One of the most common external corrosion failures in petroleum and power industry is due to corrosion under insulation (CUI). The difficulty in corrosion monitoring has contributed to the scarcity of corrosion rate data to be used in Risk-Based Inspection (RBI) analysis for degradation mechanism due to CUI. Limited data for CUI presented in American Petroleum Institute standard, (API 581) reflected some uncertainty for both stainless steels and carbon steels which limits the use of the data for quantitative RBI analysis. The objective of this paper is to present an adaptive neural based fuzzy model to estimate CUI corrosion rate of carbon steel based on the API data. The simulation reveals that the model successfully predict the corrosion rates against the values given by API 581 with a mean absolute deviation ( MAD ) value of 0.0005, within that the model is also providing its outcomes for those values even for which API 581 has not given its results. The results from this model would provide the engineers to do necessary inferences in a more quantitative approach.
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.
PurposeThis paper aims to propose a practical method of performing maintainability analysis of an offshore system at operation phase having some improvement trend.Design/methodology/approachThe analysis follows a systematically developed method of analyzing maintenance data, identifying critical factors affecting system performance, and developing suitable downtime distribution model with some applications of statistical analysis techniques and expert opinion.FindingsImprovement in spare part logistics is found significant in reducing downtime thus should be well feedback to design and plant engineers so that it can be incorporated in new offshore system. The downtime models developed based on the steady state analysis and expert input are found to be practical for prediction of the system maintainability performance.Research limitations/implicationsThe analysis focuses on the downtime which includes the repair, logistics and administrative delay time. At the operation phase, plant personnel are mostly interested in the availability and downtime performance of the system thus this study provides an excellent example of how the analysis can be done practically and effectively.Practical implicationsMaintainability analysis at operation phase is crucial to assess and predict the maintenance system performance and provide valuable feedback to the design phase and existing plant for further improvement. The methodology developed here is practical hence can assist plant personnel to perform maintainability analysis effectively.Originality/valueThis paper present a generic method of analyzing maintainability at the operation phase. The proposed distribution method via steady state and expert input approaches provides a practical method for formulating downtime distribution model when improvement trend exists.
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