## 4 Machine Learning (ML) Algorithms
### Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author/Year** & **Algorithm Used** & **Findings** & **Parameters** & **Platform Used** & **Data Source** \\ \hline
[132] [PERSON] et al., 2019 & Feature-based transfer neural network (FTNN), convolutional neural network (CNN) & Identify the health states of bearings used in real-case machines BRMs & Raw vibration data & Real data \\ \hline
[131] [PERSON] et al., 2019 & Adaptive order particle filter (AOPF) & Estimate RUL & Vibration & Synthetic data \\ \hline
[132] [PERSON] et al., 2019 & Sparsity and neighbor- hood preserving deep extreme learning machine (SNP-DELM), extreme learning machine (ELM-AE) & Bearing fault diagnosis & Number of balls, Pitch diameter, Ball diameter, Outer-race fault order, Inner-race fault order & MATLAB & Real data \\ \hline
[133] [PERSON] et al., 2019 & Temporal convolutional network (TCN), KNN, LSTM, & Internal pump leakage & Pressure, temp. volume flow, vibration, cooling efficiency, cooling power, the efficiency factor & Real data \\ \hline
[134] [PERSON] et al., 2018 & Deep neural networks, convolutional neural networks, deep learning (DNN-CNN, DL) & Machine fault detection and oil level prediction & Accelerometer, thermocouple, and thermal camera measurements & Real data \\ \hline
[135] [PERSON] 2018 et al. & Self-organized mapping (SOM) neural network (NN); a long short-term memory (LSTM) network is utilized & Fault detection & Vibration & Synthetic data \\ \hline
[28] [PERSON] et al., 2018 & Ladder network, multi-layer perceptron, and denoising autoencoder (LN, MLP, DAE) & Estimate RUL & Flow, pressure, stress & Real data measurement \\ \hline
[37] [PERSON] et al., 2018 & SVM & Cavitation detection & Speed, pressure & Lab view & Real data measurement \\ \hline
[132] [PERSON] et al., 2018 & Feature-based transfer neural network (FTNN), convolutional neural network (CNN) & Health status of bearings & Normal (N), inner race fault (IF), roller fault (RF), and outer race fault (OF). & Real data \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._In Table 8, numerous studies on pumps are listed, and the table also describes the various parameters used in the studies. The findings show the multiple platforms used for the analyses in each work. The table further elaborated on what type of data was acquired by the authors (synthetic or real data) for the study, and some types were not mentioned in the studies. The table also enumerates the parameters and algorithms employed for each identified problem chosen to be solved by the authors. In this study, an observation has been made on the frequency of usage of the algorithms used in studies. Figure 17 shows that ANN is the most commonly used algorithm in studies, followed by BN and SVM. The studied literature emphasizes the importance of selecting proper training algorithms to speed up the training process and achieve excellent classification accuracy. It has been observed that there is no specific method or algorithm chosen for a particular problem. The choice depends on the type of data and the suitability of the algorithm or methodology to solve the given faults. A comprehensive literature analysis of fault prognosis and diagnosis on pumps is provided herein, and Figure 18 shows that previous studies concentrate more on fault diagnostics than prognostics. Currently, fault diagnostic systems are predominantly constructed as a collection of discrete components, such as data collecting, feature extraction, dimensionality reduction, and fault recognition, with little regard for the diagnostic system as a whole.