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Teymourian, K., Seneviratne, D. & Galar, D. (2019). Integrating Ergonomics in Maintanability: A Case Study from Manufacturing Industry. Journal of Industrial Engineering and Management Science, 2018(1), 131-150, Article ID 8.
Open this publication in new window or tab >>Integrating Ergonomics in Maintanability: A Case Study from Manufacturing Industry
2019 (English)In: Journal of Industrial Engineering and Management Science, E-ISSN 2446-1822, Vol. 2018, no 1, p. 131-150, article id 8Article in journal (Refereed) Published
Abstract [en]

Maintainability is key part of Reliability, Availability, Maintainability and Safety (RAMS) estimation and prediction in complex assets. Indeed, availability calculation comprises accurate estimation of maintainability and frequently, it is just a time stamp for mean time to repair (MTTR) estimations. However, maintainability is a human related figure where the skill, capabilities, tools and the design of the asset play key role in its performance. The aim of this article is to describe the effects of ergonomists’ contribution during maintainability process for system/products design. System designer thinking in system and its subsystem in a way of technical functionality. On the other hand, ergonomists are expertise in human capability and limitation. If human become a part of system than their interface and interaction become crucial factors in a success of system performance and its sustainability. In this paper, it has discussed three main issues that help the process of maintainability design. These issues are safety, task analysis and risk analysis. It has also touched reliability engineer’s task to increase Overall Equipment Effectiveness (OEE). These issues are explained via a case study from a manufacturing industry.

Place, publisher, year, edition, pages
River Publishers, 2019
Keywords
Maintainability, Hierarchical Task Analysis (HTA), Ergonomics, Risk, Safety
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-74042 (URN)10.13052/jiems2446-1822.2018.008 (DOI)
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-05-27Bibliographically approved
Zhang, S., Liu, H., Qiang, J., Gao, H., Galar, D. & Lin, J. (2019). Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem. CMES - Computer Modeling in Engineering & Sciences, 119(2), 373-394
Open this publication in new window or tab >>Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem
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2019 (English)In: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 119, no 2, p. 373-394Article in journal (Refereed) Published
Abstract [en]

Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X0 , Y0) and release time (T0), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed. The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index. The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events. Based on the optimized monitoring well position and sampling frequency, the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach. The case study results show that the following parameters were obtained: 1) the optimal monitoring well position (D) is at (445, 200); and 2) the optimal monitoring frequency (Δt) is 7, providing that the monitoring events is set as 5 times. Employing the optimized monitoring well position and frequency, the mean errors of inverse modeling results in source parameters (M, X0 ,Y0 ,T0 ) were 9.20%, 0.25%, 0.0061%, and 0.33%, respectively. The optimized monitoring well position and sampling frequency can effectively safeguard the inverse modeling results in identifying the contamination source parameters. It was also learnt that the improved Metropolis-Hastings algorithm (a Markov chain Monte Carlo method) can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization, which significantly improved the accuracy and numerical stability of the inverse modeling results.

Place, publisher, year, edition, pages
Tech Science Press, 2019
Keywords
Contamination source identification, monitoring well optimization, Bayes’ Theorem, information entropy, differential evolution algorithm, Metropolis Hastings algorithm, Latin hypercube sampling
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-74424 (URN)10.32604/cmes.2019.03825 (DOI)000466023600010 ()2-s2.0-85065227402 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-06-12 (johcin)

Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-25Bibliographically approved
Raposo, H., Torres Farinha, J., Fonseca, I. & Galar, D. (2019). Predicting condition based on oil analysis: A case study. Tribology International, 135, 65-74
Open this publication in new window or tab >>Predicting condition based on oil analysis: A case study
2019 (English)In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 135, p. 65-74Article in journal (Refereed) Published
Abstract [en]

The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Condition monitoring, Predictive maintenance, Oil analysis, Time series, t-Student, Diesel engines
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73083 (URN)10.1016/j.triboint.2019.01.041 (DOI)000469157500007 ()2-s2.0-85062372547 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-13 (johcin)

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-06-20Bibliographically approved
González-González, A., Jimenez Cortadi, A., Galar, D. & Ciani, L. (2018). Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach. Measurement, 123, 80-93
Open this publication in new window or tab >>Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach
2018 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, p. 80-93Article in journal (Refereed) Published
Abstract [en]

