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  • 1.
    Al-Chalabi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Life cycle cost analysis of the ventilation system in Stockholm's road tunnels2018In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 24, no 3, p. 358-375Article in journal (Refereed)
    Abstract [en]

    Purpose

    This study developed a practical economic replacement decision model to identify the economic lifetime of the ventilation system used by Trafikverket in its Stockholm tunnels.

    Design/methodology/approach

    The proposed data driven optimisation model considers operating and maintenance costs, purchase price and system resale value for a ventilation system consisting of 121 fans. The study identified data quality problems in Trafikverket’s MAXIMO database.

    Findings

    It found the absolute economic replacement time (ERT) of the ventilation system is 108 months but for a range of 100 to 120 months, the total cost remains almost constant. Sensitivity and regression analysis showed the operating cost has the largest impact on the ERT.

    Originality/value

    The results are promising; the company has the possibility of significantly reducing the LCC of the ventilation system by optimising its lifetime. In addition, the proposed model can be used for other systems with repairable components, making it applicable, useful, and implementable within Trafikverket more generally.

  • 2.
    Al-Chalabi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Al-Douri, Yamur
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Time Series Forecasting using ARIMA Model: A Case Study of Mining Face Drilling Rig2018Conference paper (Refereed)
    Abstract [en]

    This study implements an AutoregressiveIntegrated Moving Average (ARIMA) model to forecast totalcost of a face drilling rig used in the Swedish mining industry.The ARIMA model shows different forecasting abilities usingdifferent values of ARIMA parameters (p, d, q). However,better estimation for the ARIMA parameters is required foraccurate forecasting. Artificial intelligence, such as multiobjective genetic algorithm based on the ARIMA model, couldprovide other possibilities for estimating the parameters. Timeseries forecasting is widely used for production control,production planning, optimizing industrial processes andeconomic planning. Therefore, the forecasted total cost data ofthe face drilling rig can be used for life cycle cost analysis toestimate the optimal replacement time of this rig.

  • 3.
    Al-Douri, Yamur
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data imputing using genetic algorithms (GA): A case study of cost data for tunnel fans2017Conference paper (Refereed)
  • 4.
    Al-Douri, Yamur K.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data imputing using generic algorithms (GA)2017In: Mine Planning and Equipment Selection (MPES 2017): Proceeding of the 26th International Symposium on Mine Planning and Equipment Selection Luleå, Sweden, August 29-31, 2017 / [ed] Behzad Ghodrati, Uday Kumar, Håkan Schunnesson, Luleå: Luleå tekniska universitet, 2017, p. 205-208Conference paper (Refereed)
  • 5.
    Al-Douri, Yamur K.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans2018In: Algorithms, ISSN 1999-4893, Vol. 11, no 8, article id 123Article in journal (Refereed)
    Abstract [en]

    The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

  • 6.
    Al-Douri, Yamur K.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Zhang, Liangwei
    Department of Industrial Engineering, School of Mechanical Engineering, Dongguan University of Technology, 523808 Dongguan, China.
    Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans2018In: Cogent Engineering, ISSN 2331-1916, Vol. 5, no 1, p. 1-16, article id 1513304Article in journal (Refereed)
    Abstract [en]

    Data clustering captures natural structures in data consisting of a set of objects and groups similar data together. The derived clusters can be used for scale analysis and to posit missing data values in objects, as missing data have a negative effect on the computational validity of models. This study develops a new two-level multi-objective genetic algorithm (GA) to optimize clustering in order to redact and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level uses a multi-objective GA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Our clustering model is validated using k-means clustering. The second level uses a multi-objective GA to impute the missing cost redacted data in size using a valid data period. The optimal population has a low , 0.1%, and a high intensity, 99%. It has three cluster centres, with the highest data reduction of 27%. These three cluster centres have a suitable geometry, so the cost data can be partitioned into relevant contents to be redacted for imputing. Our model show better clustering detection and evaluation compared with k-means. The amount of missing data for the two cost objects are: labour 57%, materials 81%. The second level shows highly correlated data (R-squared 0.99) after imputing the missing data objects. Therefore, multi-objective GA can cluster and impute data to derive complete data that can be used for better estimation of forecasting.

