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  • 1.
    Barabadi, Abbas
    et al.
    University of Tromsø - The Arctic University of Norway.
    Garmabaki, Amir
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Islamic Azad University.
    Fuqing, Yuan
    Tromsø University.
    Lu, J.
    University of Tromsø - The Arctic University of Norway.
    Maintainability analysis of equipment using point process models2015Inngår i: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, s. 797-801, artikkel-id 7385757Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The maintenance cost can be reduced significantly by applying the maintainability principle in the design and operation phase. An effective maintainability prediction can help the designer to improve performance and safety of equipment. The analysis of historical repair by an affective statistical approach provides essential information for decision-making regarding the planning of operation and maintenance activities of the plant. However, the literature on field repair data is quite scarce and they are not detailed. This paper will try to provide step by step guideline for field repair data using point process models by a case study.

  • 2.
    Block, J.
    et al.
    Logistic Analysis and Fleet Monitoring Division, Saab Aerotech.
    Tyrberg, T.
    Maintenance Information, Systems Division, Saab Aerotech.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optimal repair for repairable components during phaseout an aircraft fleet2010Inngår i: IEEE Aerospace Conference Proceedings, Piscataway, NJ, 2010, artikkel-id 5446880Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The successive removal of units from a group of systems during phasing out leads to a variable requirement for stock level of spare parts. Repairable units from phased out systems can be reused for the remaining functional systems in the group. Hence, the stock level of spare parts increases and the demand for spare parts decreases. This can lead to excessive stocking of spare parts and high maintenance costs for spare parts. This paper proposes a methodology to determine the optimum time to stop repair of repairable units to minimize the maintenance cost. It uses a Poisson Distribution based on NHPP (Non-Homogenous Poisson Process) to predict the available number of units, while fulfilling the demand for spare parts for the remaining systems. A concept, called Minimal Margin is introduced to formulate the problem and nonlinear programming is proposed to obtain the optimum solution. Finally, a numerical example is presented to demonstrate the approach

  • 3. Dandotiya, Rajiv
    et al.
    Fuqing, Yuan
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Optimal maintenance decision for line reparable units (LRU) for an aircraft system: a conceptual approach2008Inngår i: Quarterly Journal of the Operational Research Society of India (OPSEARCH), ISSN 0030-3887, E-ISSN 0975-0320, Vol. 45, nr 3, s. 291-302Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Maintenance decisions concerning repair of the Line Replaceable Unit (LRU) of an aircraft fleet need to be considered carefully while deciding the phasing out of the fleet. This is important for achieving higher degree of cost effectiveness and fleet availability at desired level. Discard rate and phasing out period for an aircraft fleet are the critical parameters for determining optimum time to stop the maintenance of LRU. The economic value of remaining useful life of an aircraft fleet should be taken into consideration by salvaging the LRU at the end of the phasing out. The paper suggests a methodology to arrive the time that will minimize the total life cycle cost and provide us economic basis to withdraw the maintenance resources. A mathematical model has been developed for the discard rate of aircrafts based on failure rate, mission life and remaining useful life of the aircrafts in the fleet. This will assist in fulfilling the managing demand of LRU while phasing out of the aircraft fleet.

  • 4.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Failure diagnostics using support vector machine2011Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Failure diagnostics is an important part of condition monitoring aiming to identify incipient failures in early stages. Accurate and efficient failure diagnostics can guarantee that the operator makes the correct maintenance decision, thereby reducing the maintenance costs and improving system availability. The Support Vector Machine (SVM) is discussed in this thesis with the purpose of efficiently diagnosing failure. The SVM utilizes the kernel method to transform input data from a lower dimensional space to a higher dimensional space. In the higher dimensional space, the hitherto linearly non separable patterns can be linearly separated, without compromising the computational cost. This facilitates failure diagnostics as in the higher dimensional space, the existing failure or incipient failure is more identifiable. The SVM uses the maximal margin method to overcome the “overfitting” problem. This problem makes the model fit special data sets. The maximal margin method also makes it suitable for solving small sample size problems. In this thesis, the SVM is compared with another well known technique, the Artificial Neural Network (ANN). In the comparative study, the SVM performs better than the ANN. However, as the performance of the SVM critically depends on the parameters of the kernel function, this thesis proposes using an Ant Colony Optimization (ACO) method to obtain the optimal parameters. The ACO optimized SVM is applied to diagnose the electric motor in a railway system. The Support Vector Regression (SVR) is an extension of the SVM. In this thesis, SVR is combined with a time-series to forecast reliability. Finally, to improve the SVM performance, the thesis proposes a multiple kernel SVM. The SVM is an excellent pattern recognition technique. However, to obtain an accurate diagnostics performance, one has to extract the appropriate features. This thesis discusses the features extracted from the time domain and uses the SVM to diagnose failure for a bearing. Another case in this thesis is presented, namely failure diagnostics for an electric motor installed in a railway’s crossing and switching system; in this case, the features are extracted from the power consumption signal. In short, the thesis discuses the use of the SVM in failure diagnostics. Theoretically, the SVM is an excellent classifier or regressor possessing a solid theoretical foundation. Practically, the SVM performs well in failure diagnostics, as shown in the cases presented. Finally, as failure diagnostics critically relies on feature extraction, this thesis considers feature extraction from the time domain.

