## 2 Diagnostics and Prediction of Faults in Pumps
Based on the values and attributes of the produced residual signal, probable fault situations are identified, based on a comparison between the model outputs and the actual system outputs. Figure 5 shows the perceptions of a typical model-based approach to fault diagnostics. However, advanced signal processing algorithms can assist in determining what sorts of failures occurred and where they occurred in the equipment. Additionally, the diagnosis outcomes from signal processing techniques are too technical for machine users to comprehend. Therefore, during the fault detection procedure, parameters indicating the state and functionality of the pumps are monitored. These parameters can be identified as process parameters, including fluid flow, temperature, pressure, electric current, power, and efficiency. Another set of parameters used in fault detection is that of fault character parameters, including the peak value, virtual value, amplitude spectrum, phase spectrum, and power spectrum [45]. In addition, oil analysis parameters are also vital to study. This is carried out by analyzing the material, concentration, and the wear particle's dimension distribution, using oil analysis and the characteristic statistical parameters, such as the mean time before failure (MTBF), reliability, availability, and maintainability. These parameters can be considered fault symptom signals, indicating the characteristics of potential faults [35]. As a result, current industrial applications select defect diagnostic technologies that can automatically categorize machine health statuses [45]. According to [PERSON] et al. [46], PHM is now the safest method for managing the safety state of equipment. These are accomplished by the methodical implementation of recent test results in AI and IT technologies. The authors stated that PdM could save maintenance expenses and extend the RUL. PdM primarily focuses on utilizing predictive data to plan future maintenance activities correctly. In addition to the process data and parameters, PdM strives to capture data on the physical condition of a machine or component., including data on pressure, vibration, temperature, viscosity, acoustics, viscosity, and flow rate [47]. At the same time, this information is now often used for equipment health assessments, defect identification, early problem detection, and forecasting future equipment conditions. In addition, intelligent fault diagnosis (IFD) is anticipated to achieve the goals mentioned earlier using machine learning theories [48]. The traditional theories of machine learning, such as support vector machines (SVM) [36; 37; 39; 49; 50; 51; 52; 53; 54; 55] and artificial neural networks (ANN) [27; 45; 56; 57; 58; 59; 60; 61; 62; 63; 64], have been employed to analyze machine faults in the oil and gas  
Figure 5: The model-based diagnostic approach. industries. The use of ML algorithms in industrial applications for fault detection has been the subject of numerous comprehensive literature reviews and research, but there are few concerning the oil and gas sector. The research conducted by [PERSON] et al. [6] include a systematic review of the literature (SLR) that concentrated on machine learning methodology, utilized alongside predictive maintenance methods. [PERSON] et al. [65] also established a field roadmap and an SLR on machine learning applications for machine malfunction diagnosis. [PERSON] et al. [66] analyzed the most recent progress in data analysis techniques and machine learning algorithms in the Industry 4.0 paradigm. The authors noted the sharp rise in publications since 2010 on machine fault diagnostics using machine learning. The authors reported locating more than twenty articles on predictive prognostic models, but only one on the oil and gas sector. Interestingly, only few studies, have specifically addressed the use of ML in the oil and gas industry. This includes research study by [PERSON] et al.