scholarly journals Detection of Node Failure in Wireless Image Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
Pabitra Mohan Khilar
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Image Sensor ◽  
High Accuracy ◽  
Sensor Nodes ◽  
Message Complexity ◽  
Wide Range ◽  
Node Architecture ◽  
Diagnosis Model ◽  
Simulation Results

A sequenced process of fault detection followed by dissemination of decision made at each node characterizes the sustained operations of a fault-tolerant wireless image sensor network (WISN). This paper presents a distributed self-fault diagnosis model for WISN where fault diagnosis is achieved by disseminating decision made at each node. Architecture of fault-tolerant wireless image sensor nodes is presented. Simulation results show that sensor nodes with hard and soft faults are identified with high accuracy for a wide range of fault rate. Both time and message complexity of the proposed algorithm are for an -node WISN.

Research of Hard Fault Diagnosis Simulation Platform of Aero-Engine's Key Sensors Based on Neural Network

2013 ◽  
Vol 391 ◽  
pp. 150-154 ◽  
Author(s):  
Zhao Rong Sun ◽  
Yi Gang Sun ◽  
Chun Lin Sun Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Simulation Platform ◽  
Bp Network ◽  
Fault Type ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Engine Failure ◽  
Aero Engines ◽  
Hard Failure

The purpose of the research is to establish a fault diagnosis model of the aero-engines key sensors using the artificial neural networks to replace the engines mathematical model, so as to establish a hard fault diagnosis simulation platform to monitor the performances of the engine sensors on real-time, to judge the engine failure mode timely, and to locate the fault type of sensors accurately. By analyzing the correlations of the parameters that affect the conditions of the engine, a three-layer BP network model is established. The related QAR (Quick Access Recorder) data are used to simulate and analyze the models using the MATLAB. Combined with the characteristics of the hard failure of the critical engine sensors and the correlation of the parameters, the fault diagnosis simulation platform is established. Then, the parameters of the normal engine and the failure engine are used respectively to evaluate and validate the platform. The simulation results show that the platform can judge the critical sensors faults of the engine accurately, and can locate the type of sensors reliably.

Sensor Fault Diagnosis Based on SOFM Neural Network

2014 ◽  
Vol 511-512 ◽  
pp. 193-196
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Accuracy ◽  
Classification Methods ◽  
Statistical Classification ◽  
Sensor Fault ◽  
Discriminant Functions ◽  
Fast Speed ◽  
Diagnosis Method ◽  
Simulation Results

Traditional sensor fault diagnosis is mainly based on statistical classification methods. The discriminant functions in these methods are extremely complex, and typical samples of reference modes are not easy to get, therefore it is difficult to meet the actual requirements of a project. In view of the deficiencies of conventional sensor fault diagnosis technologies, a fault diagnosis method based on self-organizing feature map (SOFM) neural network is presented in this paper. And it is applied to the fault diagnosis of pipeline flow sensor in a dynamic system. The simulation results show that the fault diagnosis method based on SOFM neural network has a fast speed, high accuracy and strong generalization ability, which verifies the practicality and effectiveness of the proposed method.

Coordination Fault Diagnosis Based on RBF

2013 ◽  
Vol 329 ◽  
pp. 278-282
Author(s):  
Rui Hua Xu ◽  
Zheng Zhou Wang ◽  
Ya Dong Yan ◽  
Cai Wen Ma
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
High Accuracy ◽  
Case Based Reasoning ◽  
Rule Based ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Simulation Results ◽  
Self Learning ◽  
Case Based

In large-scale complex system, The establishment of a fast, accurate fault diagnosis system is more difficult because there exist many uncertain elements between the fault cause and the fault sign .A fault diagnosis system is established based on RBF cloud neural network ,the RBR (rule-based reasoning) and the CBR (case-based reasoning).The fault diagnosis system not only has the advantages of self-learning, high accuracy, randomness, fuzziness, etc ,and has the advantages of independently of mathematical model ,rich knowledge representation, mighty problem solving ability, etc. Theoretical analysis and simulation results show that the system is feasible and effective for fast and accurate fault positioning of complex systems.

