## 1 Introduction
The objectives of this study can be summed up as follows:  
* Provide an overview of the concepts of conventional maintenance strategies applied in previous studies. * Analyze current trends in the state of the art regarding the application of machine learning works related to fault diagnosis and prognosis using various fault detection techniques in pumps. * Develop a coherent understanding of machine learning in supervised, unsupervised, and semi-supervised algorithms. * Develop an appreciation of the core merits of machine learning through a sector-wise view of the technology. * Provide the current progress improvements, including their basics, pros and cons, and the standard commercially available Machine Learning (ML) tools. * Provide practical insights for academia and industries on which algorithms are best-suited for certain problems and how to approach the problems, while enabling quicker and more efficient maintenance decision0 making. Other related works studying ML from 2010 to 2022 and the type of data used in the studies were reviewed. The findings were elaborated to help operators meet life-extension requirements while optimizing their cost structure and conclusions; future recommendations are also made. The study offers an organized and comprehensive overview of multi-application domain machine learning research on system health management. The existing literature on the subject either focuses on one or more research areas or on a specific application domain. The writers first outline the fundamentals of each technique, before highlighting its many variants. Accordingly, developments in the literature are explored. This template offers a clearer and more concise overview of the various strategies while noting their advantages and disadvantages. While some of the current research focuses on applications for pumps in the oil and gas industries, interest from the point of view of machine learning is growing. The various applications of these strategies are listed in this publication. However, a significant feature of real-time applications is the computational complexity of these techniques,which is not discussed in many of the evaluated studies. As it appear to be no clear way to choose, build, or implement a machine learning architecture because the majority of ML implementations in this field are application- or equipment-specific, according to the authors. As a result, one of the main objectives for future ML implementations is to spread defect diagnosis and prognosis toward lower design levels so that detections or forecasts can happen more closely to the actual occurrence and, consequently, localization is made possible. Additionally, there are no any developed end-to-end solutions or adequate benchmarking provisions in the outcomes. Which algorithms will perform better for certain applications are not always obvious because these strategies frequently call for extensive parameter and framework customization. It is significant to highlight the paper's main contributions. This paper offers a critical analysis of the growing body of research on the use of ML for pump health management. ML-based fault detection is gaining popularity, although the majority of methods are application- or equipment-specific, making it unclear how to choose, construct, or use ML algorithms. Application complexity, end-to-end learning solutions, suitable benchmarking, and estimating the costs of adopting the algorithms are all areas lagging behind in research and deep understanding. The remainder of the paper is organized as follows: Section 2 focuses on the diagnostics and prediction of faults in pumps and the relevant studies for the PHM of pumps in the oil and gas sectors. The literature review process is presented in Section 3, using the standard PRISMA methodology. Section 4 contains the ML algorithms adopted in related studies, backgrounds, and applications, including their particular advantages and disadvantages. The section further explains the primary outcomes for the ML algorithm queries and data sources.