  • 5.
    Fuqing, Yuan
    et al.
    Tromsø University.
    Barabadi, Abbas
    Tromsø University.
    Lu, J.M.
    Department of Engineering and Safety, UiT The Arctic University of Norway, Tromsø.
    Garmabaki, Amir
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Islamic Azad University.
    Performance Evaluation for Maximum Likelihood and Moment Parameter Estimation Methods on Classical Two Weibull Distribution2015Inngår i: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, s. 802-806, artikkel-id 7385758Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The two-parameter Weibull distribution is the most widely used distribution in reliability engineering. Parameter estimation is a key issue to apply the Weibull to practical engineering. This paper aims to compare the performance of the maximum parameter estimation method (MLE) and the moment parameter method. It firstly investigates some mathematical properties such as solution uniqueness, estimator's equivariance and the confidence interval, that are important to practice. Later on their performances have been evaluated by using simulation. In the simulation, data sets ranged from extreme small to large have been considered. Weibull distribution with increasing, constant and deceasing failure rate have been chosen in the simulation to ensure the simulation's results concrete.

  • 6.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    A cost model for repairable system considering multi-failure type over finite time horizon2011Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 7, nr 2, s. 186-194Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In general, downtime of a system can be attributed due to multiple failure categories and repair costs for each failure categories can be different. Many of these failure types are repaired to a state which can be called as bad as old and such repair actions are termed as “minimal repair”. Many system or components are replaced after a certain number of such minimal repair actions. In this study, we intend to prove that if the system failure process can be described by NHPP (Non Homogenous Poisson Process), then each failure category can also be modelled by NHPP. Based on this, a cost model is developed by using the decomposition of the NHPP and renewal theory. Using the cost model, a model is developed to obtain the optimal number of minimum repair action every failure category. Since it is not possible to find any analytical solution, solution to the renewal function, an approximate approach is introduced to obtain numerical solution. Finally, a numerical example is presented to demonstrate the method.

  • 7.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A general imperfect repair model considering time-dependent repair effectiveness2012Inngår i: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 61, nr 1, s. 95-100Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Kijima I and Kijima II models are two important imperfect repair models in literature. These two models use one constant parameter to represent the degree of repair, which is called Repair Effectiveness (RE) in this paper. We developed a more general imperfect repair model by extending the constant RE to a time-dependent function based on the virtual age process, where the Kijima models are special cases of the new model. A simulation method is developed to estimate the cumulative number of failures for the new model, and a Bayesian inference method is proposed to select the best imperfect repair model. Finally, to demonstrate the new model, a numerical example is provided. From this example, the new model shows a more accurate mean and a narrower confidence interval than that of the Non-Homogeneous Poisson Processes, and Kijima I and Kijima II models.

  • 8.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Anomaly detection using support vector machines on overhead contact wire2013Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    This paper describes an anomaly detection method on the Overhead Contact Wire (OCW) in electrified railway system. The fundamental basic of contact wire is described. Their mechanical property and thermal property are discussed. The principle of the current collection through the overhead wire is described in brief. Some classical mechanic dynamic models between the pantograph and overhead contact wire are presented. Concentrating on the anomaly detection using vertical acceleration signal, this paper proposes a support vector regression based method to detect the anomaly detection on the surface of the overhead contact wire. The Support Vector Regression (SVR) is used to model the dependency between vertical acceleration and the other factor such as uplift, train speed, height of the wire. Correlation is used to find the significant factors which influence the vertical acceleration. The SVR model is used to de-trend the vertical acceleration signal. The statistical model is proposed to find the anomaly points on the contact wire.

