System Reliability Assessment Based on Failure Propagation Processes

Lin, Sheng; Wang, Yanhui; Jia, Limin

doi:10.1155/2018/9502953

Cited by 19 publications

(15 citation statements)

References 29 publications

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“…This problem accords with the current industrial tendency that interoperability testing for 2 Complexity communication between the connected systems becomes more important [30,31]. Next, the local faults need to be preferentially identified, because these failures obviously influence the central part [32]. For example, if an EM log sends speed information with the wrong unit or resolution, other subsystems using the speed (e.g., navigation sensor or echo sounder) have abnormal behaviors sequentially.…”

Section: Interface Faults In Complexmentioning

confidence: 91%

Interface Data Modeling to Detect and Diagnose Intersystem Faults for Designing and Integrating System of Systems

Seo

Park

2018

Complexity

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In system of systems engineering, system integrators are in charge of compatible and reliable interfaces between subsystems. This study explains a systematic solution to identify and diagnose interface faults during designing and integrating systems of systems. Because the systems targeted in this study are real underwater vessels, we first have anatomized 188 interface data transferred between 22 subsystems of them. Based on this, two interface data models are proposed, which include data sets regarding messages and inner fields and transition and decision functions for them. Specifically, a structure model at the message level evaluates how inner fields belong to a message, and a logic model at the field level assesses how each field is interpreted and if the interpreted value is understandable. The software that supports the modeling is implemented using the following concepts: (1) a model-view-viewmodel pattern for overall software design and (2) a computer network for representing sequential properties of field interpretations. The proposed modeling and software facilitate diagnostic decisions by checking the consistency between interface protocols and obtained real data. As a practical use, the proposed work was applied to an underwater shipbuilding project. Within 10 interfaces, 14 fault cases were identified and diagnosed. They were gradually resolved during the system design and integration phases, which formed the basis of successful submarine construction.

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Section: Interface Faults In Complexmentioning

confidence: 91%

Interface Data Modeling to Detect and Diagnose Intersystem Faults for Designing and Integrating System of Systems

Seo

Park

2018

Complexity

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“…where The purpose of Part II is to discuss the process of fault propagation in the mechatronics system when a fault or local fault has occurred, which provides a basis for assessing system safety. However, in view of the shortcomings of the existing fault spreading models [31], we recognize the two core elements that lead to failure propagation in the holistic system. 1) System topology.…”

Section: B Problem Description For Safety Assessment Based On the Psmentioning

confidence: 99%

A New Function-Topology-Based Method for Assessing Passive Safety of Mechatronics Systems

et al. 2020

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Various safety assessment models for predicting the future state of systems based on online monitoring data have been proposed. However, the complexity and interdependencies of mechatronics systems make it improbable to predict and prevent all possible failures/faults. Thus, it is also vital to assess the passive safety of mechatronics systems after a small or local fault occurs in order to make up for the shortcomings of online safety assessment. Hence, this paper proposes a passive safety assessment framework for a holistic system, according to the core chain of events related to component malfunction. The main contributions of this paper include three aspects. First, a component risk coefficient is proposed to more comprehensively reflect the risk degree of the component through analysis of a large number of fault data. Second, the fault propagation mechanism is explored to decrease the subjective effect based on the system topology and fault data. Third, a mapping function between system risk and system safety level is constructed; this function can provide support for management and maintenance personnel. A practical example of the bogie system for a high-speed train is examined to demonstrate the implementation and effectiveness and illustrate a potential application of the proposed passive safety method for assessing mechatronics system safety.

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“…A model to assess the reliability of smart distribution network considering the reliability of its interconnected ICT infrastructure is proposed in [10]; this model, however, is incapable of quantifying the effect of latency (as stressed in [11]) when rerouting of data is considered. Other widely accepted techniques to analyze the direct cyber-to-power impact on system reliability include Markovian model [12], Reliability Block Diagram (RBD) [13,14], fault-tree analysis [15], failure propagations studies [16], state-mapping techniques [17], and simulation approaches [18][19][20]. Nevertheless, these techniques require exhaustive computational effort, and the computational time grows exponentially with the size of the system being examined [21].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Reliability Assessment of Smart Grid Considering Risk of Failure of Communication Architecture

Zhu

Han

Milanović

et al. 2020

IEEE Trans. Smart Grid

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This paper introduces an analytical method, based on the Complex Network Theory (CNT), to assess the risk of the Smart Grid failure due to communication network malfunction, associated with latency and ICT network reliability. Firstly, the communication architecture is modelled using a two-step CNT frameworkan Operation Graph (OG) in step one and a Reliability Graph (RG) in step two. Secondly, the latency of data packets and the reliability of each communication device are incorporated into the model to identify the reliability of all operational communication paths for successful power system control purposes. Then, the risk of Smart Grid failure due to the communication network malfunction is quantified using a System Reliability Index (SRI). Next, sensitivity analysis is performed to assess the importance of each communication network component using two innovative Importance Measures (IM), namely System Reliability Advancement Worth (SRAW) and System Reliability Deterioration Worth (SRDW). Finally, the proposed approach is demonstrated on a laboratory-scale communication network.

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System Reliability Assessment Based on Failure Propagation Processes

Cited by 19 publications

References 29 publications

Interface Data Modeling to Detect and Diagnose Intersystem Faults for Designing and Integrating System of Systems

Interface Data Modeling to Detect and Diagnose Intersystem Faults for Designing and Integrating System of Systems

A New Function-Topology-Based Method for Assessing Passive Safety of Mechatronics Systems

Methodology for Reliability Assessment of Smart Grid Considering Risk of Failure of Communication Architecture

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