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Fuqing, Yuan
Publications (10 of 25) Show all publications
Barabadi, A., Garmabaki, A., Fuqing, Y. & Lu, J. (2015). Maintainability analysis of equipment using point process models (ed.). In: (Ed.), 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015. Paper presented at IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015 (pp. 797-801). Piscataway, NJ: IEEE Communications Society, Article ID 7385757.
Open this publication in new window or tab >>Maintainability analysis of equipment using point process models
2015 (English)In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 797-801, article id 7385757Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Series
IEEE International Conference on Industrial Engineering and Engineering Management, ISSN 2157-3611
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-32465 (URN)10.1109/IEEM.2015.7385757 (DOI)2-s2.0-84962010064 (Scopus ID)6f841c6d-b943-47e5-9c25-c10faf8b2cd7 (Local ID)9781467380669 (ISBN)6f841c6d-b943-47e5-9c25-c10faf8b2cd7 (Archive number)6f841c6d-b943-47e5-9c25-c10faf8b2cd7 (OAI)
Conference
IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015
Note

Validerad; 2016; Nivå 1; 20151130 (amigar)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Fuqing, Y., Barabadi, A., Lu, J. & Garmabaki, A. (2015). Performance Evaluation for Maximum Likelihood and Moment Parameter Estimation Methods on Classical Two Weibull Distribution (ed.). In: (Ed.), 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015. Paper presented at IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015 (pp. 802-806). Piscataway, NJ: IEEE Communications Society, Article ID 7385758.
Open this publication in new window or tab >>Performance Evaluation for Maximum Likelihood and Moment Parameter Estimation Methods on Classical Two Weibull Distribution
2015 (English)In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 802-806, article id 7385758Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Series
IEEE International Conference on Industrial Engineering and Engineering Management, ISSN 2157-3611
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-32263 (URN)10.1109/IEEM.2015.7385758 (DOI)2-s2.0-84962028818 (Scopus ID)6b231e41-8bee-4fb5-b6c3-e9d5493c5fa7 (Local ID)9781467380669 (ISBN)6b231e41-8bee-4fb5-b6c3-e9d5493c5fa7 (Archive number)6b231e41-8bee-4fb5-b6c3-e9d5493c5fa7 (OAI)
Conference
IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015
Note

Validerad; 2016; Nivå 1; 20151130 (amigar)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Garmabaki, A., Barabadi, A., Fuqing, Y., Lu, J. & Ayele, Y. (2015). Reliability modeling of successive release of software using NHPP (ed.). In: (Ed.), (Ed.), 2015 IEEE International Conference Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015. Paper presented at IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015 (pp. 761-766). Piscataway, NJ: IEEE Communications Society, Article ID 7385750.
Open this publication in new window or tab >>Reliability modeling of successive release of software using NHPP
Show others...
2015 (English)In: 2015 IEEE International Conference Industrial Engineering and Engineering Management (IEEM): Singapore, 6-9 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 761-766, article id 7385750Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Series
IEEE International Conference on Industrial Engineering and Engineering Management, ISSN 2157-3611
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-28701 (URN)298c4bf5-876f-4cac-877f-75e7575e6cbe (Local ID)9781467380669 (ISBN)298c4bf5-876f-4cac-877f-75e7575e6cbe (Archive number)298c4bf5-876f-4cac-877f-75e7575e6cbe (OAI)
Conference
IEEE International Conference on Industrial Engineering and Engineering Management : 06/12/2015 - 09/12/2015
Note
Validerad; 2016; Nivå 1; 20151130 (amigar)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-07Bibliographically approved
Fuqing, Y. & Kumar, U. (2014). Statistical index development from time domain for rolling element bearings (ed.). Paper presented at . International Journal of Pedagogy, Innovation and New Technologies, 10(3), 313-324
Open this publication in new window or tab >>Statistical index development from time domain for rolling element bearings
2014 (English)In: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 10, no 3, p. 313-324Article in journal (Refereed) Published
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

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-15660 (URN)f3348603-dda5-4e71-9eb3-bb6a07eb25b4 (Local ID)f3348603-dda5-4e71-9eb3-bb6a07eb25b4 (Archive number)f3348603-dda5-4e71-9eb3-bb6a07eb25b4 (OAI)
Note
Validerad; 2014; 20140509 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Fuqing, Y., Kumar, U. & Galar, D. (2013). A comparative study of artificial neural networks and support vector machine for fault diagnosis (ed.). Paper presented at . International Journal of Pedagogy, Innovation and New Technologies, 9(1), 49-60
Open this publication in new window or tab >>A comparative study of artificial neural networks and support vector machine for fault diagnosis
2013 (English)In: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, no 1, p. 49-60Article in journal (Refereed) Published
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

