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  • 251. Zhao, B.
    et al.
    Yang, J. F.
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Li, Q.
    Liu, Y.
    A NHPP-based reliability model of wireless sensor networks2014In: Applied Mechanics and Materials: Volume 596: Mechatronics and Industrial Informatics II / [ed] Prasad Yarlagadda and Seung-Bok Choi, 2014, p. 877-882Conference paper (Refereed)
    Abstract [en]

    As the Wireless Sensor Networks(WSNs) are widely applied to various fields recent years, the quality of WSN has been increasingly concerned. Since reliability is the most important factor of quality, how to evaluate reliability of WSN through failure counting is our main subject. In this paper, we try to utilize NHPP theorem in the failure process of WSN and propose a reliability model based on NHPP. And also, the model validation is conducted by simulation. 

  • 252. Zhao, B.
    et al.
    Yang, J. F.
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Li, Q.
    Liu, Y.
    An additive NHPP-Based reliability model of wireless sensor networks2014In: Applied Mechanics and Materials: Volumes 602 - 605: Advanced Manufacturing and Information Engineering, Intelligent Instrumentation and Industry Development, 2014, p. 3206-3212Conference paper (Refereed)
    Abstract [en]

    As the Wireless Sensor Networks(WSNs) are widely implemented in various fields recent years, the quality of WSNs has been increasingly concerned. WSNs can usually be divided into sub-nets, which assumed to work or fail independently. Through the failure data of those sub-nets, the additive NHPP model for reliability evaluation is composed, and then the maximum likelihood estimation is applied to estimate the unknown parameters in the model. Finally, the simulation shows that the additive NHPP model is better than general NHPP model under certain circumstances. 

  • 253.
    Zhao, B.
    et al.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Yang, J.
    Guizhou Institute of Technology, Guiyang, China .
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Li, Q.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Liu, Y.
    Beijing University of Posts and Telecommunications, Beijing, China .
    Wireless sensor network reliability modelling based on masked data2015In: International Journal of Sensor Networks (IJSNet), ISSN 1748-1279, E-ISSN 1748-1287, Vol. 17, no 4, p. 217-223Article in journal (Refereed)
    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. 

  • 254.
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Preface2011In: ICRMS'2011 - Safety First, Reliability Primary: Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, art. no. 5979227, 2011Conference paper (Other academic)
  • 255.
    Zhao, Ming
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety2011Conference proceedings (editor) (Refereed)
  • 256.
    Zhao, Ming
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production. Reliability Engineering Center, Guizhou University, Guiyang, China .
    Yang, Jianfeng
    Guizho University.
    A DCA-based method for software prognostics and health management2012In: Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012, 2012, p. 6228830-Conference paper (Refereed)
    Abstract [en]

    In recent years, the prognostics and health management (PHM) technique has greatly been developed especially for electronic devices and systems. Little work, however, has been done on PHM techniques for software systems. During their total lifecycle, the software systems, due to various reasons such as new requirements or changes in technology, have to experience changes that can lead to a degeneration of the system's architecture. Therefore, it is important and necessary to identify and predict the software health status in order to avoid serious degeneration or erosion of system's architecture. In this paper, a software PHM approach based on Discriminant Coordinates Analysis (DCA) is proposed, and the health status of software systems is suggested to be categorized into five groups: Healthy, Normal, Sub-healthy, Unhealthy and Deteriorated. The proposed model based on software design metrics can help managers and developers predict and identify the software health status. Numerical example based on simulation data is given to illustrate its application on the modeling process.

  • 257.
    Zhao, Ming
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Zhang, Y. J.
    School of Mathematics and Physics, Anhui University of Technology, Maanshan, China.
    Yang, J. F.
    Faculty of Information Engineering, Guizhou Institute of Technology, Guiyang, China.
    Masked data analysis for storage reliability model with initial failures2018In: 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 (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.

  • 258.
    Zhao, Ming
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Industrial economics. University of Gävle, Center for Logistics and Innovative Production.
    Zhang, Y
    Guizho University.
    Xu, F R
    Guizho University.
    ESS profiles with step stress level2011In: ICRMS'2011 - Safety First, Reliability Primary: Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, 2011, p. 1156-1161Conference paper (Refereed)
    Abstract [en]

    Environmental Stress Screening (ESS) is usually referred to the process of exposing a product to environmental stresses for detecting and eliminating latent defects made in manufacturing process. The common ESS profiles are using thermal cycling, random vibration or their combinations as the screening stresses. Various guidelines and standards have been available for determining an ESS profile. Nevertheless, there are cases that the standard ESS profiles may not be efficient enough to detect the latent defects in some manufacturing process. This paper reports the results of one industrial project on the ESS profile design for circuits. In the project, a comparative study was carried out for the evaluation of the ESS strength under the ESS profiles with incremental step stresses (ISS). The ISS profile under the study is firstly to implement a step thermal cycling and then perform the test with two screening stimuli concurrently: ordinary thermal cycling and random vibration of step increasing intensity levels. For the purpose of the comparative study, the ESS test samples were consisted of two groups A and B that were made at two different levels of manufacturing: soldering by skilled and unskilled workers, respectively. The comparative study shows that the ISS profiles are effective for exposing the latent defects in sample group A and B. Based on the project results, an ESS profile is recommended in this paper.

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