  • 7.
    Ghodrati, Behzad
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hoseinie, Hadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Environmental friendly manufacturing and support: Issues and challenges2015Conference paper (Refereed)
    Abstract [en]

    Environmentally Conscious Manufacturing and Product Support (ECMPS) is animportant issue driven by concern for the escalating deterioration of the environment.ECMPS involves integrating environmental thinking into the design of a product, theselection of materials, manufacturing processes, delivery and support to consumers, andend-of-life management of the product after its useful life has ended. Both academia andindustry are interested in finding solutions in this newly emerging research area. Relatedresearch is on pollution prevention, remanufacturing, disassembly, life cycle of products,after sale support and material recovery. The aim of this study is emphasizing the productdesign, operation, maintenance and disassembly effects on environment, and how theseissues can be considered in manufacturing phase to minimize the negative environmentalimpact.

  • 8.
    Hamodi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Reliability and Life Cycle Cost Modelling of Mining Drilling Rigs2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the context of mining, drilling is the process of making holes in the face and walls of underground mine rooms, to prepare those rooms for the subsequent operation, which is the charging process. Due to the nature of the task, drilling incurs a high number of drilling rig failures. Through a combination of a harsh environment (characterised by dust, high humidity, etc.), the operating context, and reliability and maintainability issues, drilling rigs are identified as a major contributor to unplanned downtime.The purpose of the research performed for this thesis has been to develop methods that can be used to identify the problems affecting drilling rig downtime and to identify the economic lifetime of drilling rigs. New models have been developed for calculating the optimum replacement time of drilling rigs. These models can also be used for other machines which have repairable or replaceable components. Based on an analysis performed in a case study, a life cycle cost (LCC) optimization model has been developed, taking the most important factors affecting the economic replacement time of drilling rigs into consideration. To this end, research literature studies, case studies, and simulation studies have been performed, interviews have been held, observations have been made and data have been collected. For the data analysis, theories and methodologies within reliability, maintainability, ergonomics and optimization have been combined with the best practices from the related industries.Firstly, this thesis analyses the downtime of the studied drilling rigs, with the precision and uncertainty of the estimation at a given confidence level, along with the factors influencing the failures. Secondly, the thesis identifies components that significantly contribute to the downtime and the reason for that downtime (maintainability and/or reliability problems). Based on the failure analysis, some minor suggestions have been made as to how to improve the critical components of the drilling rig. Thirdly, a new method is proposed that can help decision makers to identify the replacement time of reparable equipment from an economic point of view. Finally, the thesis proposes a method using the artificial neural network (ANN) for predicting the economic lifetime of drilling rigs through a series of basic weights and response functions. This ANN-based method can be made available to engineers without the use of complicated software.Most of the results are related to specific industrial and scientific challenges, such as planning for cost-effectiveness. The results of the case study are promising for the possibility of making a significant reduction in the LCC by optimizing the lifetime. The results have been verified through interaction with experienced practitioners from both the manufacturer and the mining company using the drilling rig in question.

  • 9.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ahmadzadeh, Farzaneh
    Division of Product Realization, Mälardalen University, Eskilstuna.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Economic lifetime prediction of a mining drilling machine using an artificial neural network2014In: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, no 5, p. 311-322Article in journal (Refereed)
    Abstract [en]

    This study develops models for predicting the economic lifetime of drilling machines used in mining. It uses three cases, each represented by a MATLAB code, to develop an optimisation model. The resulting ORT is fed as input to an artificial neural network (ANN) and the results translated into a relatively simple equation. The study finds that increasing the purchase price and decreasing the operating and maintenance costs will increase a machine's ORT linearly. Decreased maintenance cost has the largest impact on ORT, followed by increased purchase price and decreased operating cost. The ANN method gives a series of basic weight and response functions which can be made available to any engineer without the use of complicated software. It also helps decision-makers determine the best time economically to replace an old machine with a new one; thus, it can be extended to more general applications in the mining industry

  • 10.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Aljumaili, Mustafa
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Data Quality of Maintenance Data: A Case Study in MAXIMO CMMS2017In: Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden / [ed] Diego Galar, Dammika Seneviratne, Luleå: Luleå tekniska universitet, 2017, p. 105-110Conference paper (Refereed)
    Abstract [en]

    Computerised maintenance management systems (CMMS) are software packages; their data include information on an organisation’s maintenance, operations and costs. MAXIMO is recognised as a leading CMMS for asset management. It helps to manage maintenance data, improving data quality, making maintenance more efficient, and supporting decision making. However, MAXIMO systems have problems of data quality, with a resulting impact on efficiency and the validity of decisions based on those data. This paper investigates the quality of maintenance data in MAXIMO using the Swedish Transport Agency (Trafikverket) as a case study. It discusses the results before and after data cleaning to show the impact of data quality problems on data analysis.

