Fault Detection Model for Multi Robotic System Using Formal Method Event-B

2021 ◽  
pp. 307-316
Author(s):  
Emad Awwama ◽  
Ammar Alhaj Ali ◽  
Roman Jasek ◽  
Bronislav Chramcov ◽  
Said Krayem ◽  
...  
Keyword(s):  
Fault Detection ◽  
Formal Method ◽  
Robotic System ◽  
Detection Model

scholarly journals A Data-Based Fault-Detection Model for Wireless Sensor Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 6171 ◽  
Author(s):  
Jangsik Bae ◽  
Meonghun Lee ◽  
Changsun Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Data Transmission ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensors And Actuators ◽  
Detection Model ◽  
Management Platform

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

A Fault Detection Model for Large-Scale Circuit Communication Systems

2014 ◽  
Vol 651-653 ◽  
pp. 383-386
Author(s):  
Wen Qun Duan ◽  
Yang Yun
Keyword(s):  
Fault Detection ◽  
Traditional Method ◽  
Communication Systems ◽  
Wavelet Transformation ◽  
Large Scale ◽  
Fault Location ◽  
Detection Method ◽  
Wavelet Function ◽  
Detection Model ◽  
Long Time

In the fault detection process for large-scale circuit communication systems, the traditional method needs to exam each node to determine whether a failure exists. It is complex and needs long time which causes a certain lagging. To avoid the defects mentioned above, this paper proposes a fault detection method based on wavelet transformation that calculates the changes of coefficients of the wavelet transform and the similarity between wavelet function and the signal. This kind of fault detection can pre-process the failure might occur and effectively improve the efficiency. The experimental results approve that the proposed method can predicate the fault location and reach satisfied effects.

Synchronization Optimization Simulation of Optical Fiber Network Fault Features and Model Parameters

2014 ◽  
Vol 986-987 ◽  
pp. 1596-1599
Author(s):  
Yong Huang ◽  
Heng Jun Liu ◽  
Zeng Liang Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Optical Fiber ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Improved Genetic Algorithm ◽  
Fiber Network ◽  
Detection Model ◽  
Network Fault ◽  
Optical Fiber Network

The traditional optical fiber network fault detection method has not considered the relationship between the fault characteristics and KNN parameters, it is optimized separately, and the accuracy of optical fiber network fault diagnosis is low. The synchronous optimization fault detection model of fault characteristics detection model parameters is proposed. The candidate feature subsets and K adjacent parameters are used to construct the optical fiber network fault detection model. The improved genetic algorithm is used to solve the mathematical model, and the better accurate rate of fault diagnosis for optical fiber network is obtained. The simulation is taken for testing the performance of model, compared to the traditional model, the new model has better accurate detection rate, and the detection accuracy is improved greatly, the efficiency of optical fiber network fault detection is improved, it has great application value in practice.

scholarly journals Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

2021 ◽  
Vol 11 (17) ◽  
pp. 8030
Author(s):  
Mingzhu Tang ◽  
Zhonghui Peng ◽  
Huawei Wu
Keyword(s):  
Experimental Data ◽  
Control System ◽  
Fault Detection ◽  
False Alarm ◽  
Wind Turbine ◽  
False Alarm Rate ◽  
Optimization Algorithm ◽  
Pitch Control ◽  
Detection Model ◽  
Light Gradient

To address the issue of a large calculation and difficult optimization for the traditional fault detection of a wind turbine-based pitch control system, a fault detection model, based on LightGBM by the improved Harris Hawks optimization algorithm (light gradient boosting machine by the improved Harris Hawks optimization,IHHO-LightGBM) for the wind turbine-based pitch control system, is proposed in this article. Firstly, a trigonometric function model is introduced by IHHO to update the prey escape energy, to balance the global exploration ability and local development ability of the algorithm. In this model, the fault detection false alarm rate is used as the fitness function, and the two parameters are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the performance of the fault detection model. Three different fault data of the pitch control system in actual operations of domestic wind farms are used as the experimental data, the Pearson correlation analysis method is introduced, and the wind turbine power output is taken as the main state parameter, to analyze the correlation degree of all the characteristic variables of the data and screen the important characteristic variables out, so as to achieve the effective dimensionality reduction process of the data, by using the feature selection method. Three established fault detection models are selected and compared with the proposed method, to verify its feasibility. The experimental data indicate that compared with other algorithms, the fault detecting ability of the proposed model is improved in all aspects, and the false alarm rate and false negative rate are lower.

scholarly journals Design of Automatic Detection Model for Single-phase Earth Fault in Distribution Network

1970 ◽  
Vol 1 (1) ◽  
Author(s):  
Yang Fan
Keyword(s):  
Fault Detection ◽  
Single Phase ◽  
Distribution Network ◽  
Automatic Detection ◽  
Input Vector ◽  
Detection Accuracy ◽  
Detection Model ◽  
Earth Fault ◽  
Ground Fault ◽  
Ground Fault Detection

The current distribution network single-phase ground fault detection model knowledge expression is poor, its production process only based on the normal distribution network sample data, no single-phase ground fault data, did not make full use of a prior knowledge, resulting in low detection accuracy. The automatic detection model of single-phase earth fault of new distribution network is proposed. The fault characteristic vector is taken as the input vector, and the degree of matching between the input vector and the weight vector element is introduced as the second layer. The fault vector is used as the input vector, and the fault vector is used as the input vector. Node input, the second layer of the output as the third layer of the input, the model training, the output of the results of the distribution network is a single-phase ground fault detection results. The experimental results show that the proposed model has high detection accuracy. 

scholarly journals An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 807 ◽  
Author(s):  
Mingzhu Tang ◽  
Qi Zhao ◽  
Steven X. Ding ◽  
Huawei Wu ◽  
Linlin Li ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Wind Farm ◽  
Performance Indicator ◽  
Southern China ◽  
Confusion Matrix ◽  
Maximum Information ◽  
Detection Model ◽  
Low Efficiency ◽  
A Performance

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

Sign in / Sign up

Export Citation Format

Share Document