With the increase of wind power capacity worldwide, researchers are focusing their attention on the operation and maintenance of wind turbines. A proper pitch controller must be designed to extend the life cycle of a wind turbine’s blades and tower. The pitch control system has two main, but conflicting, objectives: to maximize the wind energy captured and converted into electrical energy and to minimize fatigue and mechanical load. Four metrics have been proposed to balance these two objectives. Also, diverse pitch controller strategies are proposed in this paper to evaluate these objectives. This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus. It uses a 100 kW wind turbine as a case study to simulate the proposed pitch control strategies and evaluate with the metrics proposed. The results are showed in two tables due to two different wind models are used.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-68106 (URN)10.1016/j.measurement.2018.01.047 (DOI)000432748600011 ()2-s2.0-85044546969 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-04-03 (andbra)

Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-06-07Bibliographically approved
Diez-Olivan, A., Del Ser, J., Galar, D. & Sierra, B. (2018). Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0. Information Fusion, 50, 92-111
Open this publication in new window or tab >>Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 92-111Article in journal (Refereed) Published
Abstract [en]

The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Data-driven prognosis, Data Fusion, Machine learning, Industry 4.0
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71259 (URN)10.1016/j.inffus.2018.10.005 (DOI)000466056900008 ()2-s2.0-85055203735 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-10-31 (svasva)

Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-06-19Bibliographically approved
Teymourian, K., Seneviratne, D. & Galar, D. (2018). Ergonomics in Maintainability: System and Product Design Process. In: Proceedings of Maintenance Preformance Measurement and Magangement (MPMM): . Paper presented at Maintenance Preformance Measurement and Management, 21-22 June 2018, Coimbra, Portugal.
Open this publication in new window or tab >>Ergonomics in Maintainability: System and Product Design Process
2018 (English)In: Proceedings of Maintenance Preformance Measurement and Magangement (MPMM), 2018Conference paper, Published paper (Refereed)
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73647 (URN)
Conference
Maintenance Preformance Measurement and Management, 21-22 June 2018, Coimbra, Portugal
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15
D'Emilia, G., Gaspari, A. & Galar, D. (2018). Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology. International Journal of Service Science, Management, Engineering, and Technology, 9(2), 85-103, Article ID 4.
Open this publication in new window or tab >>Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology
2018 (English)In: International Journal of Service Science, Management, Engineering, and Technology, ISSN 1947-959X, Vol. 9, no 2, p. 85-103, article id 4Article in journal (Refereed) Published
Abstract [en]

The evolution of systems based on the integration of Internet of Things (IoT) and Cloud computing technologies requires resolute and trustable management approaches, to let the industrial assets thrive and avoid losses in efficiency and, thus, profitability. In this article, a methodology based on the evaluation of the measurement uncertainty is proposed, which is able to suggest possible improvement paths and reliable decisions. The approach is based on the identification of subsequent tasks that should be fulfilled, also in a recursive way. Its application in the field, for the identification of the vibration and acoustic emission signatures of highly-performance machining tools, allows directing future actions to increase the potentiality of proper management of the information provided by measurements. In a complex scenario, characterized by many devices and instruments, the compliance with the procedures for measurement accuracy has proven to be a useful support.

Place, publisher, year, edition, pages
IGI Global, 2018
Keywords
Centerless Grinding, Condition Monitoring, Internet of Things (IoT), Measurement Uncertainty
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72818 (URN)10.4018/IJSSMET.2018040104 (DOI)2-s2.0-85060732461 (Scopus ID)
Available from: 2019-02-07 Created: 2019-02-07 Last updated: 2019-02-07Bibliographically approved
Kumar, U. & Galar, D. (2018). Maintenance in the Era of Industry 4.0: Issues and Challenges. In: Kapur P., Kumar U., Verma A. (Ed.), Quality, IT and Business Operations: Modeling and Optimization (pp. 231-250). Singapore: Springer
Open this publication in new window or tab >>Maintenance in the Era of Industry 4.0: Issues and Challenges
2018 (English)In: Quality, IT and Business Operations: Modeling and Optimization / [ed] Kapur P., Kumar U., Verma A., Singapore: Springer, 2018, p. 231-250Chapter in book (Refereed)
Abstract [en]

The fourth generation of industrial activity enabled by smart systems and Internet-based solutions is known as Industry 4.0. Two most important characteristic features of Industry 4.0 are computerization using cyber-physical systems and the concept of “Internet of Things” adopted to produce intelligent factories. As more and more devices are instrumented, interconnected and automated to meet this vision, the strategic thinking of modern-day industry has been focused on deployment of maintenance technologies to ensure failure-free operation and delivery of services as planned.

Maintenance is one of the application areas, referred to as Maintenance 4.0, in the form of self-learning and smart system that predicts failure, makes diagnosis and triggers maintenance. The paper addresses the new trends in manufacturing technology based on the capability of instrumentation, interconnection and intelligence together with the associated maintenance challenges in the era of collaborative machine community and big data environment.

