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  • 1.
    Asplund, Matthias
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lin, Janet
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Evaluating the measurement capability of a wheel profile measurement system by using GR&R2016In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 92, p. 19-27Article in journal (Refereed)
    Abstract [en]

    Reliable data with less variation play a key role for acceptance of the usefulness of the measurement output of a wheel profile measurement system (WPMS) in a railway network. However, in practice, most studies are carried out without checking the reliability of data from such a system, which may lead to inappropriate maintenance strategies. To ensure the measurement capability of WPMS and to support robust maintenance in railway systems, this study has evaluated measurement data for the flange height, flange thickness, flange slope, and tread hollowing of rolling stock wheels by using gauge repeatability and reproducibility (GR&R). In this study, acceptance and rejection criteria for the precision-to-tolerance ratio (PTR), signal-to-noise ratio (SNR), and discrimination ratio (DR) have been employed to evaluate the measurement capabilities. For the purpose of illustration, we have implemented a new proposed approach. This approach involves both an analysis using graphs with four regions with a confidence interval (CI) of 95% and an analysis using a graph with three regions with only the predicted values; the latter type of graph represents an innovation made in this study. The results show that the measurements of the tread hollowing and flange slope are on an acceptable level, while those for the flange height and flange thickness have to be rejected as unacceptable. The action proposed to increase the quality of data on the flange height and flange thickness is to enhance the calibration of the WPMS. In conclusion, GR&R is a useful tool to evaluate the measurement capability of WPMS and to provide helpful support for maintenance decision making.

  • 2.
    Benckert, Lars
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Wood drying studies using white light speckle photography1992In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 10, no 1, p. 24-30Article in journal (Refereed)
    Abstract [en]

    White light speckle photography is a powerful tool for measuring displacement fields in the sub-millimetre range. Here it has been utilised to study deformation and crack development in a block of wood during drying. The use of a series of single exposures made it possible to monitor the changes of the wood's surface with time. By combining two of the negatives the displacements over a given time interval were obtained for, at least in principle, all points on the surface.

  • 3.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, Ann Arbor, Michigan, USA.
    Matin, S. S.
    Islamic Azad University, Tehran, Iran.
    Makaremi, S.
    McMaster University, ON, Canada.
    Modeling of Free Swelling Index Based on Variable Importance Measurements of Parent Coal Properties by Random Forest Method2016In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 94, p. 416-422Article in journal (Refereed)
    Abstract [en]

    Coke quality has a critical role in the steelmaking industry. The aim of this study is to examine the complex relationships between various conventional coal analyses using coke making index “free swelling index (FSI)”. Random forest (RF) associated with variable importance measurements (VIMs), which is a new powerful statistical data mining approach, is utilized in this study to analyze a high-dimensional database (3961 samples) to rank variables, and to develop an accurate FSI predictive model based on the most important variables. VIMs was performed on various types of analyses which indicated that volatile matter, carbon, moisture (coal rank parameters) and organic sulfur are the most effective coal properties for the prediction of FSI. These variables have been used as an input set of RF model for the FSI modeling and prediction. Results of FSI model indicated that RF can provide a satisfactory prediction of FSI with the correlation of determination R2 = 0.96 and mean square error of 0.16 from laboratory FSIs (which is smaller than the interval unit of FSI; 0.5). Based on this result, RF can be used to rank and select effective variables by evaluating nonlinear relationships among parameters. Moreover, it can be further employed as a non-parametric reliable predictive method for modeling, controlling, and optimizing complex variables; which to our knowledge has never been utilized in the fuel and energy sectors.

  • 4.
    Chelgani, Saeed Chehreh
    et al.
    University of Michigan, USA.
    Shahbazi, B.
    Tarbiat Modares University, Tehran, Iran.
    Hadavandi, E.
    Birjand University of Technology, Birjand, Iran.
    Support vector regression modeling of coal flotation based on variable importance measurements by mutual information method2018In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 114, p. 102-108Article in journal (Refereed)
    Abstract [en]

    Support vector regression (SVR) modeling was used to predict the coal flotation responses (recovery (R∗) and flotation rate constant (k)) as a function of measured particle properties and hydrodynamic flotation variables. Coal flotation is a complicated multifaceted separation process and many measurable and unmeasurable variables can be considered for its modeling. Therefore, feature selection can be used to save time and cost of measuring irrelevant parameters. Mutual information (MI) as a powerful variable selection tool was used through laboratory measured variables to assess interactions and choose the most effective ones for predictions of R∗ and k. Feature selection by MI through variables indicated that the best arrangements for the R∗ and k predictions are the sets of particle Reynolds number-energy dissipation and particle size-bubble Reynolds number, respectively. Correlation of determination (R2) and difference between laboratory measured and SVR predicted values based on MI selected variables indicated that the SVR can model R∗ and k quite accurately with R2 = 0.93 and R2 = 0.72, respectively. These results demonstrated that the MI-SVR combination can quite satisfactorily measure the importance of variables, increase interpretability, reduce the risk of overfitting, decrease complexity and generate predictive models for high dimension of variables based on selected features for complicated processing systems.

