scholarly journals Fault Detection for Two-Dimensional Roesser Systems With Sensor Faults

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 6197-6203 ◽  
Author(s):  
Yingying Ren ◽  
Da-Wei Ding
Keyword(s):  
Fault Detection ◽  
Two Dimensional ◽  
Sensor Faults

A Neural Network Ensemble-Based Approach to Sensor Fault Detection

2011 ◽  
Vol 467-469 ◽  
pp. 923-927
Author(s):  
Ai She Shui ◽  
Wei Min Chen ◽  
Li Chuan Liu ◽  
Yong Hong Shui
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Sensor Faults ◽  
Neural Network Ensemble ◽  
Sensor Fault ◽  
Detection Approach ◽  
Tank System ◽  
Sensor Fault Detection ◽  
Feedback Control Systems

This paper focuses on the problem of detecting sensor faults in feedback control systems with multistage RBF neural network ensemble-based estimators. The sensor fault detection framework is introduced. The modeling process of the estimator is presented. Fault detection is accomplished by evaluating residuals, which are the differences between the actual values of sensor outputs and the estimated values. The particular feature of the fault detection approach is using the data sequences of multi-sensor readings and controller outputs to establish the bank of estimators and fault-sensitive detectors. A detectability study has also been done with the additive type of sensor faults. The effectiveness of the proposed approach is demonstrated by means of three tank system experiment results.

scholarly journals Finite-Frequency Fault Detection for Two-Dimensional Roesser Systems

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5818-5825 ◽  
Author(s):  
Da-Wei Ding ◽  
Xiao-Jian Li ◽  
Yingying Ren ◽  
Fang Qiu
Keyword(s):  
Fault Detection ◽  
Two Dimensional ◽  
Finite Frequency

scholarly journals A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4615 ◽  
Author(s):  
Anja Babić ◽  
Ivan Lončar ◽  
Barbara Arbanas ◽  
Goran Vasiljević ◽  
Tamara Petrović ◽  
...  
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Environmental Monitoring ◽  
Mobile Sensor Networks ◽  
Experimental Setup ◽  
Collective Decision Making ◽  
Sensor Faults ◽  
Mobile Sensor ◽  
Lagoon Of Venice ◽  
Environmental Anomalies

This paper presents a novel autonomous environmental monitoring methodology based on collaboration and collective decision-making among robotic agents in a heterogeneous swarm developed within the project subCULTron, tested in a realistic marine environment. The swarm serves as an underwater mobile sensor network for exploration and monitoring of large areas. Different robotic units enable outlier and fault detection, verification of measurements and recognition of environmental anomalies, and relocation of the swarm throughout the environment. The motion capabilities of the robots and the reconfigurability of the swarm are exploited to collect data and verify suspected anomalies, or detect potential sensor faults among the swarm agents. The proposed methodology was tested in an experimental setup in the field in two marine testbeds: the Lagoon of Venice, Italy, and Biograd an Moru, Croatia. Achieved experimental results described in this paper validate and show the potential of the proposed approach.

scholarly journals Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 745 ◽  
Author(s):  
Malathy Emperuman ◽  
Srimathi Chandrasekaran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Fault Detection ◽  
Vector Quantization ◽  
Probabilistic Neural Network ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Sensor Faults ◽  
Continuous Density

Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

Investigating Fault Detection and Diagnosis in a Hydraulic Pitch System Using a State Augmented EKF-Approach

2019 ◽  
Author(s):  
Magnus F. Asmussen ◽  
Henrik C. Pedersen ◽  
Lina Lilleengen ◽  
Andreas Larsen ◽  
Thomas Farsakoglou
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
System Parameters ◽  
Power Regulation ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Drop Outs

Abstract Pitch systems impose an important part of today’s wind turbines, where they are both used for power regulation and serve as part of a turbines safety system. Any failure on a pitch system is therefore equal to an increase in downtime of the turbine and should hence be avoided. By implementing a Fault Detection and Diagnosis (FDD) scheme faults may be detected and estimated before resulting in a failure, thus increasing the availability and aiding in the maintenance of the wind turbine. The focus of this paper is therefore on the development of a FDD algorithm to detect leakage and sensor faults in a fluid power pitch system. The FDD algorithm is based on a State Augmented Extended Kalman Filter (SAEKF) and a bank of observers, which is designed utilizing an experimentally validated model of a pitch system. The SAEKF is designed to detect and estimate both internal and external leakage faults, while also estimating the unknown external load on the system, and the bank of observers to detect sensor drop-outs. From simulation it is found that the SAEKF may detect both abrupt and evolving internal and external leakages, while being robust towards noise and variation in system parameters. Similar it is found that the scheme is able to detect sensor drop-outs, but is less robust towards this.

Fault detection and isolation of two-dimensional (2D) systems

2017 ◽  
Vol 99 ◽  
pp. 25-32 ◽  
Author(s):  
Amir Baniamerian ◽  
Nader Meskin ◽  
Khashayar Khorasani
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
2D Systems ◽  
Two Dimensional
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