## 5 Conclusions  
In this study, the research explored the most recent advancements in the field of AI and machine learning and their applications in the oil and gas industries. This study indicated that the frequency of application of artificial intelligence (AI) for the accurate diagnosis and forecasting of pump defects has risen dramatically over the past decade, particularly in the oil and gas industries. Artificial intelligence has gained popularity and is being widely used in mechanical manufacturing and automation, as a result of the quick pace of life and the arrival of Industry 4.0. The manufacturing industry benefits greatly from its powerful data processing, computational, and storage capabilities. It is beneficial for encouraging work efficiency and quality control, frees up more worthwhile tasks for humans to complete, creates much safer workplaces, offers problem diagnosis and predictive maintenance, and creates more intelligent supply chains. The advancement of mechanical manufacturing assists artificial intelligence and vice versa. The manufacturing sector is undergoing the fourth Industrial Revolution, and the introduction of 5G and the use of artificial intelligence will hasten this change. This comprehensive study will be able to pave the way for future research works, by highlighting the common faults identified, the algorithms employed in previous studies, and the parameters that are taken into consideration. Therefore, this simplifies identifying other vital areas for further investigation in the respective studies. Hence, multiple challenges that need to be addressed in future research were identified, as follows:  
* It has been noticed that no specific method or algorithm exists for a given problem. The process relies on the type of data and the algorithm's or method's attitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently treated problem is that of a bearing fault, with a percentage of 46%, followed by cavitation. The published studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research.
* Artificial neural networks, support vector machines, and hybrid models are the most frequently used machine learning models for evaluating the health of damaged oil and gas pumps.
* Due to their ability to incorporate the strengths of multiple machine learning models rather than just one, hybrid models were found to be the most successful of all the models tested. Depending on their location and medium, a wide range of factors can affect the health of oil and gas pumps; however, the most frequently used factors include pressure, temperature, flow, angular velocity, noise, vibration signals, and so  
Figure 19: The most frequent faults addressed in the studies.  
on. However, neither reliability, nor precision, nor processing time was taken into account.
* The research question found that four different types of datasets were primarily used to create machine learning models, particularly datasets based on expert judgment, field data, experimental data, and simulation-based data. Simulated data and experimental datasets have frequently been produced using field data. The data side is currently under-researched, compared to the algorithmic side.
* The data types employed in the machine learning methods for pump fault diagnosis are mainly limited to vibration and flow, which may not be adequate to characterize the condition of pumps and their attributes. This can lead to false alarms and a lack of confidence in the predictions under the dynamic operational environments of pumps.