# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 3 State of the Art  
This section provides a brief review of the main approaches that have been recently used for PdM. [PERSON] et al. [14] provided a categorization based on the optimization criteria. In this direction, [PERSON] et al. [15] presented an approach that minimizes the cost of the Remaining Useful Life (RUL) of the system, although they showed that it is also possible to define an ad hoc cost model. Other multi-objective optimization works seek to optimize multiple metrics simultaneously in order to achieve a better balance between objectives. In addition to the aforementioned, they employ metrics such as risk, security or viability. Generally, it is impossible to obtain optimal values for all objectives at the same time, which is why a wide variety of multi-objective models have been developed [16; 17; 18]. Other research define metrics to maximize the reliability and availability of the system. For example, [PERSON] et al. defined the probability of a system to be in a normal operating state in a given time interval [19] while [PERSON] and [PERSON] [20] defined the probability that the system is operational. Other works focus on the type of approach used to solve the problem. Some use expert knowledge and deductive reasoning processes. For example, there are works based on ontologies [21], on rules [22] or on models that try to link the physical processes of a system with mathematical models, such as Gaussian models [23], linear systems models [24] or Markov models [25]. In the arena of ML techniques, Artificial Neuronal Networks (ANN) [26] and decision trees [27] (including the Random Forest algorithm [28]) have been used, as well as Support Vector Machines (SVM) in terms of both supervised [29] and unsupervised [30] learning. Finally, the k-Nearest Neighbor Technique (k-NN) is one of the most common for fault classification [31], for prediction of useful life time (RUL) [32] and early detection [33]. One of the most used Deep Learning (DL) techniques is the Autoencoder Neural Networks, for which its output layer seeks to reproduce the data presented in its input layer after having gone through a dimensional compression phase, allowing the creation of robust models against noise [34]. Recurrent Neural Networks (RNN) [35], based on Long Short Term Memory Networks (LSTM) cells [36], have also been used, which can learn longer-term dependencies. These types of networks are very powerful for sequence analysis. In the specific case of engines, a few works have comprehensively addressed this task. Engines in industrial environments are usually monitored by a large number of sensors that measure physical aspects of their components such as vibrations, temperatures or pressures. By analyzing the values collected by these sensors, PdM techniques allow predicting the appearance of failures in their components. Due to the great differences that exist between engines (design, behavior and objective), there is no universal solution that solves the problem of PdM for any engine. The particularities of each engine may or may not permit the use of certain techniques in each case. In the approach presented by [PERSON] et al. [37], an innovative approach for diesel engines is applied based on ecological variables by using Hidden Markov Models. These variables are managed by taking into account the environment and, because of this, they are called ecological variables. Later, [PERSON] et al. [38] developed a complete framework to predict the level of component degradation in diesel engines by using supervised learning. By using LDA-Naive Bayes classifiers, the system is able to classify a state as normal or failed and determine which failure mode it corresponds to. Recently, [PERSON] et al. [39] applied DL techniques to prognose the remaining useful life of a turbofan engine. The proposed model consists of a network with a one-dimensional convolutional neural network (CNN), LSTM and bidirectional LSTM. The system addresses the problem of high dimensionality by employing dimensionality reduction techniques.