Abbas Barabadi

Maintainability analysis considering time-dependent and time-independent covariates

Traditional parametric methods for assessing maintainability most often only consider time to repair (TTR) as a single explanatory variable. However, to predict availability more precisely for high availability systems, a better model is needed to quantify the effect of operational environment on maintainability. The proportional repair model (PRM), which is developed based on proportional hazard model (PHM), may be used to analyze maintainability in the present of covariates. In the PRM, the effect of covariates is considered to be time independent. However this assumption may not be valid for some situations. The aim of this paper is to develop the Cox regression model and its extension in the presence of time-dependent covariates for determining maintainability. A simple case study is used to demonstrate how the model can be applied in a real case.

Application of reliability models with covariates in spare part prediction and optimization – A case study

The number of spare parts required for an item can be effectively estimated based on its reliability performance. The reliability characteristics of an item are influenced by different factors such as the operational environment, maintenance policy, operator skill, etc. However, in the majority of reliability based spare part provision studies, the effect of these influence factors has not been considered, and the only variable of interest is the operating time. The aim of this paper is to demonstrate the application of the available reliability models with covariates in the field of spare part predictions by means of a case study.

Reliability and maintainability performance under Arctic conditions

Operational environments may have a considerable influence on the reliability performance and maintainability performance of an item. In the Arctic region with harsh climate condition, sensitive environment and remote location, these influences are more critical. To achieve effective reliability and maintainability analysis and management, all technical challenges and influence factors must be identified. Then, based on the way that these factors influence the failure mechanisms, maintenance processes and other support activities, appropriate statistical approaches must be selected to quantify their effects. The first part of this paper reviews the technical challenges for the offshore oil and gas industry from the reliability and maintainability performance perspective in the Arctic region. The second part of this paper reviews the available appropriate statistical approach for reliability and maintainability performance analysis under Arctic conditions.

RAMS data collection under Arctic conditions

Reliability, availability, maintainability and supportability analysis is an important step in the design and operation of production processes and technology. Historical data such as time between failures and time to repairs play an important role in such analysis. The data must reflect the conditions that equipment has experienced during its operating time. To have a precise understanding of the conditions experienced, all influence factors on the failure and repair processes of a production facility in Arctic environment need to be identified and collected in the database. However, there is a lack of attention to collect the effect of influence factors in the reliability, availability, maintainability and supportability database. Hence, the aim of this paper is to discuss the challenges of the available methods of data collection and suggest a methodology for data collection considering the effect of environmental conditions. Application of the methodology will make the historical RAMS data of a system more applicable and useful for the design and operation of the system in different types of operational environments.

A methodology for throughput capacity analysis of a production facility considering environment condition

Throughput capacity of a production facility plays an important role in supporting managers and engineers in the decision making processes related to the design and optimization of a production plant. System throughput capacity is affected in complex ways by the reliability, maintainability, and capacity of its components. These in turn are considerably affected by operational environment such as ambient temperature, icing, dust, wind, etc. Therefore, in order to have an effective throughput capacity analysis (TCA), all influence factors (covariates) on these terms need to be identified; furthermore, their effect must be modeled and quantified by an appropriate statistical approach. The aim of this paper is to develop a methodology for throughput capacity analysis considering environment condition. A simple case study is used to demonstrate how the methodology can be applied in a real case.

Reliability Modelling of Multiple Repairable Units

This paper proposes a model selection framework for analysing the failure data of multiple repairable units when they are working in different operational and environmental conditions. The paper pr ...

Bioremediation treatment of hydrocarbon-contaminated Arctic soils: influencing parameters

The Arctic environment is very vulnerable and sensitive to hydrocarbon pollutants. Soil bioremediation is attracting interest as a promising and cost-effective clean-up and soil decontamination technology in the Arctic regions. However, remoteness, lack of appropriate infrastructure, the harsh climatic conditions in the Arctic and some physical and chemical properties of Arctic soils may reduce the performance and limit the application of this technology. Therefore, understanding the weaknesses and bottlenecks in the treatment plans, identifying their associated hazards, and providing precautionary measures are essential to improve the overall efficiency and performance of a bioremediation strategy. The aim of this paper is to review the bioremediation techniques and strategies using microorganisms for treatment of hydrocarbon-contaminated Arctic soils. It takes account of Arctic operational conditions and discusses the factors influencing the performance of a bioremediation treatment plan. Preliminary hazard analysis is used as a technique to identify and assess the hazards that threaten the reliability and maintainability of a bioremediation treatment technology. Some key parameters with regard to the feasibility of the suggested preventive/corrective measures are described as well.

Reliability and spare parts estimation taking into consideration the operational environment — A case study

Spare parts provision is a complex problem and requires an accurate model to analysis all factors that may affect the required number of spare parts. The number of spare parts required can be effectively estimated based on the reliability performance of the item. The reliability characteristics of an item are influenced not only by the operating time, but also by factors such as the operational environment. Therefore, for spare parts provisioning to be effective, the impact of these influence factors on the reliability performance of the item should be quantified. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability performance analysis that takes influence factors into account is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. In this paper the application of PHM to spare parts provision is discussed and demonstrated by a case study.

Human reliability assessment (HRA) in maintenance of production process : a case study

Human reliability makes a considerable contribution to the maintenance performance, safety, and cost-efficiency of any production process. To improve human reliability, the causes of human errors should be identified and the probability of human errors should be quantified. Analysis of human error is very case-specific; the context of the field should be taken into account. The aim of this study is to identify the causes of human errors and improve human reliability in maintenance activities in the cable manufacturing industry. The central thrust of this paper is to employ the three most common HRA techniques—human error assessment and reduction technique, standardized plant analysis risk-human reliability, and Bayesian network—for estimating human error probabilities and then to check the consistency of the results obtained. The case study results demonstrated that the main causes of human error during maintenance activities are time pressure, lack of experience, and poor procedure. Moreover, the probabilities of human error, obtained by employing the three techniques, are similar and consistent.

Application of accelerated failure model for the oil and gas industry in Arctic region

The development of offshore energy resources involves highly complex and extensive technological processes. Therefore all relevant factors which can affect equipment performance and safety must be identified and quantified exactly. This is more critical when the design is going to be established for a new operational environment such as the Arctic region with new challenges. There are a few data and little experience available regarding operation equipment in the offshore oil and gas industry in the Arctic region. However, the oil and gas industry has established regular programs such as OREDA (Offshore Reliability Data), in order to collect reliability data. Using this type of data, collected from similar systems but under different operational environmental locations, in designing processes for the Arctic region may lead to incorrect design. This may increase risk with respect to Health, Safety and Environment (HSE) or/and increase costs. Therefore, the available data need to be considered according to the environment condition. According to the existing literature, an accelerated failure time (AFT) model is a useful approach in order to consider the effect of the operational environment on the performance of equipment. The aim of this paper is to develop a methodology in order to predict the reliability of equipment in the Arctic region using an accelerated failure time (AFT) model. An illustrative numerical example is used to demonstrate how the model can be applied in a real case.