Yonas Zewdu Ayele

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.

Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero “hazardous” discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season. [DOI: 10.1115/1.4033713]

Risk-Based Cost-Effectiveness Analysis of Waste Handling Practices in the Arctic Drilling Operation

Research Council of Norway nENI Norge AS through the EWMA (Environmental Waste Management) project

Dynamic spare parts transportation model for Arctic production facility

Timely delivery of the required spare parts plays an important role in meeting the availability target and reducing the downtime of production facilities. Spare parts logistics is affected in complex ways while operating in the Arctic, since the area is sparsely populated and has insufficient infrastructure. It is also greatly affected by the distinctive operational environment of the region, such as cold temperature, varying forms of sea ice, blizzards, heavy fog, etc. Therefore, in order to have an effective logistic plan, the effect of all influencing factors, called covariates, on the transportation of the spare parts need to be identified, modelled and quantified by the use of an appropriate dynamic model. The traditional models, however, lack the comprehensive integration of the effect of covariates on the spare parts transportation. The purpose of this paper is to introduce the concept of a dynamic model for spare parts transportation in Arctic conditions by considering the time-independent and time-dependent covariates. The model continuously updates the prior probabilities according to the most recent time-dependent covariates to provide posterior probabilities. The application of the model is illustrated using a case study.

Reliability modeling of successive release of software using NHPP

This paper presents an effective reliability model for multi-release open source software (OSS), which derived based on software lifecycle development process (SDLC) proposed by Jørgensen [1]. Most of OSS reliability models do not consider the unique characteristic of OSS in the model. This model, combine bugs removed from pre-commit test and parallel debugging test phases. Furthermore, the proposed model is based on the assumptions that the total number of fault removal of the new release depends on the reported faults from the previous release and on the faults generated due to adding some new adds-on to the existing software system. The parameters of model have been estimated using three releases of the Apache project. In addition, three models in the literature are selected to compare with the proposed model. Comparison indicates that the proposed model is a suitable reliability model that fits the data across all the releases of the Apache project.

Effectiveness assessment for waste management decision-support in the Arctic drilling

The technological advances, new concepts and the ranges of possible waste management solutions in the Arctic drilling pose their own peculiar demands and affects the waste management decision making process. Thus, it is become imperative to evaluate the assessment methods for their effectiveness, to support strategic decision. The aim of this paper is to propose steps for evaluating the available assessment method for their effectiveness. The proposed steps can help to assess the assurance of the stringent drilling waste-discharge requirements in the region (such as zero hazardous discharge to the sea). By employing the proposed steps the suitable drilling waste management decision-support tool can be recommended.

Drilling waste handling and management in the High North

The Arctic region has a harsh and sensitive environment at a remote location where special local considerations will influence the design, operation and management of any waste handling system installed in the region. In order to develop a suitable waste handling system in the High North, the main technological and operational challenges need to be identified and tackled. In this paper a methodology for identification of waste handling system is developed for offshore drilling activities in the High North. The developed methodology can help to ensure fulfillment of health, safety, environmental, and quality (HSEQ) requirements in the High North. This paper also discusses different options for the handling and management of the drilling waste in the High North.

Post-disaster infrastructure recovery: Prediction of recovery rate using historical data

Abstract The recovery of infrastructure systems is of significant concern; in order to have effective risk management planning, an accurate prediction of the recovery time is required. A system may have different recovery paths due to the time of the accident, nature of the disruptive event, and surrounding environment, among many other factors. Hence, any model, which is employed to estimate the recovery time, should be able to quantify the effect of such influencing factors. Missing data, inappropriate assumption by analysts, and lack of applicable methodology are some practical challenges for recovery rate analysis. The purpose of this paper is to develop a methodology to address these challenges. It is based on the availability and the nature of historical data; it involves various steps, including categorizing the given data set into three groups: no or missing data set, homogeneous data set, and heterogeneous data set. Here, the Bayesian approach has been employed to handle the no or missing data set group. For the heterogeneous data set group, the proposed methodology suggested the application of covariate based models. Finally, for the homogeneous data set, the methodology employed statistical trend tests, to find the appropriate regression models. The application of the methodology is illustrated by real case studies.

Probabilistic metric of infrastructure resilience considering time-dependent and time-independent covariates

In recent years, the importance of resilient critical infrastructures has become more evident. More frequent extreme weather conditions and human-induced disasters, such as terror attacks, cause se ...

Production performance analysis during operation phase: A case study

Production performance analysis plays a significant role in supporting the decision-making process, for managers and engineers dealing with the challenges of the optimization procedure of a system delivery capacity. Operational conditions can influence the production performance of a system drastically, in a short or long term, by affecting the system configuration, reliability, maintainability, maintenance supportability, and functional capacity of its components. Moreover, these impacts can lead to increased business risks and uncertainties. Such a situation demands the successful application of tools and methodologies to minimize the total business risk through an accurate prediction of production performance, addressing the effects of operational conditions. However, there is a lack of practical methodologies, for production performance analysis of systems operating under the time-dependent operational conditions. Hence, the central thrust of this article is to develop a systematic methodology, for the production performance analysis of a system considering the time-dependent operational conditions. In the case study, the results show that the production performance of mining equipment is affected significantly by the operational conditions. These changes occur across the working shifts and due to any changes in the production plan of the mine. The result of the analyses shows that the critical items in the mine is the loader. Hence, in order to increase the production performance of the system, the optimization of the reliability or maintainability of the loader needs to be considered as a priority. Moreover, the wagon drill has a significant excess capacity, which means that, by adding more trucks to the system, the production performance of the system can be improved.