Javad Barabady

Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran

The performance of mining machines depends on the reliability of the equipment used, the operating environment, the maintenance efficiency, the operation process, the technical expertise of the miners, etc. As the size and complexity of mining equipments continue to increase, the implications of equipment failure become ever more critical. Therefore, reliability analysis is required to identify the bottlenecks in the system and to find the components or subsystems with low reliability for a given designed performance. It is important to select a suitable method for data collection as well as for reliability analysis. This paper presents a case study describing reliability and availability analysis of the crushing plant number 3 at Jajarm Bauxite Mine in Iran. In this study, the crushing plant number 3 is divided into six subsystems. The parameters of some probability distributions, such as Weibull, Exponential, and Lognormal distributions have been estimated by using ReliaSofts Weibull++6 software. The results of the analysis show that the conveyer subsystem and secondary screen subsystem are critical from a reliability point of view, and the secondary crusher subsystem and conveyer subsystem are critical from an availability point of view. The study also shows that the reliability analysis is very useful for deciding maintenance intervals.

Availability allocation through importance measures

Purpose – To define availability importance measures in order to calculate the criticality of each component or subsystem from the availability point of view and also to demonstrate the application of such importance measures for achieving optimal resource allocation to arrive at the best possible availability.Design/methodology/approach – In this study the availability importance measures of a component are defined as a partial derivative of the system availability with respect to the component availability, failure rate, and repair rate. Analyses of these measures for a crushing plant are performed and the results are presented. Furthermore, a methodology aimed at improving the availability of a system using the concept of importance measures is identified and demonstrated by use of a numerical example.Findings – The availability importance measure of a component/subsystem is an index which shows how far an individual component contributes to the overall system availability. The research study indicates...

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.

An approach for prediction of petroleum production facility performance considering Arctic influence factors

Abstract As the oil and gas (O&G) industry is increasing the focus on petroleum exploration and development in the Arctic region, it is becoming increasingly important to design exploration and production facilities to suit the local operating conditions. The cold and harsh climate, the long distance from customer and suppliers’ markets, and the sensitive environment may have considerable influence on the choice of design solutions and production performance characteristics such as throughput capacity, reliability, availability, maintainability, and supportability (RAMS) as well as operational and maintenance activities. Due to this, data and information collected for similar systems used in a normal climate may not be suitable. Hence, it is important to study and develop methods for prediction of the production performance characteristics during the design and operation phases. The aim of this paper is to present an approach for prediction of the production performance for oil and gas production facilities considering influencing factors in Arctic conditions. The proportional repair model (PRM) is developed in order to predict repair rate in Arctic conditions. The model is based on the proportional hazard model (PHM). A simple case study is used to demonstrate how the proposed approach can be applied.

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 analysis of crushing plants in Jajarm bauxite mine of Iran

Reliability is an important consideration in the planning, design and operation of engineering systems. As the size and complexity of mining equipment continue to increase, the implications of equipment failure become ever more critical. An unplanned failure can result in significantly higher repair costs than a planned maintenance or repair. Of even more importance is the loss of production associated with larger equipment failures. One method to mitigate the impact of failures is to improve the reliability of the equipment. Reliability is a performance indicator of overall equipment condition. A first step in reliability improvement is collection and analysis of the appropriate data. This paper presents a case study describing reliability analysis of crushing plants in Jajarm bauxite mine. In this study crushing plants are divided into seven subsystems. Reliability analysis has been done for each subsystem by using failures data. The parameters of some idealized probability distributions, such as Weibull, Exponential, Lognormal distributions, have been estimated by using ReliaSofts Weibull-H- 6 software. An investigation has also been made to determine which of these distributions provide the best fit for characterizing the failure pattern of the two crushing plants and their subsystems. Some aspects of system failure behavior are analyzed briefly for ongoing machine improvement. Reliability of both crushing plants and its subsystems has been estimated at different mission times with their best fit distribution. Analysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different subsystems shows that the reliability of crushing plant 1 and crushing plant 2 after 10 hour reduce to about 64% and 35% respectively. The study shows that reliability and maintainability analysis is very useful for deciding maintenance intervals. It is also useful for planning and organizing maintenance.

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.

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.

Criticality analysis of a production facility using cost importance measures

When the estimated plant performance is inadequate, it is necessary to use appropriate methodologies and tools for production performance improvement with the minimum effort and cost in the design and/or operation phase. Hence, the concept of importance measure can be used for this purpose. It can be used to determine which components can be improved first in order to get the largest improvement in system performance. Within the system RAM (reliability, availability and maintainability) analysis, the Birnbaum importance analysis plays an important role in the performance improvement program. When both the components’ reliability and maintainability factors are considered in the importance measure analysis, it is noted that the component/system Mean Time to Repair (MTTR) is too short compared with component/system MTTR. Thus, it is necessary to bring the cost factors into the importance measure. This paper proposes a cost importance measure which considers both the reliability and the maintainability information of a system. Then, this cost importance measure is studied in a simple oil and gas production system.