Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks

Gao, Yabin; Xiao, Fu; Liu, Jianxing; Wang, Ruchuan

doi:10.1109/tii.2018.2812771

Cited by 155 publications

(45 citation statements)

References 46 publications

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“…Therefore, it creates traffic congestion in the network and spends significant extra energy to detect failures. Gao et al 17 presented a distributed soft fault detection mechanism for WSN using the rule‐based method. This approach offered a type 2 fuzzy‐based fault detection model for WSN.…”

Section: Related Workmentioning

confidence: 99%

“…The sink/BS is located at the outside of the network. We summarize the main parameter setting in Table 1 17,24,25 . The proposed green fault detection scheme was evaluated and compared with four candidate protocols, namely, DFD, 18 DSFD, 28 HFD, 23 and DFLFND 29 .…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Hence, it is essential to detect the faulty node in the network to make the correct decision in the IoT‐based smart system. There have been several existing works studying the fault detection for WSNs 16,17 . Existing fault detection approaches are mainly categorized into four different categories: rule‐based method, 17 estimation‐based method, 18,19 learning‐based method, 16,20–22 and time‐series‐analysis‐based method 23 .…”

Section: Introductionmentioning

confidence: 99%

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Green fault detection scheme for IoT‐enabled wireless sensor networks

Chanak

Banerjee

Bose

2020

Int J Communication

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SummaryWireless sensor network (WSN) is one of the major components for the Internet of Things (IoT)‐based smart systems that accumulate meaningful information and send them to the end‐user. In IoT‐enabled WSNs, large numbers of sensor nodes are deployed in an on‐site environment to collect and transmit different physical parameters from the monitoring environments to a central repository. However, low‐cost sensing devices in IoT‐enabled WSNs are prone to fail due to energy depletion, software failures, and hardware failures. The existing fault detection approaches consume notable additional energy to detect and overcome the failures. As a result, the whole network is subject to premature death. This article proposes a green fault detection scheme for IoT‐enabled WSNs to detect faulty nodes and solve the premature death problem of the network. Furthermore, a faulty node reuse mechanism is proposed that significantly prolongs the network lifetime. We analyze the properties of the proposed algorithm mathematically and validate its performance through extensive simulation and real‐life experiments. The simulation and experimental results show improved performance of the proposed scheme in terms of fault detection accuracy, false alarm rate, and network lifetime.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Green fault detection scheme for IoT‐enabled wireless sensor networks

Chanak

Banerjee

Bose

2020

Int J Communication

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“…With the help of such an effective technique, the past few years have witnessed a quickly growing interest in design and analysis of T-S fuzzy-based systems. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] To name a few, the authors in Reference 25 designed a state feedback controller by using T-S fuzzy model for networked systems subject to data missing. The reliable dissipative filtering issue was discussed for T-S fuzzy time-delay plants in Reference 26.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Zhang

Dong

2020

Intl J Robust & Nonlinear

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This article investigates the sampled-data containment control issue for nonlinear multiagent systems (MASs) with packet losses in the framework of Takagi-Sugeno fuzzy model. In the process of designing fuzzy sampled-data containment controller, a crucial factor, that is, asynchronous premise variables, is tackled via the proposed novel asynchronous premise rebuilding approach. Based on the fact that there may be packet losses in communication networks, the fuzzy sampled-data MAS is concerned as a switched version with two subsystems. Then, in the light of average dwell-time method and Lyapunov stability theory, sufficient conditions of designing fuzzy sampled-data containment controller are derived such that the states containment can be achieved for considered systems in spite of packet losses. Finally, both a numerical simulation and a Caltech multivehicle wireless test bed simulation are presented to confirm the effectiveness of the proposed control scheme.

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“…The presence of potential faults or anomalies is detected on-line when the measured signal falls outside the time varying upper and lower bounds. The IM general concept has also been investigated in the area of fuzzy modeling where upper and lower membership functions along with weighting coefficients are used to characterize the uncertainties in the so called interval type-2 fuzzy approach [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

Crispoltoni

Fravolini

Balzano

et al. 2018

Sensors

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This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings.

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Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks

Cited by 155 publications

References 46 publications

Green fault detection scheme for IoT‐enabled wireless sensor networks

Green fault detection scheme for IoT‐enabled wireless sensor networks

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

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