# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 2 Related Work  
### Predictive Maintenance  
In smart industries, predictive maintenance is one of the most used techniques to improve condition monitoring, as it allows one to evaluate the conditions of specific equipment in order to predict problems before failure [18]. For good performance of predictive models, it is important that the sensor data collected are of good quality. Deep neural models have been used with success to improve prediction for condition monitoring of industrial equipment.  
[PERSON] et al. [19] use a model of long short-term recurrent neural networks (LSTM-RNN) with the objective of predictive maintenance based on past data. The main objective of predictive maintenance is to make an accurate estimate of a system's Remaining Useful Life (RUL). Traditional systems are only able to warn the user when it is too late and the failure occurs, causing an unpredictable offline period during which the system cannot operate properly with a consequent waste of time and resources [20].  
In order to assess the condition of a system, the predictive maintenance approach employs sensors of different kinds. Some examples are temperature, vibration, velocity or noise sensors, which are attached to the main components whose failure would compromise the entire operation of the system. In this sense, predictive maintenance analyzes the history of a system in terms of the measurements collected by the sensors that are distributed among the components, with the objective of extracting a \"failure pattern\" that can be exploited to plan an optimal maintenance strategy and thus reducing offline periods [21]. In a case related to the steel industry, [PERSON]. [22] used neural networks for classification of maintenance activities, so that interventions are planned according to the actual status of the machine and not in advance. Using multiple neural networks to identify status and RUL at a higher resolution can be very difficult, as the system can predict failure classifications and may not be able to recognize neighboring states. One limitation arises from the need for maintenance records to label datasets and the need for large amounts of data of adequate quality with maintenance events, such as component failures.  
When systems start to be very complex or the number of sensor measurements to manage is very large, it can be difficult to estimate a failure. For this reason, in recent years, machine learning techniques are used more and more to predict working conditions of a component. [PERSON] et al. [23] propose several approaches to machine learning such as support vector machines (SVMs), decision trees (DTs), Random Forests (RFs), and others that show which technique has the best performance in RUL forecast for turbofan engines.  
A major challenge in operations management is related to predicting machine speed, which can be used to dynamically adjust production processes based on different system conditions, optimize production performance and minimize energy consumption [24]. [PERSON] and [PERSON] [25] use a deep convolutional LSTM encoder-decoder architecture model on real data, obtained from a metal packaging factory. They show that it is possible to perform combinations of LSTM with other networks to significantly improve the results.