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Al-Chalabi, Hussan, Associate Senior LecturerORCID iD iconorcid.org/0000-0001-5620-5265
Alternative names
Biography [eng]

Hussan Al-Chalabi received the Ph.D. degree in operation and maintenance engineering from Luleå University of Technology, Luleå, Sweden, in 2014. He is currently working as an Associate Senior Lecturer with the Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden. He obtained his B.Sc. in Mechanical Engineering from Mosul University, Iraq in 1994. He obtained his M.Sc. in Mechanical Engineering, thermal power from Mosul University, Iraq in 2008. After working for 2 years in Mosul University as a faculty member, he joined the postgraduate program of Luleå University of Technology, Luleå, Sweden and he obtained a PhD degree in Operation and Maintenance Engineering in 2014. He joined Luleå University as a Postdoctoral Researcher since 2015 for two years. He worked as a Senior Researcher in Luleå University of Technology, Sweden since March 2017 for one year. His current research interests are mainly in Operation and Maintenance, Replacement models, Optimization aspects, Reliability Engineering and LCC analysis.

Publications (10 of 16) Show all publications
Al-Douri, Y. K., Hamodi, H. & Zhang, L. (2018). Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans. Cogent Engineering, 5(1), 1-16, Article ID 1513304.
Open this publication in new window or tab >>Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans
2018 (English)In: Cogent Engineering, ISSN 2331-1916, Vol. 5, no 1, p. 1-16, article id 1513304Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-70375 (URN)10.1080/23311916.2018.1513304 (DOI)000444436800001 ()
Note

Validerad;2018;Nivå 2;2018-10-08 (johcin) 

Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2018-10-08Bibliographically approved
Al-Chalabi, H. (2018). Life cycle cost analysis of the ventilation system in Stockholm's road tunnels. Journal of Quality in Maintenance Engineering, 24(3), 358-375
Open this publication in new window or tab >>Life cycle cost analysis of the ventilation system in Stockholm's road tunnels
2018 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 24, no 3, p. 358-375Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2018
Keywords
Capital equipment; Economic replacement time; Life cycle cost analysis; Maintenance; Optimization model; Ventilation system
National Category
Civil Engineering Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-64975 (URN)10.1108/JQME-05-2017-0032 (DOI)2-s2.0-85051506895 (Scopus ID)
Projects
LCC-metodik och koppling till Maximo
Funder
Swedish Transport Administration
Note

Validerad;2018;Nivå 2;2018-08-14 (svasva)

Available from: 2017-08-08 Created: 2017-08-08 Last updated: 2018-08-24Bibliographically approved
Al-Douri, Y. K., Hamodi, H. & Lundberg, J. (2018). Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans. Algorithms, 11(8), Article ID 123.
Open this publication in new window or tab >>Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans
2018 (English)In: Algorithms, ISSN 1999-4893, Vol. 11, no 8, article id 123Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
ARIMA model; data forecasting; multi-objective genetic algorithm; regression model
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-70330 (URN)10.3390/a11080123 (DOI)000443614500015 ()2-s2.0-85052696396 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-14 (inah)

Available from: 2018-08-11 Created: 2018-08-11 Last updated: 2018-12-03Bibliographically approved
Al-Douri, Y. K. & Hamodi, H. (2017). Data imputing using generic algorithms (GA). In: Behzad Ghodrati, Uday Kumar, Håkan Schunnesson (Ed.), 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. Paper presented at 26th International Symposium on Mine Planning and Equipment Selection, Luleå, Sweden, August 29-31, 2017 (pp. 205-208). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Data imputing using generic algorithms (GA)
2017 (English)In: 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, Published paper (Refereed)
Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2017
National Category
Other Mechanical Engineering Mineral and Mine Engineering Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-69094 (URN)978-91-7583-935-6 (ISBN)978-91-7583-936-3 (ISBN)
Conference
26th International Symposium on Mine Planning and Equipment Selection, Luleå, Sweden, August 29-31, 2017
Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-06-04Bibliographically approved
Hamodi, H. & Aljumaili, M. (2017). Data Quality of Maintenance Data: A Case Study in MAXIMO CMMS. In: Diego Galar, Dammika Seneviratne (Ed.), Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden. Paper presented at Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden (pp. 105-110). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Data Quality of Maintenance Data: A Case Study in MAXIMO CMMS
2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63835 (URN)978-91-7583-841-0 (ISBN)
Conference
Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden
Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-11-24Bibliographically approved
Hamodi, H., Hoseinie, H. & Lundberg, J. (2016). Monte Carlo Reliability Simulation of Underground Mining Drilling Rig (ed.). In: (Ed.), Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde (Ed.), Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective. Paper presented at International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015 (pp. 633-643). : Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Monte Carlo Reliability Simulation of Underground Mining Drilling Rig
2016 (English)In: 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, Published 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

