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Zhao, Ming
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Publications (10 of 33) Show all publications
Zhang, Y., Zhao, M., Zhang, Y., Pan, R. & Cai, J. (2020). Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers. European Journal of Operational Research, 283(2), 491-510
Open this publication in new window or tab >>Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers
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2020 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 283, no 2, p. 491-510Article in journal (Refereed) Published
Abstract [en]

Reconfigurable manufacturing systems (RMSs) are considered the solution of choice when variable production capacity and functionality are required. A combinational approach, which integrates the steady-state probabilities of repairable reconfigurable machine tools (RMTs) and inventory-state probabilities of buffers through an improved universal generating function, is introduced in this study to assess the compound performance indicators (CPIs) of a repairable RMS. This paper contributes to the existing literature by considering the availability of buffers to calculate the CPIs of an RMS. In the proposed approach, the dynamic-state probability for each RMT is determined with a homogeneous continuous-time Markov model, and steady-state probability is obtained as the limit of the dynamic probability as time tends to infinity. In addition, a descriptive input-output information flow, which combines the conveying processes of the machined parts through buffers with the Poisson process, is proposed to determine the inventory-state probabilities of the buffers. Moreover, the explicit expressions of the CPI and expected performance rate (for the RMS and its constituent RMTs) are determined, and the validation procedure and technical details of the performance analysis for the Monte Carlo simulation are presented. Finally, a non-serial, repairable, multi-state RMS with multiple buffers that produces three types of engine cylinder heads is presented to validate the proposed approach. The simulation results verify the accuracy of the performance assessment of the RMS. It is useful for performance improvement in terms of machine reliability, resource utilisation efficiency, and decision-making concerning the configuration of RMS with buffers.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Compound performance indicator, Dynamic and steady-state probability, Logistics, Reconfigurable manufacturing system, Universal generating function
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-31377 (URN)10.1016/j.ejor.2019.11.013 (DOI)000515445500006 ()2-s2.0-85076848053 (Scopus ID)
Available from: 2020-01-07 Created: 2020-01-07 Last updated: 2020-03-17Bibliographically approved
Zhao, M., Yang, J., Zhao, B. & Wu, Z. (2019). Copula-based reliability modelling of wireless sensor networks with dependent failures. International Journal of Sensor Networks (IJSNet), 31(2), 90-98
Open this publication in new window or tab >>Copula-based reliability modelling of wireless sensor networks with dependent failures
2019 (English)In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 31, no 2, p. 90-98Article in journal (Refereed) Published
Abstract [en]

Wireless sensor networks (WSNs) have widely been applied in various industries and business fields covering large geographical regions. It is therefore important to be able to model, assess and predict the reliability of WSNs since the failures can have a great effect on the monitoring or control systems that are normally depending on the WSNs. In this paper, the general WSN reliability models are developed by deleting the independent assumption of component or subsystem failures and are consequently more reasonable to characterise the failure process of WSNs. The methodology in the proposed WSN reliability models is to consider that the failure times of subsystems are dependent variables and their joint distribution is obtained by binding their marginal failure distributions together through a copula function. For specific Frank copula functions, the Star-based WSN reliability models are derived and their properties are also discussed in this paper. 

Place, publisher, year, edition, pages
InderScience Publishers, 2019
Keywords
Control system, Copula function, Dependence, Failure time, Joint distribution, Reliability model, Star-based WSN, Subsystem, Wireless sensor network, WSN
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hig:diva-30739 (URN)10.1504/IJSNET.2019.102185 (DOI)000485658600003 ()2-s2.0-85072250185 (Scopus ID)
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-11Bibliographically approved
Yongjin, Z., Youchao, S., Longbiao, L. & Ming, Z. (2018). Copula-based reliability analysis for a parallel system with a cold standby. Communications in Statistics - Theory and Methods, 47(3), 562-582
Open this publication in new window or tab >>Copula-based reliability analysis for a parallel system with a cold standby
2018 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, no 3, p. 562-582Article in journal (Refereed) Published
Abstract [en]

The traditional reliability models cannot well reflect the effect of performance dependence of subsystems on the reliability of system, and neglect the problems of initial reliability and standby redundancy. In this paper, the reliability of a parallel system with active multicomponents and a single cold-standby unit has been investigated. The simultaneously working components are dependent and the dependence is expressed by a copula function. Based on the theories of conditional probability, the explicit expressions for the reliability and the MTTF of the system, in terms of the copula function and marginal lifetime distributions, are obtained. Let the copula function be the FGM copula and the marginal lifetime distribution be exponential distribution, a system with two parallel dependent units and a single cold-standby unit is taken as an example. The effect of different degrees of dependence among components on system reliability is analyzed, and the system reliability can be expressed as the linear combination of exponential reliability functions with different failure rates. For investigating how the degree of dependence affects the mean lifetime, furthermore, the parallel system with a single cold standby, comprising different number of active components, is also presented. The effectiveness of the modeling method is verified, and the method presented provides a theoretical basis for reliability design of engineering systems and physics of failure.

