## 2 Diagnostics and Prediction of Faults in Pumps
[55], who presented a data-driven model for gas circulator unit health tracking using real vibration data as part of this body of work, while [PERSON] et al. [48] also revised the research on the intelligent diagnosis of oil transfer pump failures. [PERSON] et al. [67] examined PHM design for rotary machinery systems, discussing the various approaches for categorizing important components and tools for choosing the most suitable algorithm for a particular application. Similarly, review by [PERSON] et al. [68] on various maintenance approaches, types of failure, and failure detection techniques used in hydraulic pumps. A significant number of the previous studies focused on faults that occur one at a time, this is evident based on the above findings. However, concurrent faults are an essential consideration in practice [67]. [PERSON] et al. [69] classified the methods of detecting faults, based on the signals evaluated and models used, as depicted in Figure 6. Furthermore, [PERSON] [70] classified the fault detection techniques into model-based, as in [71], data-driven, as in [72], and hybrid fault detection, as in [73]. In the signal-based approaches, only the output signals are examined. Meanwhile, signal-model-based methods extract features using a specific signal model that matches the predicted output signal characteristics. The appropriate models include Fourier transform models for harmonic signals, statistical representations for random signals, correlation analysis, and wavelet analysis. Table 1 presents the signal-based methods applied in studies. Figure 6: Classification of conventional fault detection techniques. The signal-based approach was adopted in several studies for the detection of various faults, such as cavitation, blockages, impeller damage, inadequate bearing seals, and power outage, as shown in Table 1. Using methods such as signal analysis, neuro-fuzzy classifier signed directed graphs, and the Mahalanobis-Taguchi system, statistical parameters were taken from vibration analysis or current spectrum analysis for the given problems. Conversely, process model-based approaches compared the process's input and output signals with the system model. The process model-based approaches include parameter estimation, neural networks, observers, state estimation, and parity equations. Table 2 shows the studies that applied this method for detecting different faults. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Parameter** & **Method** & **Fault** \\ \hline
[74] [PERSON] et al., 2007 & Statistical parameters & Neuro-Fuzzy classifier & Cavitation, impeller damage, and imbalance \\ \hline
[75] [PERSON] et al., 2010 & Statistical features extracted from vibration signals & Decision tree and rough sets Fuzzy classifiers & Bearing, seal, and impeller faults \\ \hline
[76] [PERSON] et al., 2011 & Sensors for lateral acceleration, vertical acceleration, and inlet and outlet pressure, as well as outlet flow & Mahalanobis–Taguchi System & Clogging of filter and seal faults or impeller \\ \hline
[77] [PERSON] et al., 2012 & Inlet upper flow rate of the tank, outlet flow of the pump, flow detection unit, inlet valve of the pump, tank level, the inlet pressure of the pump, liquid level detection unit, and outlet valve of the pump & Signed directed graphs & Motor circuit damage and power outage of the impeller and the drive shaft \\ \hline
[78,79] [PERSON] 2006 & Current spectrum signatures & Signal analysis & Different fault detection methods for blockages, cavitation, and damaged impellers \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Signal-based approaches.