The Experts below are selected from a list of 658221 Experts worldwide ranked by ideXlab platform
Cheng Qian - One of the best experts on this subject based on the ideXlab platform.
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iot enabled real time Production Performance analysis and exception diagnosis model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Naiqi Wu, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
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IoT-Enabled Real-Time Production Performance Analysis and Exception Diagnosis Model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
Yingfeng Zhang - One of the best experts on this subject based on the ideXlab platform.
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iot enabled real time Production Performance analysis and exception diagnosis model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Naiqi Wu, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
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IoT-Enabled Real-Time Production Performance Analysis and Exception Diagnosis Model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
Zhao Yulong - One of the best experts on this subject based on the ideXlab platform.
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Research on the Production Performance of multistage fractured horizontal well in shale gas reservoir
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Zhang Deliang, Zhang Liehui, Guo Jingjing, Zhou Yuhui, Zhao YulongAbstract:Abstract This paper extends a linear flow model to research the Production Performance of a multi-fractured horizontal well in a shale gas reservoir. The model considers desorption, diffusion (Knudsen), discontinuous micro-fractures and stimulated reservoir volume (SRV), which are widely accepted as the obvious characteristics of shale gas reservoirs. The solution was derived in the Laplace domain, and then inverted to the real time domain using the numerical algorithm proposed by Stehfest (1970). After that, the log–log curves of rate decline and accumulative Production are plotted. Based on the curves, the effects of diffusion, desorption gas, net-fracture system, micro-fractures, fracture permeability and Langmuir volume on Production Performance were analyzed. These analyses have significant importance for understanding the Production decline dynamic in shale gas reservoirs.
Wenbo Wang - One of the best experts on this subject based on the ideXlab platform.
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iot enabled real time Production Performance analysis and exception diagnosis model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Naiqi Wu, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
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IoT-Enabled Real-Time Production Performance Analysis and Exception Diagnosis Model
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Yingfeng Zhang, Wenbo Wang, Cheng QianAbstract:The recent developments of technologies in Internet of Things (IoT) provide the opportunities for smart manufacturing with real-time traceability, visibility, and interoperability in Production planning, execution, and control. To fulfill this target, this work presents a real-time Production Performance analysis and exception diagnosis model (PAEDM). By this model, hierarchical-timed-colored Petri net (HTCPN) with smart tokens that change just like smart objects in practice is used to analyze the sensor data such that the critical Performance information can be perceived. Decision Tree is used to diagnose exceptions from the critical Production Performance, so that persuasive qualitative and quantitative exception information can be extracted accurately. The presented method is demonstrated by a case study and simulation results show that PAEDM can be used to effectively analyze Production Performance and exceptions in real-time for dynamic and stochastic manufacturing processes.
Zhang Deliang - One of the best experts on this subject based on the ideXlab platform.
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Research on the Production Performance of multistage fractured horizontal well in shale gas reservoir
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Zhang Deliang, Zhang Liehui, Guo Jingjing, Zhou Yuhui, Zhao YulongAbstract:Abstract This paper extends a linear flow model to research the Production Performance of a multi-fractured horizontal well in a shale gas reservoir. The model considers desorption, diffusion (Knudsen), discontinuous micro-fractures and stimulated reservoir volume (SRV), which are widely accepted as the obvious characteristics of shale gas reservoirs. The solution was derived in the Laplace domain, and then inverted to the real time domain using the numerical algorithm proposed by Stehfest (1970). After that, the log–log curves of rate decline and accumulative Production are plotted. Based on the curves, the effects of diffusion, desorption gas, net-fracture system, micro-fractures, fracture permeability and Langmuir volume on Production Performance were analyzed. These analyses have significant importance for understanding the Production decline dynamic in shale gas reservoirs.
