# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 6 Predictive Maintenance Models
### Remaining Useful Life  
The remaining useful life (RUL) is the period of time that a piece of equipment is likely to operate before it needs to be repaired or replaced. Depending on the system, this period can be represented in days, miles, cycles or any other quantity. Service life prediction provides early warnings of failure and has become a key component of prognostication and systems health management [71]. It enables maintenance planning, optimizes operating efficiency, and avoids unplanned downtime [72]. The estimation of RUL is a real challenge because the relevance and effectiveness of maintenance actions depend on the accuracy and precision of the results obtained \(\mathbf{RUL}=\mathbf{tf}-\mathbf{tc}\). A confidence measure should also be constructed to indicate the degree of certainty of the RUL as presented in Figure 13.  
In the last decade, many tools and methods have been proposed as shown in Figure 14. Generally, two types of methods are used to predict equipment life: physics-based methods and data-based methods [73]. The physics-based approach uses mathematical principles such as statistics and probability, which attempt to create accurate mathematical models from prior knowledge to predict equipment life. However, relying on prior knowledge can lead to insufficient generalization. Data-driven methods aim to explore the potential relationship between monitored sensor data and their RUL value based on historical data. The RUL of equipment is a random variable that depends on the current age of a machine/equipment and its health information. It gives the remaining time for a machine, component or system before it is no longer functional. In the research, there has been an interest in the use of remaining life estimates within industries [74]. In the following, we will present articles that have dealt with this subject.  
In order to improve the prediction of RUL, [PERSON] et al. [75] used convolutional neural networks (CNN) and hierarchical Bayesian regression. The conclusion is improved by  
Figure 14: A brief explication of RUL models.  
Figure 13: Representation of the residual life.  
defining a min-max scaling for the RUL and giving a good prediction accuracy of the CNN. However, the combination of CNN with hierarchical Bayesian regression gave a higher rate if just CNN is used for RUL prediction. [PERSON] et al. [76] opted to use the LSTM long-term short-term memory for motor prediction; the results give a success rate of 91/100 for failure prediction. Indeed, during their experiments, the authors of [77] used three neural networks that differ by the number of layers of LSTM used for the same dataset. They found that adding an LSTM layer increases the accuracy of the training and validation set. The use of deep learning methods has therefore become essential in the last decade. The author of [78] used two datasets to test neural network models on over 100 motors to predict their remaining lifetimes. The results of the MAE and RMSE metrics were compared to three other topologies. Here, the author found that the recurrent neural network model outperformed the other three topologies for the first dataset used while for the entire second dataset, the error was within the acceptable range for the proposed topology.