Optimal Sensor Placement Based on System Reliability Criterion Under Epistemic Uncertainty

Duan, Rongxing; Lin, Yanni; Feng, Tao

doi:10.1109/access.2018.2873420

Cited by 11 publications

(9 citation statements)

References 34 publications

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“…Fault tree is a logical causal diagram in the form of an inverted tree, which is used to represent the relationship between system faults and their causes. 33 Symbols, used to build fault tree, are generally divided into two categories: logic gate symbols and event symbols. Fault tree analysis (FTA) is to calculate the occurrence probability of the top event and identify the critical event, which is used to improve the system reliability.…”

Section: Fault Model Analysis Of Complex Systemsmentioning

confidence: 99%

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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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Section: Fault Model Analysis Of Complex Systemsmentioning

confidence: 99%

“…On this basis, Duan et al. 33 presented a new optimal sensor placement using system reliability criterion under epistemic uncertainty. Nevertheless, this method only used a diagnostic importance factor to determine the potential locations of sensors.…”

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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“…(iii) Only a handful of previous works on optimal sensor placement have considered cost-effectiveness while maximizing probabilistic detection of damages [10], [23]. Additionally, simplifying assumptions, such as having a fixed number of the same-type sensors and a one-dimensional sensor network, are used in the corresponding literature to minimize the computational cost of multi-type sensor optimization models [5], [6], [9], [17], [24], [25]. Moreover, the literature on human inspection is more focused on optimizing inspection time rather than deciding on inspection location and tool type [4], [7], [23].…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Multi-Type Sensors and Human Inspection Tools With Probabilistic Detection of Localized Damages for Pipelines

2020

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In this paper, a new layout optimization approach for health monitoring of locally damaged and non-piggable segments of pipelines is presented. In contrast to the existing literature, the proposed optimization-based approach considers (i) sensors network and human inspection, as detection methods, together, (ii) severity level of damages, such as damage size and risk of failure, (iii) three probabilistic detection metrics (to detect, infer, and size different damages) and a health monitoring cost metric simultaneously, (iv) several key attributes of detection methods, such as data acquisition cost and frequency, in an optimization context, and (v) probabilistic sampling methods and probabilistic damage data. An optimization model for determining decision variables such as the type and location of sensors and human inspection areas and tools along a pipeline is formulated. Due to the unavailability of publicly available real-world data and benchmarks, applications of the proposed approach are demonstrated using two notional examples with synthetically generated damage data. In the first example, a step-by-step demonstration of the proposed approach is given. Considering the severity level of different localized damages, it is shown that the proposed layout optimization approach can obtain a better and more robust solution, especially when used during a pipeline design, compared to those that only consider a single detection method or rely on deterministic damage data. In the second example, a longer pipeline segment with a greater density of localized damages is considered to show applicability of the proposed approach to larger sized problems. Finally, based on the results obtained it is demonstrated that the proposed approach provides a practical solution for consideration of probabilistic detection metrics and probabilistic damage data corresponding to stochastic degradation processes in optimal health monitoring of pipelines.

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“…Generally, the sensor location approaches can be classified into three primary subcategories, i.e. reliability-based approach [9], observability-based approach [8][9][10] and isolatability-based approach (also known as resolution) [11][12][13]. The reliability-based approach designs a sensor network with redundancy to ensure a certain abnormality can be detected timely even in the case that some sensors are malfunctioned.…”

Section: Introductionmentioning

confidence: 99%

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

Wang

Smith

et al. 2020

Journal of the Franklin Institute

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Fault detection and diagnosis (FDD) modules in a modern control system are effective in detecting and identifying abnormal process behaviours in a timely manner, ensuring the high-performance of large-scale engineering systems. The detection and isolation of faults is essentially built on the characterisation of the observed behaviour of a system. However, due to the large number of technical indicators available for measurement, as well as the various constraints of sensor installation, monitoring all the operating parameters of a large-scale engineering system is not feasible. Therefore, locating sensors optimally in a large-scale system, to achieve a comprehensive description of an abnormality, becomes a key issue to successfully apply diagnostic technologies to real world situations. In this paper, a fault feature reduction (FFR) based sensor location approach is proposed for optimal sensor placement so as to achieve the desired performance of fault detection and isolation. The behaviour of faults is firstly analysed using a fault tree to obtain a comprehensive understanding of the multi-dimensional relationships between faults and symptoms. A Boolean matrix is then constructed to represent the corresponding relations around faults and potential sensors. All the alternative configurations of sensors, for a desired diagnosis of a system, are obtained by eliminating the redundant fault features. The trade-off without a certain sensor is also attained using the  * Corresponding author.

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Optimal Sensor Placement Based on System Reliability Criterion Under Epistemic Uncertainty

Cited by 11 publications

References 34 publications

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

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

Layout Optimization of Multi-Type Sensors and Human Inspection Tools With Probabilistic Detection of Localized Damages for Pipelines

Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction

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