Fuzzy Logic Based Fault Detection in Distributed Sensor Networks

Bhajantri, Lokesh B.

doi:10.26438/ijsrcse/v6i2.2732

“…The proposed mechanism uses three parameters: residual energy, data sensing value and distance. The traditional algorithm AOSD [25] , REFER [24] ,DPFDRM [22] and FBFTN [11] mechanism is based on residual energy and distance only. In the proposed distributed fault tolerance mechanism three parameters are considered like residual energy, data sensing value and distance using fuzzy logic.…”

Section: Simulation Resultsmentioning

confidence: 99%

Energy Attentive and Pre-fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Talmale

¹

,

Bhat

²

2021

Wireless Pers Commun

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Wireless Sensor Network (WSN) have transformed over recent years with incredible development in design of smart real time applications. In spite of countless potential, it poses numerous design issues like fault tolerant communication, low latency, scalability, transmission media and coverage problems. One of the foremost design issues of WSN is to detect the run time fault because it is unaware of internal operation of the sensor node. As a result, valuable sensed information is unable to reach its destination and performance start degrading throughout the routing period of the network. To address this issue, the proposed mechanism design a novel Pre-fault detection mechanism based on fuzzy rule based mechanism for multilevel transmission in distributed sensor networks. Many researchers have been focused on fault detection mechanisms without considering the dynamic behavior status of sensor node. In this proposed mechanism the Fuzzy decision rule set is identified to take routing decisions based on fuzzy fault count status. This mechanism helps to recognize the fault in advance and a routing decision has been taken in order to save energy as well as minimize delay. On the basis of node fault status data transmission rate is finalized in order to avoid more energy depletion. The experimented simulations results prove the accuracy of the proposed mechanism. The results demonstrate that the proposed mechanism performed well on judgment evaluation metrics such as energy dissipation ratio, packet drop rate and communication delay.

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“…The proposed mechanism uses three parameters: residual energy, data sensing value and distance. The traditional algorithm AOSD [25] , REFER [24] ,DPFDRM [22] and FBFTN [11]…”

Section: Simulation Resultsmentioning

confidence: 99%

Energy Attentive and Pre-Fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Talmale

¹

,

Bhat

²

2021

Preprint

0

View full text Add to dashboard Cite

Wireless Sensor Network (WSN) have transformed over recent years with incredible development in design of smart real time applications. In spite of countless potential, it poses numerous design issues like fault tolerant communication, low latency, scalability, transmission media and coverage problems. One of the foremost design issues of WSN is to detect the run time fault because it is unaware of internal operation of the sensor node. As a result, valuable sensed information is unable to reach its destination and performance start degrading throughout the routing period of the network. To address this issue, the proposed mechanism design a novel Pre-fault detection mechanism based on fuzzy rule based mechanism for multilevel transmission in distributed sensor networks. Many researchers have been focused on fault detection mechanisms without considering the dynamic behavior status of sensor node. In this proposed mechanism the Fuzzy decision rule set is identified to take routing decisions based on fuzzy fault count status. This mechanism helps to recognize the fault in advance and a routing decision has been taken in order to save energy as well as minimize delay. On the basis of node fault status data transmission rate is finalized in order to avoid more energy depletion. The experimented simulations results prove the accuracy of the proposed mechanism. The results demonstrate that the proposed mechanism performed well on judgment evaluation metrics such as energy dissipation ratio, packet drop rate and communication delay.

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“…Fuzzy rule bases, and the fuzzy logic in general on which they operate, act as a nonlinear mapping between inputs and outputs by means of determining the degree of membership to which "crisp" inputs belong to "fuzzy" qualitative states and using the fuzzy states to determine the consequence of the given inputs. Fuzzy rule bases have found application for fault diagnosis in various disciplines and numerous components-many of which are found in NPPs-including induction motors (Shetgaonkar, 2017), other standard rotating machinery (Da Silva et al, 2017), spur gears (Krishnakumari et al, 2017), bearings (Berredjem and Benidir, 2018), power transformers (Husain, 2018), diesel generators (Nain and Varde, 2013), distributed sensor networks (Bhajantri, 2018), and high-power lithium-ion batteries (Wu et al, 2017).…”

Section: Rule-based Methodsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Zhao

¹

,

Kim

²

,

Warns

³

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Fuzzy Logic Based Fault Detection in Distributed Sensor Networks

Cited by 5 publications

References 12 publications

Energy Attentive and Pre-fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Energy Attentive and Pre-fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Energy Attentive and Pre-Fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

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