  • 9.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kernelized proportional intensity model for repairable systems considering piecewise operating conditions2012Inngår i: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 61, nr 3, s. 618-624Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The proportional intensity model (PIM) has been used to model the intensity function of repairable systems taking non-time factors, such as operating conditions, and repair history, into consideration. This paper develops a kernelized PIM (KPIM) by combining the PIM and the kernel method to consider a scenario where a repairable system experiences piecewise operating conditions. The kernel method is used to approximate the PIM covariate function nonlinearly. An approach based on the regularized likelihood function is proposed to obtain the optimal parameters for the KPIM. A numerical example is provided to demonstrate the KPIM model, and the parameter estimation approach

  • 10. Fuqing, Yuan
    et al.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Predicting time to failure using support vector regression2010Inngår i: Proceedings of the 1st international workshop and congress on eMaintenance, Luleå tekniska universitet, 2010, s. 223-226Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Support Vector Machine (SVM) is a new but prospective technique which has been used in pattern recognition, data mining, etc. Taking the advantage of Kernel function, maximum margin and Lanrangian optimization method, SVM has high application potential in reliability data analysis. This paper introduces the principle and some concepts of SVM. One extension of regular SVM named Support Vector Regression (SVR) is discussed. SVR is dedicated to solve continuous problem. This paper uses SVR to predict reliability for repairable system. Taking an equipment from Swedish railway industry as a case, it is shown that the SVR can predict (Time to Failure) TTF accurately and its prediction performance can outperform Artificial Neural Network (ANN).

  • 11.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Proportional Intensity Model considering imperfect repair for repairable systems2013Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, nr 2, s. 163-174Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Proportional Intensity Model (PIM) extends the classical Proportional Hazard Model (PHM) in order to deal with repairable systems. This paper develops a more general PIM model which uses the imperfect model as baseline function. By using the imperfect model, the effectiveness of repair has been taken into account, without assuming an "as-bad-as-old" or an "as-good-as-new" scheme. Moreover, the effectiveness of other factors, such as the environmental conditions and the repair history, is considered as covariant in this PIM. In order to solve the large number parameters estimation problem, a Bayesian inference method is proposed. The Markov Chain Monte Carlo (MCMC) method is used to compute the posterior distribution for the Bayesian method. The Bayesian Information Criterion (BIC) is employed to perform model selection, namely, selecting the baseline function and remove the nuisance factors in this paper. In the final, a numerical example is provided to demonstrate the proposed model and method.

  • 12. Fuqing, Yuan
    et al.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Replacement policy for repairable system under various failure types with finite time horizon2009Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The repairable system suffers various type of failure and each failure type has different repair cost. Assume the failure process of the system as Non-homogenous Poisson Process (NHPP). The system is replaced after it experienced a predetermined number of minimal repairs. Considering finite time horizon, the paper proposes a replacement model for the system. It firstly proves that the failure process of each type of failure also follows NHPP. Then it develops a model to estimate the total cost which covers minimal repair cost for each type of failure and system replacement cost. To obtain the numerical solution, the paper introduces a numerical approach to approximate renewal function and a nonlinear programming model is developed. A numerical example is presented eventually.

  • 13. Fuqing, Yuan
    et al.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Statistical index development from time domain for rolling element bearings2014Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 10, nr 3, s. 313-324Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Feature extraction is crucial to efficiently diagnose fault. This paper discusses a number of time-domain statistical features, including Kurtosis or the Crest Factor, the Mean by Deviation Ratio (MDR), and Symbolized Sequence Shannon Entropy (SSSE). The SSSE reflects the spatial distribution of the signal which is complementary with the statistical features. A new feature, Normalized Normal Negative Likelihood (NNNL), is used to improve the Normal Negative Likelihood (NNL). A Separation Index (SI) called the Extended SI (ESI) evaluates the performance of each feature and to remove noise feature. The Multi-Class Support Vector Machine (MSVM) recognizes bearing defect patterns. A numerical case is presented to demonstrate these features, their feature subset selection method and the pattern recognition method. The MSVM is used to detect three different types of bearing defects: defects in the inner race, outer race and bearing ball