Place, publisher, year, edition, pages
Taylor and Francis Inc., 2018
Keywords
Copula-based reliability; Dependent component; Initial failure; Mean time to failure; Cold standby
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-25588 (URN)10.1080/03610926.2017.1309432 (DOI)000423624000005 ()2-s2.0-85031398267 (Scopus ID)
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-03-13Bibliographically approved
Zhao, M., Zhang, Y. J. & Yang, J. F. (2018). Masked data analysis for storage reliability model with initial failures. In: Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem (Ed.), Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018: . Paper presented at ESREL 2018, June 17-21, 2018, Trondheim, Norway (pp. 2565-2572). CRC Press/Balkema
Open this publication in new window or tab >>Masked data analysis for storage reliability model with initial failures
2018 (English)In: Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018 / [ed] Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, CRC Press/Balkema , 2018, p. 2565-2572Conference paper, Published paper (Refereed)
Abstract [en]

Storage reliability is of importance for the products that largely stay in storage in their total life-cycle such as warning systems for harmful radiation detection, rescue systems, many kinds of defense systems, etc. The storage reliability of a product is commonly defined as the probability that the product can perform its specific function for a period of specific storage time under specific storage environment. Logically, the failures of the product in storage should be identified with the same criteria as in its operation process. However, the failure data in storage may be observed indirectly through the maintenance or inspection activities. Nevertheless, when the storage reliability is concerned in general, the reliability model should take into consideration the possibility that the operational reliability does not start at 100%, for example, the one-shot product may have only 96% operational reliability when they are newly produced. In this paper, the storage reliability model with possibly initial failures, which are usually neglected at the beginning of storage in most of storage models, is studied on the statistical analysis method when the masked data are observed. The parametric estimation procedure, based on the Least Squares method, is developed generally by applying an EM-like (Expectation and Maximization) algorithm for the storage data in which some information about which components have caused the system failures is not known, namely the failure data are masked. The estimates of the model parameters including the initial reliability are formalized. In the case of exponentially distributed storage lifetime and series system, a numerical example is provided to illustrate the method and procedure though the method is not limited to such case. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, maintenance strategy optimization and identifying the production quality.

Place, publisher, year, edition, pages
CRC Press/Balkema, 2018
Keywords
Decision making, Failure (mechanical), Least squares approximations, Life cycle, Numerical methods, Parameter estimation, Reliability analysis, Systems engineering, Distributed storage, Inspection activities, Least squares methods, Maintenance strategies, Operational reliability, Parametric estimation, Radiation detection, Statistical analysis methods, Digital storage
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:hig:diva-29065 (URN)2-s2.0-85058104878 (Scopus ID)978-0-8153-8682-7 (ISBN)
Conference
ESREL 2018, June 17-21, 2018, Trondheim, Norway
Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-01-07Bibliographically approved
Zhang, Y., Sun, Y. & Zhao, M. (2017). A combinatorial estimation approach for storage reliability with initial failures based on periodic testing data. Communications in statistics. Simulation and computation, 46(4), 3319-3340
Open this publication in new window or tab >>A combinatorial estimation approach for storage reliability with initial failures based on periodic testing data
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 4, p. 3319-3340Article in journal (Refereed) Published
Abstract [en]

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. Unlike the operational reliability, storage reliability for certain types of products may not be always 100% at the beginning of storage since there are existing possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new combinatorial approach, the non-parametric measure for the estimates of the number of failed products and the current reliability at each testing time in storage, and the parametric measure for the estimates of the initial reliability and the failure rate based on the exponential reliability function, is proposed for estimating and predicting the storage reliability with possible initial failures. The proposed method has taken into consideration that, the initial failure and the reliability testing data before and during the storage process, are available for providing more accurate estimates of both initial failure probability and the probability of storage failures. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is provided in this paper. Finally, numerical examples are given to illustrate the method. Furthermore, a detailed comparison between the proposed method and the traditional method, for examining the rationality of assessment and prediction on the storage reliability, is presented. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.

