Hilmi Hussin

Maintainability analysis of an offshore gas compression train system, a case study

Purpose – This paper aims to propose a practical method of performing maintainability analysis of an offshore system at operation phase having some improvement trend.Design/methodology/approach – The 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.Findings – Improvement 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/implications – The analysis focuses on the downtime which includes the repair, logistics and administrative delay time. At the operation phase, plant personnel a...

A Practical Method for Analyzing Offshore Gas Compressor System Maintenance Data

A systematic approach with proper statistical analysis techniques for analyzing maintenance data can give insight on how well the performance of the existing system. The objective of this study is to present a methodology for systematically analyzing the maintenance data of offshore gas compressor system to gain insight about the system performance and identify the critical factors influencing the performance. The study approach is based on problem and data‐lead rather than technique‐driven. The results of trend test propose that the system under studied can be modeled using a simple Homogeneous Poisson process (HPP) process where the failure rate is constant. Analyses of covariates are done using Kaplan Meier and Proportional hazards models. The results indicate that the preventive maintenance (PM) plus engine wash has a significance influence on the system failure distribution. This covariate is found to play a positive role in extending the inter‐arrival failure times thus improving the system performance.

Continuous Life Cycle Cost Model for Repairable System

Traditionally, the estimation of maintenance cost of a repairable system was evaluated using discrete approach based on estimated number of system failure, cost of repair as well as the interest rates. As maintenance cost represents a significant portion of overall life cycle cost (LCC), accurate estimation of maintenance cost would influence LCC analysis. However, in actuality the failure of the repairable system occurs in a continuous probabilistic manner thus the assumption of discrete occurrence is rather inaccurate. This paper presents an alternative continuous LCC model to better represent the actual operating phenomena of repairable system. The model was established based on the widely used Weibull distribution probability density function and continuous combined interest method. The result of the developed LCC model was then validated using Monte Carlo method. The result indicates that the continuous LCC model is able to accurately estimate LCC for any given time that can be useful in decision making based on life cycle cost.

A Neural Based Fuzzy Logic Model to Determine Corrosion Rate for Carbon Steel Subject to Corrosion under Insulation

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.

Identification of Critical Factors to System Performance Using Availability Modeling and Simulation Analysis

Availability analysis presents a means to understand the impact of existing maintenance system and maintenance resources to the overall system operational availability. The practical method for conducting availability analysis of a plant system at operation phase is illustrated and discussed via a case study of an acid gas removal system of gas processing plant. This study demonstrates that the availability modeling and simulation is effective in assessing the existing and future system configurations and determining possible impacts and critical factors to systems availability. These findings can significantly assist management to make right actions in improving plant system performances.

Reliability assessment framework for repairable system

System reliability assessment serves as one of the decision tools in selecting the right maintenance strategy. However, selecting the right reliability model can be a formidable task given the vast number of available reliability prediction models This paper presents a framework of selecting the right model based on system failure data with special emphasis on generalized renewal process (GRP) for system that exhibits failure trending The results indicate a better fit for the data with GRP compared with life data analysis approach.