# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 1 Introduction  
Modern processors, computers and high speed networks make it possible to acquire, transfer and store large quantities of data in real time. Acquisition and combination of data from different sensors makes it possible to gain an insightful view of the state of factories, industrial plants and other facilities. Large datasets can be constructed, stored and processed using information technologies such as Big Data, cloud computing, cutting-edge computing, and artificial intelligence tools. The Internet of Things (IoT) is a recent concept, which provides many benefits to different areas, such as maintenance and production management, because it facilitates the automation of tasks such as monitoring and maintenance. This results in the popularization of intelligent systems, which are highly dependent on Big Data [1] and are an important area of study, since they offer the tools and methods to acquire and process large volumes of data such as historical production processes, including many production and operating parameters. Modern time-series and other data analysis techniques have been used with success for different tasks, such as freeway traffic analysis [2] and additive manufacturing [3]. Different approaches have also been proposed in the field of predictive maintenance [4, 5]. Satisfactory results were obtained using Big Data records as support for PCA models, which resulted in a warning alarm several days before a potential failure happened [6]. Life cycle optimization has been an important concern for decades. A physical asset with proper maintenance will have a longer useful life with a greater return on investment for the organization [7]. Predictive maintenance requires good quality data. The information that is extracted from the online or offline data must be reliable, and so the results must be good enough to justify the investment in data collection and analysis. The process starts from thecorrect calibration of the reading sensors and equipment [8]. The data are then stored and processed using different models, such as Principal Component Analysis (PCA) and Neural Networks [9]. Maintenance planning involves the use of several algorithms, the most common being time series [10]. Maintenance of equipment in the industry becomes a sensitive and important point that affects the equipment's operating time and efficiency [5]. This makes maintenance one of the strategic points for the development and growth of competitiveness vis-a-vis competitors. [PERSON] and [PERSON] studied the total expected cost of maintaining a flotation system, including the cost of lost production, the cost of repairs, and the cost of standby machines [11]. [PERSON] et al. propose the integration of Artificial Intelligence (AI) systems, which will bring many benefits in diagnosing condition problems of industrial machines [12]. They highlight the viability of AI that combines the use of Artificial Neural Networks (ANNs) with a dynamic time series model, for fault diagnostics, to optimize the equipment intervention time. [PERSON] et al. demonstrated that neural networks can be a great technology in the support and decision making of large and small companies [13]. There is a trend to use those tools in predictive maintenance systems with the aim of making the prediction systems more intelligent [14]. According to [PERSON] et al., there is a great effort in the development of predictive models for application in predictive maintenance [15]. [PERSON] and [PERSON] apply Long Short-Term Memory (LSTM) neural network approaches to predict real production data, obtaining satisfactory results, superior to conventional models [16]. In their study to improve maintenance planning to minimize unexpected stops, they apply a new method that consists of the combined use of decomposition in empirical mode of ensemble and long-term memory. Their results showed a performance superior to other state of the art models. LSTM networks use several ports with different functions to control neurons and to store information.