## 1 Introduction
In addition, it enables early failure identification using prediction tools, based on historical data, for instance, machine learning techniques, integrity factors such as visual features, wear, statistical inference methods, and the engineering approach [23]. Figure 1 depicts the types of maintenance strategies and procedures explained above. The advantages of PdM include extending the period when equipment is utilized and operated. Examples include delaying/reducing maintenance operations and cutting material and labor costs [6]. According to [PERSON] et al. [24], there are three sorts of PdM approaches suitable for monitoring equipment conditions for diagnostic and prognostic purposes. These are model-based techniques, statistical approaches, and artificial intelligence approaches [25]. Figure 2 depicts the extension of the life of equipment using PdM. The authors demonstrated in Figure 2 how the use of PdM in the prognosis of pumps might extend the life of the equipment by detecting faults or the onset of issues at an early stage, prior to the equipment's total failure. Early detection permits early maintenance to be performed to prevent failure. As a result of the early employment of maintenance, the pumps' lifespan is extended. Additionally, the oil and gas industry views oil field digitization as a brand-new possibility for greater production efficiency. The crucial issue is how to put these tools into practice in a way that all known risks are managed, value is truly delivered, the actual outcomes can significantly affect the operation's profitability, and, of course, they are applicable to specific, set production optimization targets. Certain aspects  
Figure 1: Maintenance types. Figure 2: Predictive maintenance extending the equipment’s lifespan [26]. of oil production applications, such as reservoir management, production optimization, artificial lift, flow assurance, and predictive maintenance were among the important areas where prior research in this field had the potential to further modernize technology. This study primarily focuses on the application of machine learning to comprehend equipment, specifically pumps status, in order to assist in predictive maintenance and save operational downtime. This study presents an overview of artificial intelligence (AI) approaches and focuses on the use of machine learning algorithms for system health monitoring. In recent years, machine learning has been used for fault diagnosis and prediction. This expansion has, so far, included mechanical equipment monitoring, electrical systems, power installations, and aerospace disciplines. This comprises techniques for diagnosing electromechanical equipment faults, classifying degradation and pattern identification, and predicting component Remaining Useful Life (RUL). The current state-of-the-art is a compilation of articles that were examined as part of an ongoing study on the use of machine learning. The authors provided a thorough assessment on ML by taking a practical approach and focusing their work on pump-system health management. Interestingly, there are other publications offering far more in-depth research, wherein researchers conducted thorough analyses on machine learning within the oil and gas industry, e.g., see [27; 28; 29; 30; 31; 32; 33]. However, design engineers and researchers who work in the fields of testability, diagnostic algorithm design, and health monitoring technologies within the context of artificial intelligence research should find this paper to generally be of interest. It can be used to explain the idea of machine learning and identify applications for it. When current implementation technology is taken into account, the study also identifies several problems. Although the solutions being developed for pump applications are the primary focus, other disciplines should be able to find the information in this article to applicable.