## 4 Results
#### RQ3.What type of contributions are resulting from publications?
The relation between application area for prediction and the type of learning approach is reported in Figure 17. In dealing with fault prediction for electrical and electronic components, semiconductor manufacturing process equipment, rotary machines, and piping structures, the use of supervised learning is put forward. Manufacturing machinery components can be investigated using different learning modes. In this case, the supervised approach was widely applied with no evidence of semi-supervised proposals. Analysing motor components, turbines, and pumps, there was generally the possibility to use different types of learning approaches. Motors make use of unsupervised, semi-supervised, and supervised (major evidence) learning. Turbines make use of supervised, unsupervised, and inductive approaches. Fans and blowers applied unsupervised and supervised learning strategies. Figure 16: Comparison of different machine learning approaches and tasks. Figure 15: Popularity of machine learning approach types. **RQ6.Which machine learning strategy is the most frequently applied for predictive maintenance and for which tasks is it setting? **  
Eighty-two machine learning methods were selected for analysis. Several papers reported successful implementation of algorithms matched with ensemble approaches. This is reported in tests as the best solution for prediction in specific problems. Figure 18 displays how the popularity of ML for PDM in SM was gaining a rising trend over the analysis period. At least one successful ML application was published each year for support vector machine algorithms and neural networks. These two approaches were the only ones reported in 2015. In 2014 and 2016 few applications, characterized by different methods, were presented. The growing interest in ML resulted (see data for 2017 and 2018) in a diversification of methods, with multi-methods composition not evident. Notwithstanding, NNs remained the most prominent sources of learning methods. Thirty-three algorithms of 82 proposed neural network implantations, 15 for random forests algorithms, and 14 for SVM, with 75% of publications based on these methods alone. Figure 19 displays the  
Figure 17: Types of machine learning approaches used for each type of investigated equipment. Figure 18: Popularity of machine learning methods by year. percentage of learning methods over the case history. A relevant portion was assigned to decision trees, linear regression, and k-Means methods. Forty percent of publications (36 different papers) reported about NNS. The most significant contribution was from DL algorithms (highlighted with green clouds in Figure 20). DL for ML constituted 67% (24 proposed methods) of all NNs case test. With 22%, long short-term memory covered the prominent class of NNs methods. There was a 19% usage of deep neural network and an 11% of convolutional neural network. Artificial neural networks and Bayesian neural networks were implemented in 8% of publications. Figure 21 reports about the machine learning methods utilized for each type of investigated equipment. While analysing piping equipment, there was just one proposal covering the application of liner regression [93]. Electrical and electronic equipment reported three successful applications of linear regression and neural network algorithms. Data from fan and blower, motor, and semiconductor manufacturing processes are studied through  
Figure 19: Percentages for each class of learning methods. Figure 12:0 Popularity of neural network algorithms.