## 4 Results
#### RQ3.What type of contributions are resulting from publications? The type of contribution is reported in Figure 11 over the 78 papers. The contributions to the field mainly consisted of models. Many proposed ML algorithms to predict equipment reliability or failure. The majority of ML-based publications were of methodology proposal for diagnostic, anomaly detection, and prediction [83]. Twelve publications reported about conceptual framework platforms that describe architecture for software implantation [84,85]. Early research efforts focused on tool proposal, and just one publication was in the area of process mapping. All proposals started by analysing the production process to discover correlations between sensor data from equipment. Data were collected and generally stored in a local cloud [86]. The learning part of the PdM technique was implemented for system's monitoring, prediction of quality in products, and scheduling decisions for flexible maintenance of equipment. Figure 12 illustrates the distribution of publications. A growing interest manifested in the last 2 years in which the publication of at least one paper for each category was evident. RQ4.What class of equipment is a candidate to be mapped for smart manufacturing and what is the relation between equipment and type of data under investigation? Seventy of 78 papers reported about using PdM while applying ML. This included both validations (with simulations on open source test data set) and evaluations (with proposed  
Figure 11: Comparison of contribution type. Figure 12: Trend in publication by contribution type. real industrial applications). Figure 13 reports about the type of equipment and process where successful learning efforts were registered. Manufacturing machinery was the most prominent area of manufacturing for this ML application, followed by motor and rotary machines. The most relevant sector making use of ML was semiconductor manufacturing. Forty-two papers presented, in case study, solutions for prediction of equipment degradation. They all specified the type of source data monitored (Figure 14). Vibration analysis was the most used type of data for machine learning. Acoustic signals were also commonly combined in data with force, torque, and speed values [87,88]. Dimensional data and surface roughness, energy consumption, voltage, and current measurement were monitored for manufacturing machinery components [89]. Few papers reported on the use of thermal data or digital images and magnetic emissions. **RQ5.What machine learning strategy is the most frequently applied for predictive maintenance and for which tasks is it setting? **  
Figure 15 reports on the type of ML algorithm most prevalent within the literature in the database. Supervised learning was the most common (53%) type of algorithm as reported by the literature. Eighteen percent of the literature reported ensemble methods; 11% in the class of unsupervised as well as inductive approach; 3% of application reported about a semi supervised implementation; 2% were the implementation of reinforcement and instance-based techniques. Figure 14: Comparison of investigated equipment and type of monitored data. Figure 13: Number of publications for each case study. Fifty-five of the 78 papers compared different ML approaches in order to demonstrate the superiority of the method put forward. Figure 16 shows the comparison of different machine learning approaches and tasks. The supervised approach reported successful implementation for classification of rough data, time series classification [90], and regression components [91]. The semi-supervised and ensemble approaches can be used for both classification and regression, while the reinforced approach is only used for clustering [92]. Instance-based approach reported successful application in classification. Inductive learning approaches was the only one that manifested consistent application for classification, regression, and clustering.