## 5 Discussion
### RQ1
The perfect division of the 78 papers into two types of research in association with the exponential growth in the topic of research revealed the importance of the area, but it is still not mature, due to the still high proportion of validation output. **RQ3. **  
This question arose with the purpose of understanding the approaches used to find solutions in predictive maintenance for manufacturing equipment thanks to the advancing machine learning techniques. More low-level approaches, framework, architectures, and especially mathematical models and methodology are proposed in the reviewed literature. This is a signal of early research efforts in the area; however, some toolboxes [95] for the implementation of data-driven approaches in various manufacturing settings are developing, as a signal of growing interest in applicable solutions [96]. In particular, [PERSON] et al., 2014 [97], proposes a PdM tool that allows a flexible maintenance scheduling decision support system. It employs machine learning and regularized regression methods, by iteratively exploiting the new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks [98; 99]. Another example could be the industrial partner ProphecySensorlytics that deployed an Android mobile app, called OnSpot, based on SpotCheck, a prototype system that uses well-regularized deep neural networks to analyze sound, vibrational, and magnetic emissions of industrial machinery to provide non-invasive machine diagnostics, both for fault detection and to meter the day-to-day mode of operation of the machinery [100; 101]. **RQ4. **  
Failure of manufacturing equipment due to performance degradation of certain components can be evaluated, and their degradation can be predicted through early condition monitoring and data analysis. This can be achieved through data acquisition from sensors, which have become smarter, smaller, easier to implement in existing systems, and cheaper and more reliable [102]. A sensor converts physical values into electrical values (voltage, current or resistance). Usually, one sensor measures one mechanical value, for example, the mechanical values of acceleration, pressure, flow, torque, and force [103]. With this mechanical value, one can interpret the vibration data, acoustic data, temperature [104], humidity, weather, altitude, etc., and show a summary of the components and the mechanical values acquired [34]. Therefore, this question aimed to discover the most researched applications in smart manufacturing for predictive maintenance and the most monitored type of sensor data utilized. With 30 papers, the results highlight manufacturing machinery as the most discussed topic in publications, particularly in relation to the availability of vibration signal monitoring. In fact, manufacturing machinery is the heart of a smart manufacturing with the aim being to prevent failures before they occur, thus avoiding loss of production, quality, downtime and cost; it was also the most comprehensive topic individuated in the study. When mechanical equipment sets fail, the vibration phenomenon is obvious and universal. The equipment produces vibration as long as it is running, and any change in each part will have an impact on the vibration signal. Vibration signal contains the most abundant information, and the measurement is more convenient. Vibration signal is the result of the interaction of various factors with irregular characteristics, such as chaos, typing, nonlinear, and data are multi-scale [38]. **RQ5. **  
This question arose from the interest in highlighting the most relevant machine learning approaches used for predictive maintenance and linking each of them with the tasks for which they are employed. The analysis evoked by the output charts highlights supervised learning, applied for classification, as the most commonly used approach for prediction through machine learning techniques.