  • 11.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hoseinie, Hadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Monte Carlo Reliability Simulation of Underground Drilling Rigs2015Conference paper (Refereed)
  • 12.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Hoseinie, Hadi
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Monte Carlo Reliability Simulation of Underground Mining Drilling Rig2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 633-643Conference paper (Refereed)
    Abstract [en]

    Drilling rigs are widely used in mine development or construction and tunnel engineering projects. The rig consists of 12 subsystems in a series configuration and can be driven by diesel or electrical engines. This paper uses the Kamat-Riley (K-R) event-based Monte Carlo simulation method to perform reliability analysis of an underground mine drilling rig. For data analysis and to increase statistical accuracy, the paper discusses three case studies in an underground mine in Sweden. Researchers built a process to programme the simulation process and used MATLABTM software to run simulations. The results showed the simulation approach is applicable to the reliability analysis of this rig. Moreover, the reliability of all rigs reaches almost zero value after 50 h of operation. Finally, the differences between the reliability of the studied fleet of drilling rigs are a maximum 10 %. Therefore, all maintenance or spare part planning issues can be managed in a similar way

  • 13.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Replacement team of mining drilling rigs2014In: Proceedings of Maintenance Performance Measurement and Management: th - 5th September 2014 Coimbra, Portugal : (MPMM 2014), 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a practical model to calculate the optimal replacement time (ORT) of drilling rigs used in underground mining. As a case study, cost data for drilling rig were collected over four years from a Swedish mine. The cost data include acquisition, operating, maintenance and downtime costs when using a redundant rig. A discount rate is used to determine the value of these costs over time. The study develops an optimisation model to identify the ORT of a mining drilling rig which represents a key performance indicator. It uses an artificial neural network (ANN) technique to identify the effect of the various cost factors on the ORT. The absolute ORT in the case study is 87 months, and there is an optimal replacement range within which the company can replace the rig. The results also show that the redundant rig cost has the largest impact on the ORT followed by acquisition, maintenance and operating costs. Regression analysis shows a linear relationship between the cost factors and the ORT of the drilling rig.

  • 14.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Al-Gburi, Majid
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Construction Engineering.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Model for economic replacement time of mining production rigs including redundant rig costs2015In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 21, no 2, p. 207-226Article in journal (Refereed)
    Abstract [en]

    Purpose - This paper presents a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine. Design/methodology/approach - Four years of cost data are collected. Data is analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.Findings - The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT. Practical implications - The proposed approach can be used by the decision maker in determining the economic replacement time of production machines which used in mining industry.Originality/value - The research proposed in this paper provides and develops an optimisation model for economic replacement time of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.Keywords Decision support model, Life cycle cost, Optimisation, Replacement timePaper type Research paper

  • 15.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jonsson, Adam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Economic lifetime of a drilling machine: a case study on mining industry2015In: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 2, no 2, p. 177-189Article in journal (Refereed)
    Abstract [en]

    Underground mines use many different types of machinery duringthe drift mining processes of drilling, charging, blasting, loading, scaling andbolting. Drilling machines play a critical role in the mineral extraction processand thus are important economically. However, as the machines age, theirefficiency and effectiveness decrease, negatively affecting productivity andprofitability and increasing total cost. Hence, the economic replacementlifetime of the machine is a key performance indicator. This paper introducesan optimisation model that gives the optimal lifetime for a drilling machine. Acase study has been done at an underground Swedish mine to identify theeconomic replacement time of a drilling machine. It considers the purchaseprice, maintenance and operation costs, and the machine’s second-hand value.Findings show that the economic replacement lifetime of a drilling machine inthis mine is 96 months. The proposed model can be used for other undergroundmining machines.

