The Experts below are selected from a list of 48732 Experts worldwide ranked by ideXlab platform

Jing Gong - One of the best experts on this subject based on the ideXlab platform.

  • a Reliability Assessment of the hydrostatic test of pipeline with 0 8 design factor in the west east china natural gas pipeline iii
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

  • A Reliability Assessment of the Hydrostatic Test of Pipeline with 0.8 Design Factor in the West–East China Natural Gas Pipeline III
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

Lei He - One of the best experts on this subject based on the ideXlab platform.

  • a Reliability Assessment of the hydrostatic test of pipeline with 0 8 design factor in the west east china natural gas pipeline iii
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

  • A Reliability Assessment of the Hydrostatic Test of Pipeline with 0.8 Design Factor in the West–East China Natural Gas Pipeline III
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

Weichao Yu - One of the best experts on this subject based on the ideXlab platform.

  • a Reliability Assessment of the hydrostatic test of pipeline with 0 8 design factor in the west east china natural gas pipeline iii
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

  • A Reliability Assessment of the Hydrostatic Test of Pipeline with 0.8 Design Factor in the West–East China Natural Gas Pipeline III
    Energies, 2018
    Co-Authors: Lei He, Weichao Yu, Jing Gong
    Abstract:

    The use of 0.8 design factor in Chinese pipeline industry is a breakthrough with the success of the test pipe section in the west–east China gas pipeline III. For such a design factor, the traditional P-V (Pressure-Volume) curve based pressure test control cannot describe the details of the process, and the 0/1 type failure is not an efficient index to show the safety level of the pipeline. In this paper, a Reliability based Assessment Method is proposed to monitor the real-time failure probability of the pipeline during the hydrostatic test process. The Reliability index can be used as the degree of risk. Following the actual hydrostatic testing of a test pipe section with 0.8 design factor in the west–east China gas pipeline III, Reliability analysis was performed using Monte Carlo technique. The basic values of input parameters of the limit state equations are based on the data collected from either the tested section or the recommended value in the codes. The analysis of limit states, i.e., the yielding deformation and the excessive plastic deformation of pipeline, proceeded based on these distributions. Finally, it is found that the gradually increased water pressure makes the failure probability increase accordingly. A Reliability Assessment Method was proposed and illustrated with the practical pressure test process.

Jinjun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • data driven Reliability Assessment Method of integrated energy systems based on probabilistic deep learning and gaussian mixture model hidden markov model
    Renewable Energy, 2021
    Co-Authors: Lixun Chi, Enrico Zio, Meysam Qadrdan, Li Zhang, Lin Fan, Jing Zhou, Zhaoming Yang, Jinjun Zhang
    Abstract:

    Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic Methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time Reliability of IESs is proposed by integrating different machine learning Methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the Reliability Assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed Method. The results show that the Method is able to effectively evaluate the Reliability of IESs, which can provide useful information for system operation and management.

Enrico Zio - One of the best experts on this subject based on the ideXlab platform.

  • data driven Reliability Assessment Method of integrated energy systems based on probabilistic deep learning and gaussian mixture model hidden markov model
    Renewable Energy, 2021
    Co-Authors: Lixun Chi, Enrico Zio, Meysam Qadrdan, Li Zhang, Lin Fan, Jing Zhou, Zhaoming Yang, Jinjun Zhang
    Abstract:

    Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic Methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time Reliability of IESs is proposed by integrating different machine learning Methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the Reliability Assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed Method. The results show that the Method is able to effectively evaluate the Reliability of IESs, which can provide useful information for system operation and management.