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2016
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-40121 (URN)10.1007/978-3-319-23597-4_46 (DOI)f1cf544e-5618-4af2-adba-8b7120f23fe0 (Local ID)978-3-319-23596-7 (ISBN)978-3-319-23597-4 (ISBN)f1cf544e-5618-4af2-adba-8b7120f23fe0 (Archive number)f1cf544e-5618-4af2-adba-8b7120f23fe0 (OAI)
Conference
International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015
Note
Godkänd; 2016; Bibliografisk uppgift: Containing selected papers from the ICRESH-ARMS 2015 conference in Lulea, Sweden, collected by editors with years of experiences in Reliability and maintenance modeling, risk assessment, and asset management, this work maximizes reader insights into the current trends in Reliability, Availability, Maintainability and Safety (RAMS) and Risk Management. Featuring a comprehensive analysis of the significance of the role of RAMS and Risk Management in the decision making process during the various phases of design, operation, maintenance, asset management and productivity in Industrial domains, these proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for those in the field. ; 20151223 (andbra)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-25Bibliographically approved
Hamodi, H., Lundberg, J., Jonsson, A. & Ahmadi, A. (2015). Case Study: Model for economic lifetime of drilling machines in the Swedish mining industry (ed.). Paper presented at . The Engineering Economist, 60(2), 138-154
Open this publication in new window or tab >>Case Study: Model for economic lifetime of drilling machines in the Swedish mining industry
2015 (English)In: The Engineering Economist, ISSN 0013-791X, E-ISSN 1547-2701, Vol. 60, no 2, p. 138-154Article in journal (Refereed) Published
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.

National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Matemathical Statistics
Identifiers
urn:nbn:se:ltu:diva-14705 (URN)10.1080/0013791X.2014.952466 (DOI)000353499300003 ()2-s2.0-84928644059 (Scopus ID)e20b5005-d939-4ebe-a128-136ec348a9c2 (Local ID)e20b5005-d939-4ebe-a128-136ec348a9c2 (Archive number)e20b5005-d939-4ebe-a128-136ec348a9c2 (OAI)
Note
Validerad; 2015; Nivå 2; 20140915 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Hamodi, H., Lundberg, J. & Jonsson, A. (2015). Economic lifetime of a drilling machine: a case study on mining industry (ed.). Paper presented at . International Journal of Strategic Engineering Asset Management (IJSEAM), 2(2), 177-189
Open this publication in new window or tab >>Economic lifetime of a drilling machine: a case study on mining industry
2015 (English)In: 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) Published
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.

Keywords
drilling machine, economic replacement time, optimisation model, asset management., Manufacturing engineering and work sciences - Manufacturing engineering, Produktion och arbetsvetenskap - Produktionsteknik
National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Matemathical Statistics; Future mining (AERI)
Identifiers
urn:nbn:se:ltu:diva-11071 (URN)10.1504/IJSEAM.2015.070624 (DOI)9fab7268-baa4-4e35-b7fc-12b1e6724b34 (Local ID)9fab7268-baa4-4e35-b7fc-12b1e6724b34 (Archive number)9fab7268-baa4-4e35-b7fc-12b1e6724b34 (OAI)
Note
Validerad; 2015; Nivå 1; Bibliografisk uppgift: Hussan Al-Chalabi received his BEng in Mechanical Engineering from Mosul University, Iraq in 1994 and MSc in Mechanical Engineering in Thermal Power from Mosul University, Iraq in 2008. Then he joined the Department of Mechanical Engineering at Mosul University as a lecturer. Since 2011, he joined the Division of Operation, Maintenance and Acoustics at LTU as a doctoral student. Jan Lundberg is a Professor of Machine Elements at Luleå University of Technology and also a Professor in Operation and Maintenance with focus on product development. During the years 1983–2000, his research concerned mainly about engineering design in the field of machine elements in industrial environments. During the years 2000–2006, his research concerned mainly about industrial design, ergonomic and related problems as cultural aspects of design and modern tools for effective industrial design in industrial environments. From 2006 and forward, his research is completely focused on maintenance issues like methods for measuring failure sources, how to do design out maintenance and how to design for easy maintenance. Adam Jonsson is a Senior Lecturer in the Department of Engineering Sciences and Mathematics, Luleå University of Technology, Sweden. He received his PhD in Statistics in 2008. His research is in applied probability and social welfare economics.; 20150224 (hasham)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Ghodrati, B., Hamodi, H. & Hoseinie, H. (2015). Environmental friendly manufacturing and support: Issues and challenges (ed.). Paper presented at International conference on Environmental Engineering and Pollution Technology : 01/10/2015 - 03/10/2015. Paper presented at International conference on Environmental Engineering and Pollution Technology : 01/10/2015 - 03/10/2015.
Open this publication in new window or tab >>Environmental friendly manufacturing and support: Issues and challenges
2015 (English)Conference paper, Oral presentation only (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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-32729 (URN)74e8d6ff-4919-42fc-82e5-c31c356802e5 (Local ID)74e8d6ff-4919-42fc-82e5-c31c356802e5 (Archive number)74e8d6ff-4919-42fc-82e5-c31c356802e5 (OAI)
Conference
International conference on Environmental Engineering and Pollution Technology : 01/10/2015 - 03/10/2015
Note
Godkänd; 2015; 20151204 (behzad)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Hamodi, H., Lundberg, J., Al-Gburi, M., Ahmadi, A. & Ghodrati, B. (2015). Model for economic replacement time of mining production rigs including redundant rig costs (ed.). Paper presented at . Journal of Quality in Maintenance Engineering, 21(2), 207-226
Open this publication in new window or tab >>Model for economic replacement time of mining production rigs including redundant rig costs
Show others...
2015 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 21, no 2, p. 207-226Article in journal (Refereed) Published
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

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-15537 (URN)10.1108/JQME-07-2014-0041 (DOI)000211515300005 ()2-s2.0-84929074579 (Scopus ID)f116c303-ba8e-43b6-9aff-48109010af18 (Local ID)f116c303-ba8e-43b6-9aff-48109010af18 (Archive number)f116c303-ba8e-43b6-9aff-48109010af18 (OAI)
Note
Validerad; 2015; Nivå 1; 20141016 (hasham)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5620-5265

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