  • 16.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jonsson, Adam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Economic lifetime of a drilling machine: a case study on mining industry2013In: MPMM 2013 (Maintenance Performance Measurement and Management) / [ed] sari Monto; Miia Pirttilä; Timo Kärri, Lappeenranta, Finland: MPMM 2013 , 2013, p. 138-147Conference paper (Refereed)
    Abstract [en]

    Underground mines use many different types of machinery during the drift mining processesof drilling, charging, blasting, loading, scaling and bolting. Drilling machines play a criticalrole in the mineral extraction process and thus are important economically. However, as themachines age, their efficiency and effectiveness decrease, negatively affecting productivityand profitability and increasing total cost. Hence, the economic replacement lifetime of themachine is a key performance indicator. This paper introduces an optimisation model thatgives the optimal lifetime for a drilling machine. A case study has been done at anunderground Swedish mine to identify the economic replacement time of a drilling machine.It considers the purchase price, maintenance and operation costs, and the machine’s secondhandvalue. Findings show that the economic replacement lifetime of a drilling machine inthis mine is 96 months. The proposed model can be used for other underground miningmachines.

  • 17.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jonsson, Adam
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Ahmadi, Alireza
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Case Study: Model for economic lifetime of drilling machines in the Swedish mining industry2015In: The Engineering Economist, ISSN 0013-791X, E-ISSN 1547-2701, Vol. 60, no 2, p. 138-154Article in journal (Refereed)
    Abstract [en]

    The purpose of this paper is to develop a practical economic replacement decision model to identify the economic lifetime of a mining drilling machine. A data driven optimisation model was developed for operating and maintenance costs, purchase price and machine resale value. Equivalent present value of these costs by using discount rate was considered. The proposed model shows that the absolute optimal replacement time (ORT) of a drilling machine used in one underground mine in Sweden is 115 months. Sensitivity and regression analysis show that the maintenance cost has the largest impact on the ORT of this machine. The proposed decision making model is applicable and useful and can be implemented within the mining industry.

  • 18.
    Hamodi, Hussan
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Wijaya, Andi
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Downtime analysis of drilling machines and suggestions for improvements2014In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 20, no 4, p. 306-332Article in journal (Refereed)
    Abstract [en]

    Purpose– The purpose of this paper is to analyse and compare the downtime of four drilling machines used in two underground mines in Sweden. The downtime of these machines was compared to show what problems affect downtime and which strategies should be applied to reduce it.Design/methodology/approach– The study collects failure data from a two-year period for four drilling machines and performs reliability analysis. It also performs downtime analysis utilising a log-log diagram with a confidence interval.Findings– There are notable differences in the downtime of most of the studied components for all machines. The hoses and feeder have relatively high downtime. Depending on their downtime, the significant components can be ranked in three groups. The downtime of the studied components is due to reliability problems. The study suggests the need to improve the reliability of critical components to reduce the downtime of drilling machines.Originality/value– The method of analysing the downtime, identifying dominant factors and the interval estimation for the downtime, has never been studied on drilling machines. The research proposed in this paper provides a general method to link downtime analysis with potential component improvement. To increase the statistical accuracy; four case studies was performed in two different mines with completely different working environment and ore properties. Using the above method showed which components need to be improved and suggestions for improvement was proposed and will be implemented accordingly.

  • 19.
    Hoseinie, Seyed Hadi
    et al.
    Department of Mining Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
    Al-Chalabi, Hussan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Ghodrati, Behzad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Comparison between Simulation and Analytical Methods in Reliability Data Analysis: A Case Study on Face Drilling Rigs2018In: Data, ISSN 2306-5729, Vol. 3, no 2, article id 12Article in journal (Refereed)
    Abstract [en]

    Collecting the failure data and reliability analysis in an underground mining operation is challenging due to the harsh environment and high level of production pressure. Therefore, achieving an accurate, fast, and applicable analysis in a fleet of underground equipment is usually difficult and time consuming. This paper aims to discuss the main reliability analysis challenges in mining machinery by comparing three main approaches: two analytical methods (white-box and black-box modeling), and a simulation approach. For this purpose, the maintenance data from a fleet of face drilling rigs in a Swedish underground metal mine were extracted by the MAXIMO system over a period of two years and were applied for analysis. The investigations reveal that the performance of these approaches in ranking and the reliability of the studies of the machines is different. However, all mentioned methods provide similar outputs but, in general, the simulation estimates the reliability of the studied machines at a higher level. The simulation and white-box method sometimes provide exactly the same results, which are caused by their similar structure of analysis. On average, 9% of the data are missed in the white-box analysis due to a lack of sufficient data in some of the subsystems of the studies’ rigs.

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