## 2 Diagnostics and Prediction of Faults in Pumps
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Parameter** & **Method** & **Fault** \\ \hline
[80] [PERSON] 2015 &
\begin{tabular}{c} Pressure fluctuation and \\ mechanical vibration signals \\ \end{tabular} & Neuro-fuzzy classifier & Impeller misalignment \\ \hline
[81] [PERSON] 1999 & \
\begin{tabular}{c} Pressures, temperatures, \\ power, and flow \\ \end{tabular} &
\begin{tabular}{c} Fast Fourier transformation \\ (FFT) \\ \end{tabular} & Imbalance, blockade, and wear \\ \hline
[82] [PERSON] 2002 & \
\begin{tabular}{c} Pressures, temperatures, \\ power, and flow \\ \end{tabular} &
\begin{tabular}{c} Neural networks, \\ discriminants, and Bayes \\ classifiers \\ \end{tabular} & Imbalance, blockade, and wear \\ \hline
[83] [PERSON] 2006 &
\begin{tabular}{c} Flow, temperature, angular \\ velocity, pressure, and the \\ magnetic field \\ \end{tabular} & Decision tree classifier & Gas fault states in fluid, cavitation, and blockade \\ \hline
[84] [PERSON] et al., 2007 & \
\begin{tabular}{c} Flow, temperature, angular \\ velocity, pressure, and the \\ magnetic field \\ \end{tabular} &
\begin{tabular}{c} Discrete wavelet transforms, a \\ neuro-fuzzy classifier based \\ on rough set theory \\ \end{tabular} & Cavitation, misalignment, and impeller damage \\ \hline
[85] [PERSON] et al., 2008 & \
\begin{tabular}{c} Horizontal, axial, \\ and vertical accelerations \\ \end{tabular} &
\begin{tabular}{c} K-nearest-neighbor approach, \\ as well as a multi-layer \\ perceptron neural network \\ \end{tabular} & Bearing faults \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Signal model-based approaches. As can be seen in Table 2, model-based strategies were employed with mathematical models in studies to discover faults. Methods such as fast Fourier transformation, Bayes classifiers, neural networks, Bayesian belief networks with support vector machines, load torque signature analysis, discrete wavelet transforms, neuro-fuzzy classifiers, and decision tree classifiers were applied to resolve the given problems. The majority of the parameters are pressure fluctuation, temperature, flow, angular velocity, and frequency spectra. Based on the table, most of the research focused on model-based solutions that leverage signal or process models [6]. However, [PERSON] and [PERSON] [88] argued that a data-driven strategy is more capable of detecting faults than a model-based approach. This is because the data-driven method is based on examining and processing raw signals collected from the system. As a result, [PERSON] [89] recommended investigating numerous rotor system failures. Table 3 presents the studies based on the process-model techniques applied in detection, along with what faults were addressed. It has been observed that parameter estimation and observer-based fault diagnosis are the most commonly used methods in studies, as shown in Table 3. It has been challenging to source earlier studies on the raw data from operational machinery, despite a thorough literature analysis. [PERSON] et al. [94] recently described the recent application of AI for defect identification in the oil and gas industry. The importance of evaluating fault detection, and its advantages it is widely used for strategic management and technological enabling. However, as a result of the inadequate readiness of industries to share ownership of data, notably in the area of petrochemical applications, various studies rely on information derived through simulations or experimental tests performed in a laboratory. The following section provides an overview of the machine learning methods used in predictive maintenance for pumps.