  • 5.
    Chi, Zhexiang
    et al.
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Chen, Ruoran
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Huang, Simin
    Department of Industrial Engineering, Tsinghua University, Beijing, China.
    Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train2020In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 149, article id 107022Article in journal (Refereed)
    Abstract [en]

    Environmental factors, like seasonality, have been proved to exert significant impact on reliability of China high-speed rail train wheels in this article. Most studies on polygonization of train wheels are based on physical models, mathematical models or simulation systems. Normally, characteristics and mechanisms of wheel polygonization are studied under ideal conditions without considering the impact of the environment. However, in practical use, there are many irregular wear wheels and irregular wear cannot be explained by theoretical models with assumptions of ideal conditions. We look at two possible factors in polygonization: seasonality and proximity to engines. Our analysis of field data shows the environmental factor has more impact on wheel polygonization than the mechanical factor. Based on the Bayesian models, the mean time to failure of the wheels under different operation conditions is conducted. A case study of China’s HSR train wheels is conducted to confirm the finding. The case study shows the degree of polygonal wear is much more severe in summer than other seasons. The finding may give a totally new research perspective on polygonization of train wheels. We use Bayesian analysis because this method is useful for small and incomplete data sets. We propose three Bayesian data-driven models.

  • 6.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence.
    Context awareness for maintenance decision making: A diagnosis and prognosis approach2015In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 67, p. 137-150Article in journal (Refereed)
    Abstract [en]

    All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is important to evaluate the consistency and reliability of the measurement data obtained during laboratory testing and the prognostic/diagnostic monitoring of the system under examination.This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with a sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.

  • 7.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Jimenez Cortadi, Alberto
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Tecnalia Research and Innovation, Industry and Transport Division, Miñano .
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach2018In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, p. 80-93Article in journal (Refereed)
    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.

  • 8.
    Hadavandi, E.
    et al.
    Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran.
    Chelgani, Saeed Chehreh
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
    Estimation of coking indexes based on parental coal properties by variable importance measurement and boosted-support vector regression method2019In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 135, p. 306-311Article in journal (Refereed)
    Abstract [en]

    Coke as a fuel has a critical role for steel making industries. Since coke is a product of blended coals, it is essential to study relationships between parental coal components with quality of their coke products. Free swelling index (FSI) and maximum fluidity (MF) are standard coking indexes that widely used for blending coals and measuring quality of products. This study has been explored interdependencies between measured coal components by mutual information (MI) method and evaluated their importance in the prediction of coking indexes for a wide range of Illinois coal samples. MI results indicated that the set of moisture-organic sulfur and moisture-nitrogen-sulfate sulfur were the best variables for predictions of log(MF) and FSI, respectively. Adaptive Boosting method based on support vector regression (SVR), called Boosted-SVR, was used the selected variable sets for predictions of coking indexes. In testing stage of models, correlation of determination (R2) between actual and predicted values for the log(MF) and FSI were 0.89 and 0.90, respectively. These results indicated that Boosted-SVR model could quite satisfactory predict coking indexes. In general, outcomes of this investigation demonstrated an appropriate potential of coking quality prediction with limited numbers of input variables and suggested that a combination of MI with Boosted-SVR model as a new powerful tool which can be used for the computation of other complex fuel and processing problems based on measurement of conventional properties.

  • 9.
    Hansson, Lars
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Lundgren, Nils
    Antti, Lena
    Hagman, Olle
    Microwave penetration in wood using imaging sensor2005In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 38, no 1, p. 15-20Article in journal (Refereed)
    Abstract [en]

    It is possible to determine properties of wood using microwave scanning techniques. The purpose of this study was to verify the measured values from a microwave imaging sensor. Attenuation and phase shift of an electromagnetic wave transmitted through birch wood were measured and compared with theoretical calculated values. A test piece with varying thickness was measured with a scanner based on a microwave sensor (Satimo 9.375GHz) at different temperatures and moisture contents. The density distribution of the test piece was determined by computer tomography scanning. The result showed good correspondence between measured and theoretical values. The proportion of noise was higher at low moisture content due to lower attenuation. There is more noise in attenuation measurement than in measurement of phase shift. A reason for this could be that wood is an inhomogeneous material in which reflections and scattering affect attenuation more than phase shift. The microwave scanner has to be calibrated to a known dielectric to quantify the error in the measurement

  • 10. Holm, Martin
    et al.
    Stang, Jacob
    Delsing, Jerker
    Simulation of flow meter calibration factors for various installation effects1995In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 15, no 4, p. 235-244Article in journal (Refereed)
    Abstract [en]

    The determination of flow meter calibration factors has been made using a computer simulation approach. The proposed technique is based on computational fluid dynamics (CFD). The CFD tools were used to determine the flow field in a flow meter as developed by three different pipe configurations. These flow fields were used to determine the calibration factor for an ultrasonic flow meter. The results have been compared with calibration factors obtained by CFD using detailed LDV input boundary data, analytical calculations and experimental data. Tests were made for reference conditions of 100D straight-pipe and for single- and double-elbow pipe configurations using Reynolds numbers from 100 to 100,000. For reference conditions good agreement is shown. For disturbed flow conditions the simulations well resembled the experimental data. However we find differences for transitional and swirl flows.

