## 2 State of the Art  
The remarkable rate at which new technologies have been developed over the past few years has substantially impacted the new industrial landscape, giving way to what is called \"Industry 4.0\" (14.0) in 2011 [24]. Industry 4.0 involves technologies such as Cyber-Physical Systems (CPSs), the Internet Of Things (IoT), cloud computing technology, and big data and advanced analysis techniques [25]. Within the scope of Industry 4.0, the increase in information provided by advancements such as the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) has facilitated the improvement in factory monitoring and maintenance systems, resulting in the development of new techniques that use the massive quantity of data obtained during the operation process to make decisions that help reduce costs [2; 23; 26]. Some authors have pointed out that sensors will include \"enterprise information systems\" that will use big data techniques in the future [25]. Large-scale data collection allows for the creation of data-based models that make much more accurate predictions and allow processes to be managed more efficiently. Data-based algorithms are designed to characterize machinery using mathematical models that provide information on a process, and they are also designed to forecast new parameters of interest by monitoring a system [19; 27]. Data-driven models apply machine learning, soft computing, and statistical theory and can achieve high accuracy in less time than other approaches [28]. Therefore, their use for predictive maintenance is becoming increasingly relevant, especially with respect to multidimensional data [10; 19; 29; 30]. The authors of [9] provide a classification of methods for predictive maintenance and describe their advantages and disadvantages. The authors of [31] published an interesting review of techniques useful for the diagnosis of rotating-machinery faults, serving as a valuable source of knowledge. Detecting bearing failures and evaluating motor efficiency are two of the most relevant aspects of predictive maintenance for this type of system [32]. In this field, some researchers have used the analysis of vibration values under real operating conditions to predict bearing failures [33]. As a result, relationships between bearing state and vibration frequency have been identified, making this approach an effective technique for PdM [34]. More recently, artificial intelligence and machine learning techniques were used to create models of data-driven motor pumping systems and roller systems [10; 35; 36; 37; 38]. Furthermore, recent studies [39; 40] suggest that long- and short-term memory (LSTM) together with recurrent neural networks (RNNs) can better establish long- and short-term dependence in a dataset. This suggestion is assessed in our case study in order to gauge behavior. Despite the fundamental engineering principles involved in failure analysis theories and how the corresponding technology enables the monitoring of interesting variables, the focus is still on the proposed frameworks, as they become critical for successful implementation in practical applications. An interesting framework dealing with PsM was designed by the authors of [41]; they propose incorporating a knowledge maintenance base that contains different stored models. The recommendation system comes from an aggregator of different sources, such as probabilistic measures, similarities based on process variable distances, textual analysis, and other sources. However, the implementation of such a framework seems rather problem-dependent, and the feedback to the learning-based models is indicated but not clearly depicted. The proposal presented in [42; 43] includes a diagnostic system driven by the configurations of a rule-based knowledge system. Such sets of controls help to identify errors in prediction and provide feedback addressing such errors. Although suggestive, this rule-based system lacks automation capabilities because it requires strong support from a central office.