Keywords
Storage reliability, Non-parameter (NP) estimation, Initial reliability, Least squares (LS) estimation, Maximum likelihood (ML) estimation
National Category
Economics and Business Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-21099 (URN)10.1080/03610918.2015.1130836 (DOI)000400186200054 ()2-s2.0-85011932299 (Scopus ID)
Available from: 2016-01-25 Created: 2016-01-25 Last updated: 2018-12-03Bibliographically approved
Zhang, Y., Zhao, M., Zhang, S., Wang, J. & Zhang, Y. (2017). An integrated approach to estimate storage reliability with initial failures based on E-Bayesian estimates. Reliability Engineering & System Safety, 159, 24-36
Open this publication in new window or tab >>An integrated approach to estimate storage reliability with initial failures based on E-Bayesian estimates
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2017 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 159, p. 24-36Article in journal (Refereed) Published
Abstract [en]

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. For certain types of products, Storage reliability may not always be 100% at the beginning of storage, unlike the operational reliability, which exist possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new integrated technique, the non-parametric measure based on the E-Bayesian estimates of current failure probabilities is combined with the parametric measure based on the exponential reliability function, is proposed to estimate and predict the storage reliability of products with possible initial failures, where the non-parametric method is used to estimate the number of failed products and the reliability at each testing time, and the parameter method is used to estimate the initial reliability and the failure rate of storage product. The proposed method has taken into consideration that, the reliability test data of storage products containing the unexamined before and during the storage process, is available for providing more accurate estimates of both the initial failure probability and the storage failure probability. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is presented in this paper. Finally, a numerical example is given to illustrate the method. Furthermore, a detailed comparison between the proposed and traditional method, for examining the rationality of assessment and prediction on the storage reliability, is investigated. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.

Keywords
Bayesian networks; Behavioral research; Decision making; Digital storage; Failure analysis; Forecasting; Maximum likelihood; Maximum likelihood estimation; Numerical methods; Probability; Program processors; Reliability; Statistical tests, Bayesian estimations; Integrated techniques; Least squares estimation; Non-parametric estimates; Nonparametric methods; Operational reliability; Reliability functions; Storage reliability, Parameter estimation
National Category
Mathematics
Identifiers
urn:nbn:se:hig:diva-22874 (URN)10.1016/j.ress.2016.10.024 (DOI)000392897600003 ()2-s2.0-84994571996 (Scopus ID)
Note

Funding Agency: National Natural Science Foundation of China  Grant Number: 70571018 and Grant Number: 11401007;  Natural Science Foundation of Jiangsu Province of China  Grant Number: BK20150455 

Available from: 2016-11-28 Created: 2016-11-28 Last updated: 2018-03-13Bibliographically approved
Hu, W., Mao, J., Yang, J. & Zhao, M. (2017). Maintainability design based on complex network. In: 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP): . Paper presented at 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2017), 15-17 Dec. 2017, Chengdu, China (pp. 309-314).
Open this publication in new window or tab >>Maintainability design based on complex network
2017 (English)In: 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2017, p. 309-314Conference paper, Published paper (Refereed)
Abstract [en]

Many faults come from the design phase. In order to improve the maintainability of the software, the design of software architecture must be adopted modular design. This paper presents a method of UML class diagram translated into a directed complex network with weight value. The relation weight coefficient matrix between classes can be calculated by Dijkstra algorithm. The clustering algorithm is implemented on the relation weight coefficient matrix. The result of clustering analysis is that the closely related classes can be clustered into a component. Finally, modular design of the software system can be realized. © 2017 IEEE.

Keywords
Class Digram, Complex Network, Maintainability, Software Design, UML, Clustering algorithms, Computer software, Clustering analysis, Dijkstra algorithms, Modular designs, Software systems, UML class diagrams, Weight coefficients, Weight values, Complex networks
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hig:diva-27630 (URN)10.1109/ICCWAMTIP.2017.8301503 (DOI)000464096300068 ()2-s2.0-85050662075 (Scopus ID)978-1-5386-1010-7 (ISBN)
Conference
14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2017), 15-17 Dec. 2017, Chengdu, China
Available from: 2018-08-13 Created: 2018-08-13 Last updated: 2019-08-28Bibliographically approved
Yang, J., Liu, Y., Xie, M. & Zhao, M. (2016). Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes. Journal of Systems and Software, 115, 102-110
Open this publication in new window or tab >>Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes
2016 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 115, p. 102-110Article in journal (Refereed) Published
Abstract [en]

Large software systems require regular upgrading that tries to correct the reported faults in previous versions and add some functions to meet new requirements. It is thus necessary to investigate changes in reliability in the face of ongoing releases. However, the current modeling frameworks mostly rely on the idealized assumption that all faults will be removed instantaneously and perfectly. In this paper, the failure processes in testing multi-release software are investigated by taking into consideration the delays in fault repair time based on a proposed time delay model. The model is validated on real test datasets from the software that has been released three times with new features. A comprehensive analysis of optimal release times based on cost-efficiency is also provided, which could help project managers to determine the best time to release the software. 

