The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
P A Zotos - One of the best experts on this subject based on the ideXlab platform.
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motor failures due to steep fronted switching surges the need for surge protection user s experience
Petroleum and Chemical Industry Technical Conference, 1993Co-Authors: P A ZotosAbstract:Steep fronted wave surges cause severe stress on the installation of the stator windings of large AC motors due to the nonuniform distribution of voltage across windings. The absence of acceptable national standards defining the impulse voltage withstand capability of rotating machines, as applied on Static Equipment, has led to inconsistency, uncertainty, and failures of many such motors. Based on available technical literature, recent national studies, and user experience, the need to specify extra dielectric strength for turn-to-turn insulation to withstand the overvoltages and the need for acceptable national standards and testing requirements are presented. >
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Motor failures due to steep fronted switching surges-the need for surge protection-users experience
Industry Applications Society 40th Annual Petroleum and Chemical Industry Conference, 1993Co-Authors: P A ZotosAbstract:Steep fronted wave surges cause severe stress on the installation of the stator windings of large AC motors due to the nonuniform distribution of voltage across windings. The absence of acceptable national standards defining the impulse voltage withstand capability of rotating machines, as applied on Static Equipment, has led to inconsistency, uncertainty, and failures of many such motors. Based on available technical literature, recent national studies, and user experience, the need to specify extra dielectric strength for turn-to-turn insulation to withstand the overvoltages and the need for acceptable national standards and testing requirements are presented.
Maneesh Singh - One of the best experts on this subject based on the ideXlab platform.
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A Fuzzy logic-possibilistic methodology for risk-based inspection (RBI) planning of oil and gas piping subjected to microbiologically influenced corrosion (MIC)
International Journal of Pressure Vessels and Piping, 2017Co-Authors: Maneesh Singh, Marshal PokhrelAbstract:Abstract Operating oil and gas installations are continuously subjected to attacks by a number of degrading mechanisms. In order to detect the presence and location of these attacks, installations need to be regularly inspected. Unfortunately, comprehensive inspection programs are quite expensive; hence, Risk-Based Inspection (RBI) methodology is often adopted to assist in the development of effective and efficient inspection programs. In order to account for a particular degradation mechanism in RBI analysis, inspection engineers need to know its likelihood of occurrence and its estimated rate of degradation. The complex natures of various degradation mechanisms make accurate prediction of the rates of corrosion in an operating plant rather difficult. Luckily, for developing a risk-based inspection (RBI) program, it is not necessary for a model to accurately estimate the degradation process over a wide range of conditions. Instead the requirement is for a practical model which is simple to use, flexible enough to reflect different sections' requirements, and able to incorporate field data. Microbiologically influenced corrosion (MIC) is one of the commonly encountered degradation mechanisms in offshore and onshore oil and gas installations. As with any other corrosion process, the prediction of likelihood of its initiation and its associated rate of corrosion is difficult to accurately model. A model based on a fuzzy logic framework and possibilistic approach may offer a simple yet flexible tool to assist engineers in developing their RBI programs. This paper presents a proposed methodology, based on a fuzzy logic framework, for estimating the rate of MIC corrosion in carbon steel Static Equipment, pipes and pressure vessels. The paper also presents a procedure based on possibility approach to calculating the possibility and necessity of failure. Finally, the paper presents a methodology for determining the optimum time for inspection.
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Data–information–knowledge hierarchy based decision support system for risk based inspection analysis
International Journal of System Assurance Engineering and Management, 2017Co-Authors: Maneesh Singh, Stig HetlevikAbstract:During their operational lifetime, the Static process Equipment (including piping, pipelines and topside Static Equipment) that constitute an offshore oil and gas production installation are subjected to a number of degrading mechanisms, like corrosion and erosion. These degradation mechanisms can significantly reduce the integrity of Equipment thereby increasing their possibility of their failure. In order to mitigate the risk associated with the failure, the pipes/Equipment are regularly inspected, monitored or tested using various techniques. These inspection–monitoring–testing activities can provide valuable data/information about the existing condition of the pipes/Equipment and provide direction for future maintenance activities. Unfortunately, all these inspection–maintenance activities may result in a vast amount of, potentially imperfect, data that may be difficult to interpret. Currently, data from various stages: design, operation, inspection, monitoring and maintenance etc. are collected but often inefficiently used. The decisions regarding future inspection and maintenance programs are therefore made without taking into consideration all relevant data. Thus there is a need to develop systems that can effectively use the maximum amount of available data to aid in the decision-making process. This paper discusses a framework for an intelligent human-centric decision support system based on the concepts of data – information – knowledge hierarchy . Such a system can help inspection engineers effectively use available databases, information systems and knowledge-based systems for implementing risk-based inspection analysis of degrading structures.
