Step 1. Understanding the project's needs:  
The first step consists in understanding the business elements and problems of the project as well as the constraints to overcome. In this step, it is necessary to fully understand the system and the equipment that are of interest to solve the project as well as the operation of the equipment. This implies the definition of the physical quantities to be measured, the choice of the sensors, and their installations if needed. In this step, we must also define a list of types of failures that we can face. Step 2. Data collection, understanding, and preparation:  
Data collection: The sensors available in the equipment have the capacity to collect data and transfer them to the database. Understanding: This phase involves determining precisely what data to analyze, identifying the quality of the available data, and relating the data to its meaning. Preparation: This sub-step includes elements such as selecting related data; integrating data by merging datasets; cleaning and managing missing values by deleting missing values or imputing them with related data; managing erroneous data by deleting erroneous data; checking outliers and processing them; using feature engineering to build new data and obtain new features from existing data; formatting the data into the desired structure; and removing unnecessary columns and features. Data preparation is the most time-consuming step, accounting for 70-90% of the total project time, but it is the most important step in the entire life cycle. Step 3. Data modeling:  
Data modeling is considered the heart of data analysis. The model takes the data prepared in the previous step (data preparation) as input and provides the required output. This step involves choosing the right type of algorithm, whether it is a classification problem, a regression problem or a clustering problem. The testing and parameterization of different chosen algorithms constitute a model. Step 4. Evaluation and deployment:  
* Step 4.1--Model Evaluation: Ultimately, we need to evaluate the model by measuring accuracy (how well the model works, i.e., does it accurately describe the data) and relevance (does it answer the original question that needs to be answered). We also need to ensure that there is a good balance between performance and generalization, which means that the model created should not be biased; it should be a generalized model. * Step 4.2--Deploying the model: The evaluated model is finally deployed in the required format and channel. This is the last step in the predictive maintenance life cycle that is related to the data. Each stage of the life cycle explained below should be carefully considered. If one step is not executed well, it will affect the next step and all the effort will be wasted. For example, if data is not collected properly, information will be lost, and a representative model cannot be created. If the data is not properly cleaned, the model will not work properly. If the model is not evaluated correctly, it will fail to give a perfect output in the real world. From understanding the project to deploying the model, proper attention, time, and energy must be given to each step. Step 5. Decision making:  
In general, the decision-making process helps operators solve problems by deciding on the best path forward. Using a step-by-step approach is an effective way to make thoughtful, informed decisions that have a positive impact on short- and long-term goals. * Step 5.1--Identifying the decision: The first step in making the right decision is to recognize the problem.