Three-valued diagnosable systems: Diagnosability, optimal design and fault identification algorithm

1987 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Kaiyuan Huang
Keyword(s):  
Optimal Design ◽  
Fault Identification ◽  
Identification Algorithm

A Fault Identification Algorithm for ti-Diagnosable Systems

1986 ◽  
Vol C-35 (6) ◽  
pp. 503-510 ◽  
Author(s):  
Che-Liang Yang ◽  
Gerald M. Masson
Keyword(s):  
Fault Identification ◽  
Identification Algorithm

Fault Identification Algorithm of Power Grid Based on Wide Area Information and PNN

2015 ◽  
Vol 738-739 ◽  
pp. 382-390
Author(s):  
Hao Wu ◽  
Qun Zhan Li ◽  
Wei Liu
Keyword(s):  
Fault Tolerance ◽  
Feature Vector ◽  
Power Grid ◽  
High Accuracy ◽  
Wide Area ◽  
Fault Identification ◽  
Identification Algorithm ◽  
Simulation Experiments ◽  
Definition Of ◽  
Grid Based

With the help of wide area information, a new fault identification algorithm of power grid based on PNN is proposed. This algorithm gives a definition of the line associated domain, the elements’ action information of the line associated domain gathered by line IEDs can form the feature vector into PNN classifier, and then the fault elements of power grid would be identified on PNN classifier. Through a large number of simulation experiments, it shows that the new fault identification algorithm of power grid based on PNN and wide area information has high accuracy and good fault tolerance.

A Fast Fault-Identification Algorithm Tailored for Optical Interconnection Networks in High-Performance Computers

2014 ◽  
Vol 936 ◽  
pp. 2307-2312
Author(s):  
He Li
Keyword(s):  
Interconnection Networks ◽  
Decomposition Method ◽  
High Performance ◽  
Fault Identification ◽  
Optical Interconnection ◽  
Identification Algorithm ◽  
Cycle Decomposition ◽  
Diagnosis Algorithm ◽  
High Performance Computers

Due to integrated positive features of both hypercube and tori, optical multi-mesh hypercube (OMMH) networks in high-performance computers are regarded as a class of promising optical inter-connection networks. This paper firstly derive that the diagnosability of OMMH under the pessimistic strategy is (2n+6)/(2n+6), which shows that the OMMH possesses strong self-diagnosingability. With the improved cycle decomposition method by Yang in J. Parall. Distrib. Comput. [10], a fast diagnosis algorithm to identify all faulty nodes tailored for OMMH, which runs in O(Nlog2N) time is also proposed, where N is the number of the processors of an OMMH.

scholarly journals A novel wavelet packet transform based fault identification procedures in HV transmission line based on current signals

2019 ◽  
Vol 8 (1) ◽  
pp. 11
Author(s):  
Ahmed R. Adly ◽  
Ragab El Sehiemy ◽  
Mahmoud A. Elsadd ◽  
Almoataz Y. Abdelaziz
Keyword(s):  
Transmission Lines ◽  
Power Transmission ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Fault Identification ◽  
Identification Algorithm ◽  
Power Transmission Lines ◽  
Load Variations ◽  
System Configurations

<p>This paper presents an adaptive fault identification algorithm bases on wavelet packet transform (WPT) for two-terminal power transmission lines. The proposed scheme performs four functions which are the fault detection, fault classification, distinguishing among the temporary and the permanent faults, and detection of the arc extinguish instant. The presented algorithm only uses the measured current at one terminal reducing the required cost. Also, it can mitigate the error resulting from the load variations via updating the presetting value. Consequently, it does not need retesting under changing the transmission system configurations. The proposed scheme is deduced in the spectral domain and depended on the application of the WPT. The db6 wavelet packet is used for decomposing the faulty phase current waveform (level 7) to get the energy coefficients. The presented algorithm is assessed under various fault conditions such as fault distances, inception angles, and faults nature via simulating different secondary arc models via using ATP/EMTP. The obtained results are investigated and evaluated.</p>

FLOSPAT: Fault Localization by Sensitized Path Transformation

1996 ◽  
Author(s):  
M.W. Heath ◽  
W. Maly
Keyword(s):  
Boolean Functions ◽  
Fault Localization ◽  
Fault Model ◽  
Sequential Circuits ◽  
Fault Identification ◽  
Identification Algorithm ◽  
Path Tracing ◽  
Full Scan ◽  
Diagnostic Resolution ◽  
Test Vectors

Abstract This paper describes a fault identification algorithm for combinational and full-scan sequential circuits called FLOSPAT - Fault Localization by Sensitized Path Transformation [1,2]. The goal of fault identification is to localize a fault to the fewest possible gates and to determine the Boolean functions realized by those gates. Instead of choosing a fault model, FLOSPAT uses fault-independent sensitized path tracing [3] to localize functional deviations. Sensitized path transformation is used to adaptively generate test vectors which improve the diagnostic resolution. The output of FLOSPAT is used for physical defect diagnosis by cross-referencing gate-level defect dictionaries generated by the contamination-defect-fault mapper CODEF [4,5,6].

A parallel fault identification algorithm

1990 ◽  
Vol 11 (2) ◽  
pp. 231-241 ◽  
Author(s):  
E Schmeichel ◽  
S.L Hakimi ◽  
M Otsuka ◽  
G Sullivan
Keyword(s):  
Fault Identification ◽  
Identification Algorithm

Performance Comparison Between Fast Fourier Transform-Based Segmentation, Feature Selection, and Fault Identification Algorithm and Neural Network for the Condition Monitoring of Centrifugal Equipment

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Samer Gowid ◽  
Roger Dixon ◽  
Saud Ghani
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Selection ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Condition Monitoring ◽  
Identification Accuracy ◽  
Fault Identification ◽  
Identification Algorithm ◽  
Centrifugal Equipment

This paper compares and evaluates the performance of two major feature selection and fault identification methods utilized for the condition monitoring (CM) of centrifugal equipment, namely fast Fourier transform (FFT)-based segmentation, feature selection, and fault identification (FS2FI) algorithm and neural network (NN). Multilayer perceptron (MLP) is the most commonly used NN model for fault pattern recognition. Feature selection and trending play an important role in pattern recognition and hence affect the performance of CM systems. The technical and developmental challenges of both methods were investigated experimentally on a Paxton industrial centrifugal air blower system with a rotational speed of 15,650 RPMs. Five different machine conditions were experimentally emulated in the laboratory. A low training-to-testing ratio of 50% was utilized to evaluate the performance of both methods. In order to maximize fault identification accuracy and minimize computing time and cost, a near-optimal NN configuration was identified. The results showed that both techniques operated with a fault identification accuracy of 100%. However, the FS2FI algorithm showed a number of advantages over NN. These advantages include the ease of implementation and a reduction of cost and time in development and computing, as it processed the data from the first trial in less than 6.2% of the time taken by the NN.

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