The paper briefly introduces the concept of Industry 4.0 and presents maintenance solutions aligned to the need of the next generation of manufacturing technologies and processes being deployed to realize the vision of Industry 4.0.The suggested maintenance approach to deal with new challenges due to the implementation of industry 4.0 is captured within the framework of eMaintenance solutions developed using maintenance analytics. The paper is exploratory in nature and is based on literature review and study of the current development in maintenance practices aligned to industry 4.0.

Place, publisher, year, edition, pages
Singapore: Springer, 2018
Series
Springer Proceedings in Business and Economics, ISSN 2198-7246
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65958 (URN)10.1007/978-981-10-5577-5_19 (DOI)978-981-10-5576-8 (ISBN)978-981-10-5577-5 (ISBN)
Available from: 2017-10-04 Created: 2017-10-04 Last updated: 2017-11-24Bibliographically approved
Raposo, H., Farinha, J. T., Ferreira, L. A. & Galar, D. (2017). An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance. Eksploatacja i Niezawodnosc, 19(3), 358-368
Open this publication in new window or tab >>An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance
2017 (English)In: Eksploatacja i Niezawodnosc, ISSN 1507-2711, Vol. 19, no 3, p. 358-368Article in journal (Refereed) Published
Abstract [en]

Maintenance policies influence equipment availability and, thus, they affect a company’s capacity for productivity and competitiveness. It is important to optimize the Life Cycle Cost (LCC) of assets, in this case, passenger bus fleets. The paper presents a predictive condition monitoring maintenance approach based on engine oil analysis, to assess the potential impact of this variable on the availability of buses. The approach has implications on maintenance costs during the life of a bus and, consequently, on the determination of the best time for bus replacement. The paper provides an overview of economic replacement models through a global model, with an emphasis on availability and its dependence on maintenance and maintenance costs. These factors help to determine the size of the reserve fleet and guarantee availability

Abstract [pl]

Polityka konserwacji wpływa na gotowość sprzętu, a tym samym na wydajność i konkurencyjność przedsiębiorstwa. Ważne jestoptymalizowanie kosztów cyklu życia (LCC) aktywów, w tym przypadku taboru autobusowego. W artykule przedstawiono metodęutrzymania ruchu polegającą na predykcyjnym monitorowaniu stanu w oparciu o analizę oleju silnikowego w celu oceny potencjalnegowpływu tej zmiennej na gotowość autobusów. Podejście to ma praktyczne konsekwencje jeśli chodzi o koszty utrzymaniaw trakcie eksploatacji autobusu, a także pozwala na ustalenie najlepszego czasu na wymianę pojazdów taboru. W pracy przedstawionoprzegląd ekonomicznych modeli wymiany oraz opracowano model globalny integrujący te modele, ze szczególnymuwzględnieniem gotowości oraz jej zależności od konserwacji oraz kosztów utrzymania ruchu. Czynniki te pomagają określićwielkość floty rezerwowej i zapewnić gotowość taboru.

Place, publisher, year, edition, pages
Polish Academy of Sciences Branch, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-64691 (URN)10.17531/ein.2017.3.6 (DOI)000403344800006 ()2-s2.0-85020500047 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-06-30 (andbra)

Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2018-07-10Bibliographically approved
Leturiondo, U., Salgado, O., Ciani, L., Galar, D. & Catelani, M. (2017). Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measurement, 108, 152-162
Open this publication in new window or tab >>Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data
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2017 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, p. 152-162Article in journal (Refereed) Published
Abstract [en]

The advances in technology involving internet of things, cloud computing and big data mean a new perspective in the calculation of reliability, maintainability, availability and safety by combining physics-based modelling with data-driven modelling. This paper proposes an architecture to implement hybrid modelling based on the fusion of real data and synthetic data obtained in simulations using a physics-based model. This architecture has two levels of analysis: an online process carried out locally and virtual commissioning performed in the cloud. The former results in failure detection analysis to avoid upcoming failures whereas the latter leads to both diagnosis and prognosis. The proposed hybrid modelling architecture is validated in the field of rotating machinery using time-domain and frequency-domain analysis. A multi-body model and a semi-supervised learning algorithm are used to perform the hybrid modelling. The state of a rolling element bearing is analysed and accurate results for fault detection, localisation and quantification are obtained. The contextual information increases the accuracy of the results; the results obtained by the model can help improve maintenance decision making and production scheduling. Future work includes a prescriptive analysis approach.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61838 (URN)10.1016/j.measurement.2017.02.003 (DOI)000404201400016 ()2-s2.0-85012901785 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-06-07 (rokbeg)

Available from: 2017-02-06 Created: 2017-02-06 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4107-0991

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