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

Feng, Tao; Duan, Rongxing; Lin, Yanni; Zeng, Yining

doi:10.1177/0954406219864982

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…The higher the importance of each design objective, the higher its assigned weight would be. These algorithms are categorized into two groups (Feng et al, 2019): subjective and objective. In the former, the specialists determine the weights of each attribute based on their experience and knowledge, while in the latter, mathematical equations are used in order to calculate the weights on an analytical basis.…”

Section: Optimization Proceduresmentioning

confidence: 99%

“…After reaching several optimum answers, the MCDM technique is used in order to rank the design points among these answers. For this purpose, the technique for order preference by similarity to an ideal solution (TOPSIS) (Feng et al, 2019; Ge et al, 2019) is considered. This algorithm sorts the Pareto front design points with respect to their distance from the ideal answer.…”

Section: Optimization Proceduresmentioning

confidence: 99%

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Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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The application of piezoelectric materials in energy harvesting devices has become an interesting topic for designers in recent years. Most of the reported works consider electrical power as a major parameter throughout the design procedure. What is less discussed is considering other design factors like the fatigue life of the harvester as well as the financial costs of the device. In this research, a methodology is introduced to design an optimum harvester, taking into account the aforementioned design factors. The finite element model of a piezoelectric harvester used in roadways is developed and validated. The fatigue test constants of the piezoelectric material are extracted via a novel approach. A parametric study is conducted on the model to generate a dataset containing electrical and mechanical characteristics, where it is used as an input for training an artificial neural network to model the behavior of the harvester. In order to evaluate the level of importance of the optimization objectives, two methods are employed: the Shannon entropy method and equal weighting factors. Results show the effectiveness of the model, where considering the electromechanical characteristics of the module is important in terms of overall performance, efficiency, durability, and financial costs.

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Section: Optimization Proceduresmentioning

confidence: 99%

Section: Optimization Proceduresmentioning

confidence: 99%

Optimization of a piezoelectric energy harvester considering electrical fatigue

Niasar

Dahmardeh

Googarchin

2020

Journal of Intelligent Material Systems and Structures

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Effective sensor placement based on a VIKOR method considering common cause failure in the presence of epistemic uncertainty

Duan¹,

He²,

Feng³

et al. 2021

Eksploatacja i Niezawodność – Maintenance and Reliability

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Owing to expensive cost and restricted structure, limited sensors are allowed to install in modern systems to monitor the working state, which can improve their availability. Therefore, an effective sensor placement method is presented based on a VIKOR algorithm considering common cause failure (CCF) under epistemic uncertainty in this paper. Specifically, a dynamic fault tree (DFT) is developed to build a fault model to simulate dynamic fault behaviors and some reliability indices are calculated using a dynamic evidence network (DEN). Furthermore, a VIKOR method is proposed to choose the possible sensor locations based on these indices. Besides, a sensor model is introduced by using a priority AND gate (PAND) to describe the failure sequence between a sensor and a component. All placement schemes can be enumerated when the number of sensors is given, and the largest system reliability is the best alternative among the placement schemes. Finally, a case study shows that CCF has some influence on sensor placement and cannot be neglected in the reliabilitybased sensor placement.

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Understanding the Role of Sensor Optimisation in Complex Systems

Suslu,

Ali,

Jennions

2023

Sensors

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Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective.

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

Cited by 3 publications

References 45 publications

Optimization of a piezoelectric energy harvester considering electrical fatigue

Optimization of a piezoelectric energy harvester considering electrical fatigue

Effective sensor placement based on a VIKOR method considering common cause failure in the presence of epistemic uncertainty

Understanding the Role of Sensor Optimisation in Complex Systems

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