  • 14.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A comparative study of artificial neural networks and support vector machine for fault diagnosis2013Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, nr 1, s. 49-60Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fault detection is a crucial step in condition based maintenance requiring. The importance of fault diagnosis necessitates an efficient and effective failure pattern identification method. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) emerging as prospective pattern recognition techniques in fault diagnosis have been showing its adaptability, flexibility and efficiency. Regardless of variants of the two techniques, this paper discusses the principle of the two techniques, and discusses their theoretical similarity and difference. Eventually using the commonest ANN, SVM, a case study is presented for fault diagnosis using a wide used bearing data. Their performances are compared in terms of accuracy, computational cost and stability

  • 15.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    An adaptive multiple kernel method-based support vector machine used for classication2013Inngår i: International Journal of Condition Monitoring, ISSN 0019-6398, E-ISSN 2047-6426, Vol. 3, nr 1, s. 8-15Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Classification is an important technique used for condition monitoring. Extensive research has been carried out on classification and numerous techniques have been developed. The support vector machine (SVM) is one of these techniques; it has excellent classification capacity and is widely used. The effectiveness of the SVM depends on the selection of the kernel function, so to maximise performance this paper proposes using an adaptive multiple kernel SVM (AMK-SVM). Using AMK, many heterogeneous features, such as continuous, categorical, logical etc, can be merged. Instead of predefining the parameters of kernel functions as with other multiple kernel SVMs, this method can adapt its parameters to data automatically through kernel alignment. The paper offers two numerical examples: one with benchmarking data to test the feasibility and performance of the approach (for this case a two-layer neural network and two single kernel SVMs are applied to the same datasets to compare their performance with the AMK-SVM); the other example uses the AMK-SVM to discriminate a healthy bearing from a defective bearing

  • 16.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Failure detection using support vector machine and artificial neural networks: a comparative study2011Inngår i: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos.: Coxmoor Publishing Co. , 2011, Vol. 1, s. 189-201Konferansepaper (Fagfellevurdert)
  • 17.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Failure diagnosis of railway assets using support vector machine and ant colony optimization method2012Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, nr 2, s. 3-10Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Support Vector Machine (SVM) is an excellent technique for pattern recognition. This paper uses a multi-class SVM as a classifier to solve a multi-class classification problem for fault diagnosis. As the pre-defined parameters in the SVM influence the performance of the classification, this paper uses the heuristic Ant Colony Optimization (ACO) algorithm to find the optimal parameters. This multi-class SVM and ACO are applied to the fault diagnosis of an electric motor used in a railway system. A case study illustrates how efficient the ACO is in finding the optimal parameters. By using the optimal parameters from the ACO, the accuracy of the performed diagnosis on the electric motor is found to be highest.

  • 18.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Reliability prediction using support vector regression2010Inngår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 1, nr 3, s. 263-268Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Reliability prediction of machinery is crucial to schedule overhauls, spare parts replacement and maintenance interventions. Many AI tools have been used in order to provide these predictions for the industry. Support vector regression (SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVR combining time series to predict the next failure time based on historical failure data. To solve the parameter selection problem a method has been proposed. This method approximates the widely used leave-one-out method. To bound the prediction error, a confidence interval is proposed based on the non-homogeneous poisson process. A numerical case from the mining industry is presented to demonstrate the developed approach.

  • 19.
    Fuqing, Yuan
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Krishna, B. Misra
    Complex system reliability evaluation using support vector machine for incomplete data-set2011Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 7, nr 1, s. 32-42Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Support Vector Machine (SVM) is an artificial intelligence technique that has been successfully used in data classification problems, taking advantage of its learning capacity. In systems modelled as networks, SVM has been used to classify the state of a network as failed or operating to approximate the network reliability. Due to the lack of information, or high computational complexity, the complete analytical expression of system states may be impossible to obtain, that is to say, only incomplete data-set can be obtained. Using these incomplete data-sets, depending on amount of missed data-set, this paper proposes two different approaches named rough approximation method and simulation based method to evaluate system reliability. SVM is used to make the incomplete data-set complete. Simulation technique is also employed in the so called simulation based approximation method. Several examples are presented to illustrate the approaches.

  • 20. Fuqing, Yuan
    et al.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    S, Claudio M. Rocco
    Facultad de Ingeniería, Universidad Central de Venezuela.
    Misra, Krishna B.
    RAMS Consultants.
    Complex system reliability evaluation using support vector machine2010Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Support Vector Machine (SVM) is a data mining technique that has been successfully used in classification problems, starting from a known training data set (TDS). In systems modeled as networks, SVM has been used to classify the state of a network as failed or operating and jointly combined in a Monte Carlo sampling approach to approximate the network reliability. The analytical expression of the binary function (failed/operating) produced by SVM is difficult to be understood, since it generally involves the evaluation of non-linear operators, which consider a subset of the TDS, called Support Vectors (SV) and sampled system states. In this paper a different approach is proposed to assess system reliability. Information about path and cut sets is obtained directly from SV, without considering the analytical expression of the binary function produced by SVM. From here the system reliability is approximated directly. Several examples illustrate the approach.

  • 21.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    RUL prediction using moving trajectories between SVM hyper planes2012Inngår i: 2012 proceedings: Annual Reliability and Maintainability Symposium (RAMS 2011) : Reno, Nv 23-26 Jan. 2012, Piscataway, NJ: IEEE Communications Society, 2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate and robust algorithms for data analysis. Improvements in database's technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is knowledge discovery, i.e. patterns in the data that lead to better understanding of the data generating process and to useful predictions. The knowledge that becomes available through data mining enables an asset owner to make important decisions about life cycle costs in advance. In maintenance field, CMMS (Computer maintenance management system) and CM (Condition Monitoring) are the most popular software available in the industries. Since first one stores all historical data, maintenance actions, events and ma nufacturer recommendations, second one collects and stores all critical physical parameters (vibration, temperature.) to be monitored in a regular time basis. However, converting these data into useful information is a challenge. The degradation process of a system may be affected by many unknown factors, such as unidentified fault modes, unmeasured operational conditions, engineering variance, environmental conditions, etc. These unknown factors not only complicate the degradation behaviors of the system, but also make it difficult to collect quality data. Due to lack of knowledge and incomplete measurements, certain important con text information (e.g. fault modes, operational conditions) of the collected data will be missing. Therefore, historical data of the system with a large variety of degradation patterns will be mixed together. With such data, learning a global model for Remaining Useful Life (RUL) prediction becomes extremely hard since the end user does not have enough and good-quality data to model properly the system. This has led us to look for advanced RUL prediction techniques beyond the traditional RUL prediction models. The degradation process for many engineering systems, especially mechanical systems, is irreversible unless the condition is recovered by effective maintenance actions. The irreversible degradation process does not necessarily imply that the observed features will exhibit a monotonic progression pattern during degradation. Such progression pattern is sometimes hard to model using parametric methods. Considering a degradation process involving no or limited maintenance, the process may compose of a sequence of irreversible stages (either discrete or continuous) from new to be worn out, which can be implicitly expressed by the trajectory of the measured condition data or features. Therefore, the RUL of the system can be estimated if its future degradation trend can be projected from those historical instances. In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is going to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at a different time horizon. Therefore, the final RUL of a specific component can be estimated, and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method. Proposed model develops an effective RUL prediction method that addresses multiple challenges in complex system prognostics, where many parameters are unknown. Similarities between degradation trajectories can be checked in order to enrich existing methodologies in prognostic's applications. Existing CM data for bearings will be used to verify the model.

  • 22.
    Garmabaki, Amir
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Barabadi, Abbas
    Tromsø University.
    Fuqing, Yuan
    Tromsø University.
    Lu, J.
    University of Tromsø - The Arctic University of Norway.
    Ayele, Y.Z.
    University of Tromsø - The Arctic University of Norway.
    Reliability modeling of successive release of software using NHPP2015Inngår i: 2015 IEEE International Conference Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, s. 761-766, artikkel-id 7385750Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an effective reliability model for multi-release open source software (OSS), which derived based on software lifecycle development process (SDLC) proposed by Jørgensen [1]. Most of OSS reliability models do not consider the unique characteristic of OSS in the model. This model, combine bugs removed from pre-commit test and parallel debugging test phases. Furthermore, the proposed model is based on the assumptions that the total number of fault removal of the new release depends on the reported faults from the previous release and on the faults generated due to adding some new adds-on to the existing software system. The parameters of model have been estimated using three releases of the Apache project. In addition, three models in the literature are selected to compare with the proposed model. Comparison indicates that the proposed model is a suitable reliability model that fits the data across all the releases of the Apache project.

  • 23.
    Gong, Liang
    et al.
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University.
    Liu, Chengliang
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University.
    Li, Yanming
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Training feed-forward neural networks using the gradient descent method with the optimal stepsize2012Inngår i: Journal of Computational Information Systems, ISSN 1553-9105, Vol. 8, nr 4, s. 1359-1371Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The most widely used algorithm for training multiplayer feedforward networks, Error BackPropagation (EBP), is an iterative gradient descend algorithm by nature. Variable stepsize is the key to fast convergence of BP networks. A new optimal stepsize algorithm is proposed for accelerating the training process. It modifies the objective function to reduce the computational complexity of the Jacobin and consequently that of Hessian matrices, and hereby directly computes the optimal iterative stepsize. The improved backpropagation algorithm helps alleviating the problem of slow convergence and oscillations. The analysis indicates that the backpropagation with optimal stepsize (BPOS) is more efficient when treating large-scale samples. The numerical experiment results on pattern recognition and function approximation problems show that the proposed algorithm possesses the features of fast convergence and less intensive computational complexity.

  • 24.
    Lv, J.
    et al.
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Wang, W.
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Krafft, T.
    Department of International Health, Faculty of Health, Medicine and Life Sciences, Maastricht University.
    Li, Y.
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Zhang, F.
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Effects of several environmental factors on longevity and health of the human population of Zhongxiang, Hubei, China2011Inngår i: Biological Trace Element Research, ISSN 0163-4984, E-ISSN 1559-0720, Vol. 143, nr 2, s. 702-716Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Increasing human health and longevity is of global interest. Environmental, genetic, and stochastic factors all affect longevity. Among these factors, the environment is extremely important. To investigate the relationship between the environment and longevity, we studied the environment in Zhongxiang (China), where the inhabitants commonly have long life spans. Air was analyzed for negative oxygen ions, SO2, and inhalable particles, while drinking water and rice were analyzed for macro- and micro-elements. The air quality in this area was determined to be grade I with high negative oxygen ion content and low SO2 and inhalable particle contents. Apart from Fe, Mn, and F, all tested elements and the pH were within national standards and World Health Organization guidelines. The percentage of long-lived people in the area was closely related to the macro- and micro-element contents of their staple food, rice. The elements in rice could be classified into three categories according to their effect on longevity: Sr, Ca, Al, Mo, and Se, which were positively correlated with longevity; Fe, Mn, Zn, Cr, P, Mg, and K, which had a weak effect on local longevity, and Cu and Ba, which had a negative effect on longevity.

  • 25.
    Lv, Jinmei
    et al.
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Wang, Wuyi
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Zhang, Fengying
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Krafft, Thomas
    Department of International Health, Faculty of Health, Medicine and Life Sciences, Maastricht University.
    Fuqing, Yuan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Li, Yonghua
    Chinese Institute of Geographic Sciences and Natural Resources Research, Beijing.
    Identification of human age using trace element concentrations in hair and the support vector machine method2011Inngår i: Biological Trace Element Research, ISSN 0163-4984, E-ISSN 1559-0720, Vol. 143, nr 3, s. 1441-1450Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Trace element content in hair is affected by the age of the donor. Hair samples of subjects from four counties in China where people are known to have long lifespan (“longevity counties”) were collected and the trace element content determined. Samples were subdivided into three age groups based on the age of the donors from whom these were taken: children (0–15 years); elderly (80–99 years); and centenarians (≥100 years). We compared the trace element content in hair of different age groups of subjects. Support vector machine classification results showed that a non-linear polynomial kernel function could be used to classify the three age groups of people. Age did not have a significant effect on the content of Ca and Cd in human hair. The content of Li, Mg, Mn, Zn, Cr, Cu, and Ni in human hair changed significantly with age. The magnitude of the age effect on trace element content in hair was in the order Cu > Zn > Ni > Mg > Mn > Cr > Li. Cu content in hair decreased significantly with increasing age. The hair of centenarians had higher levels of Li and Mn, and lower levels of Cr, Cu, and Ni comparing with that of the children and elderly subjects. This could be a beneficial factor of their long lifespan.

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