A Series DC Arc Fault Detection Algorithm Based on PV Operating Characteristics and Detailed Extraction of Pink Noise Behavior

2021 ◽  
Author(s):  
Jonathan C. Kim ◽  
Brad Lehman ◽  
Roy Ball
Keyword(s):  
Fault Detection ◽  
Detection Algorithm ◽  
Pink Noise ◽  
Operating Characteristics ◽  
Dc Arc

scholarly journals A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector Machine Classification

2021 ◽  
Author(s):  
Priyajit Biswas ◽  
Tuhina Samanta
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Correlation Coefficient ◽  
Detection Algorithm ◽  
Support Vector ◽  
Operating Characteristics ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Abstract Sensor nodes are tiny low-cost devices, prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector Machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then we used SVM to classify faulty readings from normal reading. After classification, faulty readings are discarded. We used network simulator NS-2.35 and Matlab for evaluation of our proposed method. We evaluated our fault detection algorithm using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC).

FAULT DETECTION ALGORITHM FOR TEMPERATURE SENSORS OF HOUSEHOLD REFRIGERATORS

2017 ◽  
Author(s):  
Everton Machado ◽  
Alexsandro Santos Silveira ◽  
Alexandre Trofino ◽  
claudio melo
Keyword(s):  
Fault Detection ◽  
Detection Algorithm ◽  
Temperature Sensors

scholarly journals Modeling of information channel by using of pseudorandom signals of nonlinear dynamical system

2020 ◽  
Vol 22 (4) ◽  
pp. 79-87
Author(s):  
I. K. Nasyrov ◽  
V. V. Andreev
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical System ◽  
Detection Algorithm ◽  
Main Step ◽  
Operating Characteristics ◽  
Nonlinear Dynamical ◽  
Pseudorandom Signals ◽  
Chaotic Signals ◽  
Correlation Properties

Pseudorandom signals of nonlinear dynamical systems are studied and the possibility of their application in information systems analyzed. Continuous and discrete dynamical systems are considered: Lorenz System, Bernoulli and Henon maps. Since the parameters of dynamical systems (DS) are included in the equations linearly, the principal possibility of the state linear control of a nonlinear DS is shown. The correlation properties comparative analysis of these DSs signals is carried out.. Analysis of correlation characteristics has shown that the use of chaotic signals in communication and radar systems can significantly increase their resolution over the range and taking into account the specific properties of chaotic signals, it allows them to be hidden. The representation of nonlinear dynamical systems equations in the form of stochastic differential equations allowed us to obtain an expression for the likelihood functional, with the help of which many problems of optimal signal reception are solved. It is shown that the main step in processing the received message, which provides the maximum likelihood functionals, is to calculate the correlation integrals between the components and the systems under consideration. This made it possible to base the detection algorithm on the correlation reception between signal components. A correlation detection receiver was synthesized and the operating characteristics of the receiver were found.

scholarly journals An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4787
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Power Plants ◽  
Similarity Index ◽  
Adaptive Strategy ◽  
Estimation Technique ◽  
Operating Characteristics ◽  
Model Accuracy ◽  
Auxiliary Equipment ◽  
Early Fault Detection

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

Fault Detection Algorithm Design for Electro-Mechanical Brake

2009 ◽  
Author(s):  
Kwangjin Han ◽  
Inkeun Kim ◽  
Kunsoo Huh ◽  
Myoungjune Kim ◽  
Joogon Kim ◽  
...  
Keyword(s):  
Fault Detection ◽  
Algorithm Design ◽  
Detection Algorithm
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