Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria

Salehpour-Oskouei, Farzin; Pourgol-Mohammad, Mohammad

doi:10.1177/1748006x17742766

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…Other MCDA approaches could be obviously used, as well. For example, a dual index approach is proposed in Salehpour-Oskouei et al 41 based on Shannon entropy theory.…”

Section: Ahp For Model Trustworthiness Quantificationmentioning

confidence: 99%

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

Bani-Mustafa

Pedroni

Zio

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Risk assessment provides information to support decision-making. Then, the confidence that can be put in its outcomes is fundamental, and this depends on the accuracy, representativeness and completeness of the models used in the risk assessment. A quantitative measure is needed to assess the credibility and trustworthiness of the outcomes obtained from such models, for decision-making purposes. This article proposes a four-level, top-down, hierarchical tree to identify the main attributes and criteria that affect the level of trustworthiness of models used in risk assessment. The level of trustworthiness (Level 1) is broken down into two attributes (Level 2), three sub-attributes and one “leaf” attribute (Level 3), and seven basic “leaf” sub-attributes (Level 4). Based on this hierarchical decomposition, a bottom-up, quantitative approach is employed for the assessment of model trustworthiness, using tangible information and data available for the basic “leaf” sub-attributes (Level 4). Analytical hierarchical process is adopted for evaluating and aggregating the sub-attributes, and Dempster–Shafer theory is adopted to consider the uncertainty and the inconsistency in the experts’ judgments. The approach is applied to a case study concerning the modeling of the residual heat removal system of a nuclear power plant, to compute its failure probability. The relative trustworthiness of two mathematical models of different complexity is evaluated: a fault tree and a multi-states physics-based model. The trustworthiness of the multi-states physics-based model is found to outweigh that of the fault tree model, which can be explained by the fact that multi-states physics-based model takes into account the components failure dependency relations and degradation effects. The feasibility and reasonableness of the approach are, thus, demonstrated, paving the way for its potential applicability to inform decision-making on safety-critical systems.

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“…Other MCDA approaches could be obviously used, as well. For example, a dual index approach is proposed in Salehpour-Oskouei et al 41 based on Shannon entropy theory.…”

Section: Ahp For Model Trustworthiness Quantificationmentioning

confidence: 99%

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

Bani-Mustafa

Pedroni

Zio

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…It is required that not only giving evaluation result reasonably and quickly, but also being able to find the corresponding source of risk. In this case, a wide range of practical theories have been proposed to deal with the uncertainty in risk conceptualization and risk assessment, 32 including Bayesian networks, 33,34 Monte Carlo, 35 evidence theory 36 and so on. Some of them rely on specific probability allocations through clear structure; while the latter two develop the interval concept to extra background knowledge, even though they may convey less information on judgments.…”

Section: Introductionmentioning

confidence: 99%

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Zhao

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,” but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.

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“…The location, type, and number of sensors are important factors in determining the cost competitiveness, functionality, and effectiveness of sensor networks. 1 Therefore, optimal sensor placement has an important impact on the system reliability and diagnostic performance. The main goal of optimal sensor placement is to determine the best locations of sensors on condition that the number and type of sensors are preset.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the impact of sensor reliability on the sensor placement of the system, Farzin et al. 1 utilized an AND gate to construct the sensor diagnostic model. However, this method ignored the time dependencies between sensors and components.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement based on multiattribute decision-making considering the common cause failure

Feng

Duan

Lin

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the sensor placement.

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Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria

Cited by 7 publications

References 19 publications

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Optimal sensor placement based on multiattribute decision-making considering the common cause failure

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