  • 11.
    Jafari, M.
    et al.
    University of Tehran, Tehran, Iran.
    Chelgani, Saeed Chehreh
    University of Michigan, Ann Arbor, USA.
    Pourghahramani, P.
    Sahand University of Technology, Tabriz, Iran.
    Ebadi, H.
    Sahand University of Technology, Tabriz, Iran.
    Measurement of collector concentrations to make an efficient mixture for flotation of a low grade apatite2018In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 121, p. 19-25Article in journal (Refereed)
    Abstract [en]

    It was well documented that a mixture of collectors could have synergistic advantages over the use of an individual collector for apatite flotation. Therefore, it would be an essential procedure to determine an optimum amount of each collector for development of an efficient mixture (collector). In this study, a mixture design (MD) model was used to find an optimum amount of different typical apatite anionic collectors (Atrac, Alke and Dirol) and make an efficient mixture for the direct flotation of a low grade apatite ore. Assessment of responses for apatite flotation tests which their collectors were designed by MD showed that Dirol has the highest selectivity whereas Alke has the highest collectivity for the direct flotation of apatite. According to the experiments, the MD model computed that a mixture collector with Dirol: 364 (g/t), Alke: 295.2 (g/t) and Atrac: 140.8 (g/t) concentrations can provide the most efficient responses through the apatite flotation. Results based on the purposed concentrations for the mixed collector demonstrated that higher apatite flotation responses (grade: 14%, recovery: 76%, and S.E.: 66%) in comparison with the performance of tests with a single collector. These results can be used to design flotation conditions for the apatite flotation-separation in the industrial scale and assessment of collector concentrations for other investigations.

  • 12.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Salgado, Oscar
    IK4-Ikerlan Technology Research Centre, Control and Monitoring Area.
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Catelani, Marcantonio
    University of Florence, Department of Information Engineering.
    Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data2017In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, p. 152-162Article in journal (Refereed)
    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.

  • 13.
    Saari, Juhamatti
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Luleå University of Technology, SKF-LTU University Technology Centre.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements. Luleå University of Technology, SKF-LTU University Technology Centre.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thomson, A.
    SKF (U.K), Livingston, Scotland, United Kingdom.
    Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)2019In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 137, p. 287-301Article in journal (Refereed)
    Abstract [en]

    The maintenance cost of wind turbines needs to be minimized in order to keep their competitiveness and, therefore, effective maintenance strategies are important. The remote location of wind farms has led to an opportunistic maintenance strategy where maintenance actions are postponed until they can be handled simultaneously, once the optimal opportunity has arrived. For this reason, early fault detection and identification are important, but should not lead to a situation where false alarms occur on a regular basis. The goal of the study presented in this paper was to detect and identify wind turbine bearing faults by using fault-specific features extracted from vibration signals. Automatic identification was achieved by training models by using these features as an input for a one-class support vector machine. Detection models with different sensitivity were trained in parallel by changing the model tuning parameters. Efforts were also made to find a procedure for selecting the model tuning parameters by first defining the criticality of the system and using it when estimating how accurate the detection model should be. Method was able to detect the fault earlier than using traditional methods without any false alarms. Optimal combination of features and model tuning parameters was not achieved, which could identify the fault location without using any additional techniques.

  • 14.
    Vikberg, Tommy
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Hansson, Lars
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Schajer, Gary S.
    University of British Columbia.
    Oja, Johan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Effects on microwave measurements and simulations when collecting data close to edges of wooden boards2012In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 45, no 3, p. 525-528Article in journal (Refereed)
    Abstract [en]

    Parameters like strength, moisture content, density and grain direction are important when sorting wood according to their individual properties. All those parameters can be correlated to microwave measurements of phase shift and attenuation. Measurements of phase shift and attenuation are, however, affected by the vicinity of a board edge. In this article a simulation of the measurement system is used to create a compensation function for the measurements taken close to edges as if those were taken where no effects of the board edge could be noticed. It is shown, by comparison with real measurements, that by doing this the deviation between the values measured close to the board edges and those measured in the middle of the board is decreased, meaning a higher accuracy can be achieved by using the compensating function.

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