The Experts below are selected from a list of 41016 Experts worldwide ranked by ideXlab platform
Zhaodan Kong - One of the best experts on this subject based on the ideXlab platform.
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data driven real valued timed Failure Propagation graph refinement for complex system fault diagnosis
IEEE Control Systems Letters, 2021Co-Authors: Gang Chen, Xinfan Lin, Zhaodan KongAbstract:Timed Failure Propagation Graphs (TFPGs) have been widely used for the Failure modeling and diagnosis of safety-critical systems. Currently most TFPGs are manually constructed by system experts, a process that can be time-consuming, error-prone, and even impossible for systems with highly nonlinear and machine-learning-based components. This letter proposes a new type of TFPGs, called Real-Valued Timed Failure Propagation Graphs (rTFPGs), designed for continuous-state systems. More importantly, it presents a systematic way of constructing rTFPGs by combining the powers of human experts and data-driven methods: first, an expert constructs a partial rTFPG based on his/her expertise; then a data-driven algorithm refines the rTFPG by adding nodes and edges based on a given set of labeled signals. The proposed approach has been successfully implemented and evaluated on three case studies.
Jian Wang - One of the best experts on this subject based on the ideXlab platform.
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comparative study on the transversal lengthwise thermal Failure Propagation and heating position effect of lithium ion batteries
Applied Energy, 2019Co-Authors: Jingwen Weng, Dongxu Ouyang, Mingyi Chen, Xiaoqing Yang, Guoqing Zhang, Jian WangAbstract:Abstract Because of the multi-layer structure and the diversified connection modes of most battery modules, systematically investigating the thermal Failure Propagation principles and mechanisms of lithium-ion batteries is critical to provide early warnings and protection for thermal runaway. In this work, a series of experimental studies and mathematical deductions were conducted to investigate the Propagation behavior of thermal Failures within two types of cells under various heating modes, and their heat transfer mechanisms were analyzed. In general, the thermal runaway phenomenon in Li (Ni1/3Co1/3Mn1/3) O2 cells is more severe than that in LiCoO2 cells. For different heating modes, lengthwise thermal Failure Propagation is more unlikely to occur in comparison with transversal thermal Failure Propagation; however, the former involves a more violent combustion. For different heating positions, heating near the positive pole results in the most violent phenomena. Additionally, a higher Tmax of 185.6 °C was obtained via middle heating in comparison with that obtained via heating on the poles. The temperature rising rate also varied, taking 1619, 1578, and 1699 s for the temperature to rise from 20 °C to 168.9 °C, 185.6 °C, and 173.4 °C through bottom, middle, and top heating, respectively. These phenomena were consequently ascribed to the different heat transfer rates along different directions inside the cells, including transversal/lengthwise Propagation, and positive-pole-directional/negative-pole-directional Propagation. These encouraging results may raise concerns about developing more precise and suitable surveillance and control measures to further enhance the thermal safety performance of cells/modules from both external and internal perspectives.
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experimental investigation of thermal Failure Propagation in typical lithium ion battery modules
Thermochimica Acta, 2019Co-Authors: Dongxu Ouyang, Jingwen Weng, Mingyi Chen, Que Huang, Jian WangAbstract:Abstract A series of thermal Failure researches were conducted to explore the effects of parameters on the Failure Propagation behavior within battery modules, including the gap between batteries, the state of charge (SOC) and traditional phase change material (PCM). Based on the results, obvious domino phenomena were revealed during the process of thermal Failure Propagation, where the thermal Failure of module (3 × 3) could be divided into six phases. It was illustrated that increasing battery gap could effectively prevent thermal Failure Propagation by postponing each phase of Failure and decreasing the Propagation speed. The Propagation speed agreed well with the square of battery gap, and it grew linearly with the increasing module SOC. Due to the low conductivity and diffusivity of traditional PCM, it was found that the Failure Propagation behavior of the module wrapped with PCM would be severer.
Gang Chen - One of the best experts on this subject based on the ideXlab platform.
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data driven real valued timed Failure Propagation graph refinement for complex system fault diagnosis
IEEE Control Systems Letters, 2021Co-Authors: Gang Chen, Xinfan Lin, Zhaodan KongAbstract:Timed Failure Propagation Graphs (TFPGs) have been widely used for the Failure modeling and diagnosis of safety-critical systems. Currently most TFPGs are manually constructed by system experts, a process that can be time-consuming, error-prone, and even impossible for systems with highly nonlinear and machine-learning-based components. This letter proposes a new type of TFPGs, called Real-Valued Timed Failure Propagation Graphs (rTFPGs), designed for continuous-state systems. More importantly, it presents a systematic way of constructing rTFPGs by combining the powers of human experts and data-driven methods: first, an expert constructs a partial rTFPG based on his/her expertise; then a data-driven algorithm refines the rTFPG by adding nodes and edges based on a given set of labeled signals. The proposed approach has been successfully implemented and evaluated on three case studies.
Alessandro Cimatti - One of the best experts on this subject based on the ideXlab platform.
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automated synthesis of timed Failure Propagation graphs
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Benjamin Bittner, Marco Bozzano, Alessandro CimattiAbstract:Timed Failure Propagation Graphs (TFPGs) are used in the design of safety-critical systems as a way of modeling Failure Propagation, and to evaluate and implement diagnostic systems. TFPGs are mostly produced manually, from a given dynamic system of greater complexity. In this paper we present a technique to automate the construction of TFPGs. It takes as input a set of Failure mode and discrepancy nodes and builds the graph on top of them, based on an exhaustive analysis of all system behaviors. The result is a TFPG that accurately represents the sequences of Failures and their effects as they appear in the system model. The proposed approach has been implemented on top of state-of-the-art symbolic model-checking techniques, and thoroughly evaluated on a number of synthetic and industrial benchmarks.
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smt based validation of timed Failure Propagation graphs
National Conference on Artificial Intelligence, 2015Co-Authors: Marco Bozzano, Alessandro Cimatti, Marco Gario, Andrea MicheliAbstract:Timed Failure Propagation Graphs (TFPGs) are a formalism used in industry to describe Failure Propagation in a dynamic partially observable system. TFPGs are commonly used to perform model-based diagnosis. As in any model-based diagnosis approach, however, the quality of the diagnosis strongly depends on the quality of the model. Approaches to certify the quality of the TFPG are limited and mainly rely on testing. In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs. We apply model-checking techniques to certify that a given TFPG satisfies (or not) a property of interest. Moreover, we discuss the problem of refinement and diagnosability testing and empirically show that our technique can be used to efficiently solve them.
Xinfan Lin - One of the best experts on this subject based on the ideXlab platform.
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data driven real valued timed Failure Propagation graph refinement for complex system fault diagnosis
IEEE Control Systems Letters, 2021Co-Authors: Gang Chen, Xinfan Lin, Zhaodan KongAbstract:Timed Failure Propagation Graphs (TFPGs) have been widely used for the Failure modeling and diagnosis of safety-critical systems. Currently most TFPGs are manually constructed by system experts, a process that can be time-consuming, error-prone, and even impossible for systems with highly nonlinear and machine-learning-based components. This letter proposes a new type of TFPGs, called Real-Valued Timed Failure Propagation Graphs (rTFPGs), designed for continuous-state systems. More importantly, it presents a systematic way of constructing rTFPGs by combining the powers of human experts and data-driven methods: first, an expert constructs a partial rTFPG based on his/her expertise; then a data-driven algorithm refines the rTFPG by adding nodes and edges based on a given set of labeled signals. The proposed approach has been successfully implemented and evaluated on three case studies.