Keywords
Fault correction process, Multiple upgrading, Software reliability, Computer software, Fault detection, Open source software, Open systems, Reliability, Reliability analysis, Software engineering, Software testing, Time delay, Comprehensive analysis, Current modeling, Fault detection and correction process, Large software systems, Model and analysis, Project managers
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-21360 (URN)10.1016/j.jss.2016.01.025 (DOI)000373411400007 ()2-s2.0-84960862129 (Scopus ID)
Available from: 2016-03-29 Created: 2016-03-29 Last updated: 2018-12-03Bibliographically approved
Liu, Y., Xie, M., Yang, J. & Zhao, M. (2015). A New Framework and Application of Software Reliability Estimation Based on Fault Detection and Correction Processes. In: Proceedings: IEEE International Conference on Software Quality, Reliability and Security, QRS 2015. Paper presented at IEEE International Conference on Software Quality, Reliability and Security, QRS 2015, 3-5 August 2015, Vancouver, Canada (pp. 65-74). IEEE conference proceedings, Article ID 7272916.
Open this publication in new window or tab >>A New Framework and Application of Software Reliability Estimation Based on Fault Detection and Correction Processes
2015 (English)In: Proceedings: IEEE International Conference on Software Quality, Reliability and Security, QRS 2015, IEEE conference proceedings, 2015, p. 65-74, article id 7272916Conference paper, Published paper (Refereed)
Abstract [en]

Software reliability growth modeling plays an important role in software reliability evaluation. To incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes has attracted widespread research attention recently. However, the assumption of the stochastic fault correction time delay brings more difficulties in modeling and estimating the parameters. In practice, other than the grouped fault data, software test records often include some more detailed information, such as the rough time when one fault is detected or corrected. Such semi-grouped dataset contains more information about fault removal processes than commonly used grouped dataset. Using the semi-grouped datasets can improve the accuracy of time delayed models. In this paper, a fault removal modelling framework for software reliability with semi-grouped data is studied and extended into multi-released software. Also, the corresponding parameter estimation is carried out with Maximum Likelihood estimation method. One test dataset with three releases from a practical software project is applied with the proposed framework, which shows satisfactory performance with the results.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keywords
fault correction process, maximum likelihood estimation, Non-Homogenous Poisson Process, queuing model, Software reliability
National Category
Information Systems
Identifiers
urn:nbn:se:hig:diva-21492 (URN)10.1109/QRS.2015.20 (DOI)000380466800009 ()2-s2.0-84962120863 (Scopus ID)978-146737989-2 (ISBN)
Conference
IEEE International Conference on Software Quality, Reliability and Security, QRS 2015, 3-5 August 2015, Vancouver, Canada
Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2018-03-13Bibliographically approved
Zhao, B., Yang, J., Zhao, M., Li, Q. & Liu, Y. (2015). Wireless sensor network reliability modelling based on masked data. International Journal of Sensor Networks (IJSNet), 17(4), 217-223
Open this publication in new window or tab >>Wireless sensor network reliability modelling based on masked data
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2015 (English)In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 17, no 4, p. 217-223Article in journal (Refereed) Published
Abstract [en]

This paper studies the reliability modelling of wireless sensor networks (WSNs) with the masked data that are often observed in practice. The masked data are the system failure data when exact subsystems or components causing system failures cannot be identified. When the masked data are observed, however, it is difficult to estimate the WSN reliability since the failure processes of the subnets cannot be decomposed into simple subsystem processes. In this paper, an additive non-homogeneous poisson process (NHPP) model is proposed to describe the failure process of the WSN with subnets. The maximum likelihood estimation (MLE) procedure is developed to estimate the parameters in the proposed model. By applying the given procedure, the WSN reliability estimate can be relatively easy to obtain. A numerical example based on simulation data with random masking is also provided to illustrate the applicability of the methodology. 

Keywords
masked data; WSNs; wireless sensor networks; NHPP; non-homogeneous Poisson process; reliability; MLE; maximum likelihood estimation; network reliability; reliability modelling; WSN reliability; subnets; network failure; simulation; random masking.
National Category
Signal Processing
Identifiers
urn:nbn:se:hig:diva-19949 (URN)10.1504/IJSNET.2015.069584 (DOI)000358693100002 ()2-s2.0-84930432797 (Scopus ID)
Available from: 2015-07-01 Created: 2015-07-01 Last updated: 2018-12-03Bibliographically approved
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