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-4763 (URN)2c0f1f89-3ade-4c4a-a92f-ceffaf48d367 (Local ID)2c0f1f89-3ade-4c4a-a92f-ceffaf48d367 (Archive number)2c0f1f89-3ade-4c4a-a92f-ceffaf48d367 (OAI)
Note
Validerad; 2013; 20121218 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Fuqing, Y., Kumar, U. & Galar, D. (2013). An adaptive multiple kernel method-based support vector machine used for classication (ed.). Paper presented at . International Journal of Condition Monitoring, 3(1), 8-15
Open this publication in new window or tab >>An adaptive multiple kernel method-based support vector machine used for classication
2013 (English)In: International Journal of Condition Monitoring, ISSN 0019-6398, E-ISSN 2047-6426, Vol. 3, no 1, p. 8-15Article in journal (Refereed) Published
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

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-9182 (URN)10.1784/204764213806173367 (DOI)7bedcc20-3421-4f03-9d51-15a7b15b06f6 (Local ID)7bedcc20-3421-4f03-9d51-15a7b15b06f6 (Archive number)7bedcc20-3421-4f03-9d51-15a7b15b06f6 (OAI)
Note
Validerad; 2013; 20131202 (yuafuq)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Fuqing, Y. & Kumar, U. (2013). Anomaly detection using support vector machines on overhead contact wire (ed.). Paper presented at International Intelligent Manufacturing Conference : 26/06/2013 - 27/06/2013. Paper presented at International Intelligent Manufacturing Conference : 26/06/2013 - 27/06/2013.
Open this publication in new window or tab >>Anomaly detection using support vector machines on overhead contact wire
2013 (English)Conference paper, Oral presentation only (Other academic)
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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-34187 (URN)84fa7923-d37f-4f52-9339-8777505f9fab (Local ID)84fa7923-d37f-4f52-9339-8777505f9fab (Archive number)84fa7923-d37f-4f52-9339-8777505f9fab (OAI)
Conference
International Intelligent Manufacturing Conference : 26/06/2013 - 27/06/2013
Note
Godkänd; 2013; 20130807 (yuafuq)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Fuqing, Y. & Kumar, U. (2013). Proportional Intensity Model considering imperfect repair for repairable systems (ed.). Paper presented at . International Journal of Pedagogy, Innovation and New Technologies, 9(2), 163-174
Open this publication in new window or tab >>Proportional Intensity Model considering imperfect repair for repairable systems
2013 (English)In: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, no 2, p. 163-174Article in journal (Refereed) Published
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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-6263 (URN)476d651c-1567-4129-b7f4-712975d91697 (Local ID)476d651c-1567-4129-b7f4-712975d91697 (Archive number)476d651c-1567-4129-b7f4-712975d91697 (OAI)
Note
Validerad; 2013; 20130815 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Fuqing, Y. & Kumar, U. (2012). A general imperfect repair model considering time-dependent repair effectiveness (ed.). Paper presented at . IEEE Transactions on Reliability, 61(1), 95-100
Open this publication in new window or tab >>A general imperfect repair model considering time-dependent repair effectiveness
2012 (English)In: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 61, no 1, p. 95-100Article in journal (Refereed) Published
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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-11042 (URN)10.1109/TR.2011.2182222 (DOI)000301198500011 ()2-s2.0-84858161113 (Scopus ID)9f15350f-5866-46b9-8ed3-7fb911aa2fa1 (Local ID)9f15350f-5866-46b9-8ed3-7fb911aa2fa1 (Archive number)9f15350f-5866-46b9-8ed3-7fb911aa2fa1 (OAI)
Note
Validerad; 2012; 20120322 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Fuqing, Y., Kumar, U. & Galar, D. (2012). Failure diagnosis of railway assets using support vector machine and ant colony optimization method (ed.). Paper presented at . International Journal of COMADEM, 15(2), 3-10
Open this publication in new window or tab >>Failure diagnosis of railway assets using support vector machine and ant colony optimization method
2012 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, no 2, p. 3-10Article in journal (Refereed) Published
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.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-7291 (URN)5a2abf60-c7c9-47f1-bf9e-b2b15fb95d45 (Local ID)5a2abf60-c7c9-47f1-bf9e-b2b15fb95d45 (Archive number)5a2abf60-c7c9-47f1-bf9e-b2b15fb95d45 (OAI)
Note
Validerad; 2012; 20120813 (ysko)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
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