## 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 \multirow{4}{*}{[61] [PERSON] et al., 2018} & Convolutional & \multirow{4}{*}{\begin{tabular}{c} Fault diagnosis in rolling bearing \\ \end{tabular} } & Healthy condition & \multirow{4}{*}{\begin{tabular}{c} Synthetic data \\ \end{tabular} } \\  & (CNNs) & Fault diagnosis in rolling bearing & & (H), outer race & \\  & & rolling bearing & fault (OF), inner & fault (OF), inner & fault (IF), and \\  & & & & ball fault (BF). & \\ \hline \multirow{4}{*}{[136] [PERSON] et al., 2017} & \multirow{4}{*}{\begin{tabular}{c} Intrinsic time-scale decomposition (ITD) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Motor current \\ signals from \\ different pump \\ fault cases. \\ \end{tabular} } & \begin{tabular}{c} RMS values of the \\ first proper \\ rotation \\ component (PRC) \\ with the raw signal \\ RMS values \\ \end{tabular} & \\ \hline \multirow{4}{*}{[137] [PERSON], 2017} & \multirow{4}{*}{\begin{tabular}{c} Deep belief network \\ feed-forward neural \\ network (DBN-FNN) \\ \end{tabular} } & Remaining useful & \multirow{4}{*}{\begin{tabular}{c} Real data \\ \end{tabular} } \\  & & & life (RUL) of the & & \\  & & & rotating & \\  & & & components & \\ \hline \multirow{4}{*}{[61] [PERSON] et al., 2016} & \multirow{4}{*}{\begin{tabular}{c} Stacked denoising \\ autoencoder (SDA) \\ and softmax \\ regression \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Internal \\ thermo-electric \\ potential faults \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Internal \\ superheat, low \\ energy discharge, \\ high energy \\ discharge, arc \\ discharge with \\ overheating \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} KNIME, \\ MATLAB \\ \end{tabular} } \\  & & & discharge, arc & \\  & & & discharge with & \\  & & & overheating & \\ \hline \multirow{4}{*}{[139] [PERSON] 2015} & \multirow{4}{*}{
\begin{tabular}{c} Adaptive hidden \\ semi-Markov model \\ (AHMM) \\ \end{tabular} } & \multirow{4}{*}{Estimate RUL} & \multirow{4}{*}{Oil flow} & \multirow{4}{*}{Real data} \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._In this study, another significant observation is that among the numerous research studies on pump fault diagnosis and prognosis, the most frequent fault discovered or addressed is the bearing fault, as shown in Figure 19, with a percentage of 46%, followed by cavitation. Seal damage is the third-highest fault in studies. Leakage and blockage are the least common faults considered in studies. Another important finding in this review is that most of the accomplished projects depend on auditory and vibration data. Additionally, existing issues, such as the unpredictability of the failure form, and the lack of sample data in the data system, must all be addressed. Figure 17: The frequency of algorithm application in studies. ANN—artificial neural network, SVM—support vector machine, PCA—principal component analysis, FT—Fourier transform, HMM—hidden Markov model, DT—decision trees, BN—Bayesian networks, KF—Kalman filter, PF—particle filter, GPR—Gaussian process regression, GMM—Gaussian mixture model, FLD—Fisher linear discriminant, HHT—Hilbert–Huang transform, MP—multilayer perceptron, CNN—convolutional neural network, LSTM—long short-term memory. Figure 18: The application of diagnosis and prognosis of pump faults in studies.