J. Eduardo Munive-hernandez - One of the best experts on this subject based on the ideXlab platform.
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Developing Turnaround Maintenance (TAM) Model to Optimize TAM Performance Based on the Critical Static Equipment (CSE) of GAS Plants
International Journal of Industrial Engineering and Operations Management, 2019Co-Authors: Abdelnaser Elwerfalli, M. Khurshid Khan, J. Eduardo Munive-hernandezAbstract:Many oil and gas companies have suffered major production losses, and higher cost of maintenance due to the total shutdown of their plants to conduct TAM event during a certain period and according to scope of work. Therefore, TAM is considered the biggest maintenance activity in oil and gas plant in terms of manpower, material, time and cost. These plants usually undergo other maintenance strategies during normal operation of plants such as preventive, corrective and predictive maintenance. However, some components or units cannot be inspected or maintained during normal operation of plant unless plant facilities are a totally shut downed due to operating risks. These risks differ from a company to another due to many factors such as fluctuated temperatures and pressures, corrosion, erosion, cracks and fatigue caused by operating conditions, geographical conditions and economic aspects. The aim of this paper is to develop a TAM model to optimize the TAM scheduling associated with decreasing duration and increasing interval of the TAM of the gas plant. The methodology that this paper presents has three stages based on the critical and non-critical pieces of Equipment. At the first stage, identifying and removing Non-critical Equipment pieces (NEs) from TAM activity to proactive maintenance types. During the second stage, the higher risk of each selected Equipment is assessed in order to prioritize critical pieces of Equipment based on Risk Based Inspection (RBI). At the third stage, failure probability and reliability function for those selected critical pieces of Equipment are assessed. The results of development of the TAM model is led to the real optimization of TAM scheduling of gas plants that operated continuously around the clock in order to achieve a desired performance of reliability and availability of the gas plant, and reduce cost of TAM resulting from the production shutdown and cost of inspection and maintenance.
Oladipo Alonge - One of the best experts on this subject based on the ideXlab platform.
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Effective Implementation of Risk Based Inspection (RBI) Approach in Asset Integrity Management of Oil and Gas Facilities
Volume 7: Operations Applications and Components, 2015Co-Authors: Joseph Akanni, Oladipo AlongeAbstract:Effective implementation of asset integrity management in oil and gas facilities requires a structured approach to the inspection of all Equipment to get maximum value for time and resources. This is what Risk Based Inspection (RBI) strives to achieve by optimizing inspection and monitoring efforts based on degradation effects and other associated risks. The risk based approach entails a detailed assessment of all possible degradation mechanisms for Equipment, operating context of the facility as well as the health, safety and environment impact of Equipment failure. All these assessments and considerations extend the impact of RBI beyond its initial objective of achieving an optimised inspection frequency to also ensuring process safety, increased understanding of degradation risk and assuring asset integrity.This paper focuses on the workflow, process, benefits and challenges of a typical RBI implementation for Static Equipment in facilities.Copyright © 2015 by ASME
Marshal Pokhrel - One of the best experts on this subject based on the ideXlab platform.
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A Fuzzy logic-possibilistic methodology for risk-based inspection (RBI) planning of oil and gas piping subjected to microbiologically influenced corrosion (MIC)
International Journal of Pressure Vessels and Piping, 2017Co-Authors: Maneesh Singh, Marshal PokhrelAbstract:Abstract Operating oil and gas installations are continuously subjected to attacks by a number of degrading mechanisms. In order to detect the presence and location of these attacks, installations need to be regularly inspected. Unfortunately, comprehensive inspection programs are quite expensive; hence, Risk-Based Inspection (RBI) methodology is often adopted to assist in the development of effective and efficient inspection programs. In order to account for a particular degradation mechanism in RBI analysis, inspection engineers need to know its likelihood of occurrence and its estimated rate of degradation. The complex natures of various degradation mechanisms make accurate prediction of the rates of corrosion in an operating plant rather difficult. Luckily, for developing a risk-based inspection (RBI) program, it is not necessary for a model to accurately estimate the degradation process over a wide range of conditions. Instead the requirement is for a practical model which is simple to use, flexible enough to reflect different sections' requirements, and able to incorporate field data. Microbiologically influenced corrosion (MIC) is one of the commonly encountered degradation mechanisms in offshore and onshore oil and gas installations. As with any other corrosion process, the prediction of likelihood of its initiation and its associated rate of corrosion is difficult to accurately model. A model based on a fuzzy logic framework and possibilistic approach may offer a simple yet flexible tool to assist engineers in developing their RBI programs. This paper presents a proposed methodology, based on a fuzzy logic framework, for estimating the rate of MIC corrosion in carbon steel Static Equipment, pipes and pressure vessels. The paper also presents a procedure based on possibility approach to calculating the possibility and necessity of failure. Finally, the paper presents a methodology for determining the optimum time for inspection.