Research on Fault Diagnosis Algorithm Based on Multi-Source Data Analysis

2015 ◽  
Vol 738-739 ◽  
pp. 670-673
Author(s):  
Lin Niu ◽  
Ya Jin Li ◽  
Jin Xin Huang ◽  
Jie Zhan ◽  
Meng Chao Ma ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Noisy Data ◽  
Source Data ◽  
Operational Information ◽  
Equipment Failures ◽  
Diagnosis Algorithm ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Substation Equipment ◽  
Inspection Data

Based on multi-source substation equipment inspection data, this article achieve fault diagnosis in the case of incomplete understanding of the mechanism of substation equipment, by establishing fault diagnosis model.This article selected a substation equipment failures and operational information in different conditions as simulation study. The simulation results show the feasibility of the algorithm. Compared with the traditional fault diagnosis model, this method is more flexible , have a stronger ability to handle noisy data and good prospects.

scholarly journals Scalable Dynamic Multi-Agent Practical Byzantine Fault-Tolerant Consensus in Permissioned Blockchain

2018 ◽  
Vol 8 (10) ◽  
pp. 1919 ◽  
Author(s):  
Libo Feng ◽  
Hui Zhang ◽  
Yong Chen ◽  
Liqi Lou
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Autonomous Systems ◽  
Communication Costs ◽  
Financial Applications ◽  
Byzantine Fault ◽  
Wide Range ◽  
Multi Agent ◽  
Simulation Results ◽  
The Internet Of Things

The permissioned blockchain system has recently become popular in a wide range of scenarios, such as artificial intelligence, financial applications and the Internet of things, due to its dominance in terms of distribution, decentralization, reliability and security. However, the Practical Byzantine Fault-Tolerant (PBFT) algorithm, which is currently adopted in such systems, sparks communication bottlenecks when the number of consensus nodes increases sharply, which seriously hinders large-scale applications. In this paper, we propose a scalable dynamic multi-agent hierarchical PBFT algorithm (SDMA-PBFT), which reduces the communication costs from O(n2) to O( n × k × log k n ). Specifically, SDMA-PBFT forms multiple autonomous systems at each agent node in which message multicasting can be efficiently carried out and the internal voting results can be effectively collected. Therefore, the design of these agent nodes facilitates the in-and-out operations of consensus nodes in the blockchain system. Simulation results show that our proposed algorithm substantially outperforms the PBFT algorithm in terms of latency. Hence, it can be applied to the permissioned blockchain system effectively and efficiently.

scholarly journals Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Jian Li ◽  
Xinxin Guo ◽  
Bo Li
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Parameter Identification ◽  
Single Phase ◽  
Fault Identification ◽  
Luenberger Observer ◽  
Adaptive Parameter ◽  
Wide Range ◽  
Gradient Descent Algorithm ◽  
Simulation Results

The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range.

Development of a Wide-Range Precision Positioning Sensor Based on Image Analysis of Diffracted Light

2015 ◽  
Vol 9 (5) ◽  
pp. 588-592 ◽  
Author(s):  
Yoshihisa Uchida ◽  
Keyword(s):  
Target Position ◽  
Frequency Component ◽  
Image Sensor ◽  
Measurement Range ◽  
High Accuracy ◽  
Pi Control ◽  
Frequency Spectra ◽  
Position Sensing ◽  
Wide Range ◽  
Fft Analysis

We propose a wide-range micro- and nano-positioning sensor based on an image sensor. The intensity distribution of a Moiré signal is measured on the image sensor and used for position sensing via Fast Fourier Transform (FFT) analysis. The FFT returns the frequency spectra and phase characteristics of the image. The frequencies caused by the beam divergence, the order of diffraction, and the Moiré signal can then be identified. The sensor can detect position with high accuracy using the phase shift of the Moiré signal. The results also demonstrate a measurement range of up to 6 mm and an estimated standard deviation of 3.3 nm under specified conditions. Moreover, the target position can be set arbitrarily by automatic PI control. This positioning sensor is characterized as using only one sensor to detect position with a high accuracy over a wide measurement range, making it easy to install in existing industrial machines and tools. Moreover, the accuracy and the measurement range are selectable by choosing the appropriate frequency component.

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 971-975
Author(s):  
Yong Hou Sun ◽  
Cong Li ◽  
Mei Fa Huang ◽  
Hui Jing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Structure And Properties ◽  
Automatic Equipment ◽  
Rbf Network ◽  
The Neural Network ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

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