In electrical discharge machining (EDM), the tool electrode is one of the substantial components of the system, and it ensures the success or failure of the EDM process. The electrode’s role is to conduct electrical charges and erode the workpiece to the desired shape. Different electrode materials have different impacts on machining. Certain electrode materials remove metal quickly but wear out rapidly, while others degrade slowly but the material removal is too slow. The choice of the electrode has an influence on both the mechanical properties, such as metal removal rate (MRR), wear rate, surface finish, surface modification and machinability, and the electrical properties, such as sparking initiation, time lag, gap contamination and process stability. There are factors to consider when fabricating an electrode, which include the type of workpiece materials, the metallurgical alloying of the materials, the choice of fabrication techniques, the intended use of the electrode, and material cost. Considerable challenges in EDM electrode fabrication have been reported, which include excessive tool wear for green compact electrodes, high toughness for sintered electrodes, and poor rigidity for additively manufactured electrodes. To address these issues, researchers have explored different manufacturing methods, such as casting, conventional machining, electrodeposition, powder metallurgy and additive manufacturing. In this paper, the various techniques attempted and adopted in EDM electrode manufacturing are analyzed and discussed. This paper also sought to give insight into EDM, its various forms, the dielectric fluid’s properties, EDM electrode’s size and shape, the effects of the electrode on the EDM process, material removal, electrode wear, present technologies for electrode fabrication, and the limitations of these technologies. Finally, directions for future research are highlighted.

A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention.