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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 21) Show all publications
Al-Chalabi, H. (2022). Development of an economic replacement time model for mining equipment: a case study. Life Cycle Reliability and Safety Engineering, 11(2), 203-217
Open this publication in new window or tab >>Development of an economic replacement time model for mining equipment: a case study
2022 (English)In: Life Cycle Reliability and Safety Engineering, ISSN 2520-1352, Vol. 11, no 2, p. 203-217Article in journal (Refereed) Published
Abstract [en]

In mining operation equipment replacement represents a strategic decision problem. This paper presents an economic replacement time model for mining drill rigs. A total ownership cost minimization model was developed to optimize the lifetime of a drill rig used in Tara underground mine in Ireland. The developed methodology allows an innovative practical evaluation of the replacement process by applying sensitivity and regression analysis to rank the factors affecting the replacement time of existing and new models of the production drill rig. Compared to previous studies presented in the literature, the present study represents a further development in this field as it has resulted in a practical optimization model that can be used to estimate the economic replacement time of repairable equipment used in the mining and other production industries. The proposed model shows that the absolute economic replacement time of the drill rig investigated in this case study is 81 months and the mining company operating the rig can replace it with an identical one within an optimal replacement range of 6 months (i.e. from month 79–84) when the minimum total cost can still be achieved in practice. Sensitivity and regression analyses show that the maintenance cost has the largest impact on the economic replacement time of the drill rig. The study finds that decreasing the operating and maintenance costs of the drill rig will have the positive effect of increasing the economic replacement time linearly for a new model of the drill rig. The proposed model helps decision-makers to plan the replacement of old rigs and purchase new ones from an economic view point. Thus, this new model can be extended and used for more general applications in the mining industry.

Place, publisher, year, edition, pages
Springer Nature, 2022
Keywords
Economic replacement time model, Decision support model, LCC analysis, Mining drill rig
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-90146 (URN)10.1007/s41872-022-00188-1 (DOI)2-s2.0-85169090383 (Scopus ID)
Funder
Luleå University of TechnologyVinnovaSwedish Research Council Formas
Note

Validerad;2022;Nivå 1;2022-07-26 (hanlid);

Funder: Boliden Mineral AB, Epiroc Rock Drills AB

Available from: 2022-04-11 Created: 2022-04-11 Last updated: 2023-10-11Bibliographically approved
Castaño Arranz, M., Gustafson, A. & Al-Chalabi, H. (2020). A generic framework for data quality analytics. International Journal of COMADEM, 23(1), 31-38
Open this publication in new window or tab >>A generic framework for data quality analytics
2020 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 23, no 1, p. 31-38Article in journal (Refereed) Published
Abstract [en]

The challenge of generalizing Data Quality assessment is hindered by the fact that Data Quality requisites depend on the purpose for which the data will be used and on the subjectivity of the data consumer. The approach proposed in this paper to address this challenge is to employ a semi-automated user-guided Data Quality assessment. This paper introduces a generic framework for data quality analytics which is mainly composed by a set of software units to perform semi-automated Data Quality analytics and a set of Graphical User Interfaces to enable the user to guide the Data Quality assessment. The framework has been implemented and can be customized according to the needs of the purpose and of the consumer. The framework has been instantiated in a case study on Long-hole drill rigs, where several Data Quality issues have been discovered and their root cause investigated.

Place, publisher, year, edition, pages
COMADEM International, 2020
Keywords
Data Quality, Maintenance, Long-hole drilling, Mining
National Category
Other Civil Engineering
Research subject
Operation and Maintenance; Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-78501 (URN)2-s2.0-85088900505 (Scopus ID)
Projects
IDQ4LCCAIF/R
Funder
Vinnova
Note

Validerad;2020;Nivå 1;2020-04-21 (alebob)

Available from: 2020-04-15 Created: 2020-04-15 Last updated: 2020-09-02Bibliographically approved
Al-Douri, Y. K., Al-Chalabi, H. & Lundberg, J. (2020). Risk-based life cycle cost analysis using a two-level multi-objective genetic algorithm. Paper presented at 1st International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2019), 17-19 October, 2019, Dublin, Ireland. International journal of computer integrated manufacturing (Print), 33(10-11), 1076-1088
Open this publication in new window or tab >>Risk-based life cycle cost analysis using a two-level multi-objective genetic algorithm
2020 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 33, no 10-11, p. 1076-1088Article in journal (Refereed) Published
Abstract [en]

The aim of this study has been to develop a two-level multi-objective genetic algorithm (MOGA) to optimize risk-based LCC analysis to find the optimal maintenance replacement time for road tunnel ventilation fans. Level 1 uses a MOGA based on a financial risk model to provide different risk percentages, while level 2 uses a MOGA based on an LCC model to estimate the optimal fan replacement time. Our method is compared with the approach of using a risk-based LCC model. The results are promising, showing that the risk-based LCC offers the possibility of significantly reducing the maintenance costs of the ventilation system by optimising the replacement schedule by considering the risk costs. The risk-based LCC can be used with repairable components, making it applicable, useful and implementable within Swedish Transport Administration (Trafikverket). In this study, MOGA operators have selected the cost of maintenance and risk data through the previous levels using different ways to provide different possible solutions. A drawback of the MOGA based on a risk-based LCC model with regard to its estimation is that a late replacement period over 20-year period might increase the maintenance cost. Therefore, the MOGA does not provide a good solution for a risk-based LCC.

Place, publisher, year, edition, pages
Taylor & Francis, 2020
Keywords
Life cycle cost (LCC), multi-objective genetic algorithm (MOGA), risk-based life cycle cost, optimal maintenance replacement time, optimization
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-78947 (URN)10.1080/0951192X.2020.1757157 (DOI)000534126700001 ()2-s2.0-85084842349 (Scopus ID)
Conference
1st International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2019), 17-19 October, 2019, Dublin, Ireland
Note

Godkänd;2020;Nivå 0;2020-12-03 (alebob);Konferensartikel i tidskrift

Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-12-03Bibliographically approved
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, E-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
Keywords
Data clustering, data imputing, multi-objective GA, fuzzy c-means, K-means clustering
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-70375 (URN)10.1080/23311916.2018.1513304 (DOI)000444436800001 ()2-s2.0-85052696347 (Scopus ID)
Note

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

Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2023-09-04Bibliographically 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)000441395400006 ()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: 2020-08-26Bibliographically 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, E-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: 2023-03-29Bibliographically approved
Al-Chalabi, H., Al-Douri, Y. K. & Lundberg, J. (2018). Time Series Forecasting using ARIMA Model: A Case Study of Mining Face Drilling Rig. In: Claus-Peter Rückemann; Ahmad Rafi Qawasmeh (Ed.), ADVCOMP 2018: The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences. Paper presented at 12th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2018), Athens, Greece, November 18-22, 2018 (pp. 1-3). International Academy, Research and Industry Association (IARIA)
Open this publication in new window or tab >>Time Series Forecasting using ARIMA Model: A Case Study of Mining Face Drilling Rig
2018 (English)In: ADVCOMP 2018: The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences / [ed] Claus-Peter Rückemann; Ahmad Rafi Qawasmeh, International Academy, Research and Industry Association (IARIA), 2018, p. 1-3Conference paper, Published paper (Refereed)
Abstract [en]

This study implements an Autoregressive Integrated Moving Average (ARIMA) model to forecast total cost of a face drilling rig used in the Swedish mining industry. The ARIMA model shows different forecasting abilities using different values of ARIMA parameters (p, d, q). However, better estimation for the ARIMA parameters is required for accurate forecasting. Artificial intelligence, such as multi objective genetic algorithm based on the ARIMA model, could provide other possibilities for estimating the parameters. Time series forecasting is widely used for production control, production planning, optimizing industrial processes and economic planning. Therefore, the forecasted total cost data of the face drilling rig can be used for life cycle cost analysis to estimate the optimal replacement time of this rig.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2018
Series
ADVCOMP, International Conference on Advanced Engineering Computing and Applications in Sciences, ISSN 2308-4499
Keywords
ARIMA model, Data forecasting, Mining face drilling rig
National Category
Mineral and Mine Engineering Computer Sciences
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71774 (URN)000464893300001 ()
Conference
12th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2018), Athens, Greece, November 18-22, 2018
Note

ISBN för värdpublikation: 978-1-61208-677-4

Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2021-08-23Bibliographically approved
Al-Douri, Y. K., Al-Chalabi, H. & Lundberg, J. (2018). Time Series Forecasting using Genetic Algorithm: A Case Study of Maintenance Cost Data For Tunnel Fans. In: Claus-Peter Rückemann; Ahmad Rafi Qawasmeh (Ed.), ADVCOMP 2018: The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences. Paper presented at 12th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2018), Athens, Greece, November 18-22, 2018 (pp. 4-9). International Academy, Research and Industry Association (IARIA)
Open this publication in new window or tab >>Time Series Forecasting using Genetic Algorithm: A Case Study of Maintenance Cost Data For Tunnel Fans
2018 (English)In: ADVCOMP 2018: The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences / [ed] Claus-Peter Rückemann; Ahmad Rafi Qawasmeh, International Academy, Research and Industry Association (IARIA) , 2018, p. 4-9Conference paper, Published paper (Refereed)
Abstract [en]

Time series forecasting is widely used as a basis for economic planning, production planning, production control and optimizing industrial processes. The aim of this study has been to develop a novel two-level Genetic Algorithm (GA) to optimize time series forecasting in order to forecast cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level of the GA is responsible for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements GA based on the Autoregressive integrated moving average (ARIMA) model. The second level utilizes a GA based on forecasting error rate to identify a proper forecasting. The results show that GA based on the ARIMA model produces better forecasting results for the labor cost data objects. It was found that a multi-objective GA based on the ARIMA model showed an improved performance. The forecasted data can be used for Life cycle cost (LCC) analysis.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2018
Series
ADVCOMP the International Conference on Advanced Engineering Computing and Applications in Sciences, ISSN 2308-4499
Keywords
ARIMA model, Time series forecasting, Genetic Algorithm (GA), Life Cycle Cost (LCC), Maintenance cost data
National Category
Computer Sciences Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-86798 (URN)000464893300002 ()
Conference
12th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2018), Athens, Greece, November 18-22, 2018
Note

ISBN för värdpublikation:978-1-61208-677-4

Available from: 2021-08-23 Created: 2021-08-23 Last updated: 2021-08-23Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5620-5265

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