Reliability Growth Modeling for Software Fault Detection Using Particle Swarm Optimization

Deep data-mining methods of fault signal in large-scale communication system are researched. Although with the characteristic of frequency uniformity as signals distribute in each reaction zone, common method of fault signal detection based on shortwave dispersing is invalid employing in large-scale communication system, which presents the absence or instability of fault signal. For this reason, a method based on particle swarm optimization is proposed for fault signal detection in large-scale communication system. As reaction speed and activity scope within the whole particle swarm are replaced, accurate results are achieved. Taking particle swarm optimization, it is detected that whether there is a fault in communication systems. The experimental results show that proposed method in signal fault detection process can greatly increase accuracy of signal fault detection, as plays a greater role in future.

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Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-017-0333-y ◽

2017 ◽

Vol 92 (5-8) ◽

pp. 2311-2324 ◽

Cited By ~ 1

Author(s):

Jesús Alejandro Navarro-Acosta ◽

Edgar O. Reséndiz-Flores

Keyword(s):

Feature Selection ◽

Particle Swarm Optimization ◽

Fault Detection ◽

Particle Swarm ◽

Support Vector ◽

Support Vector Data Description ◽

Binary Particle Swarm Optimization ◽

Vector Data ◽

Swarm Optimization ◽

Data Description

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Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes1113 ◽

2008 ◽

Vol 223 (3) ◽

pp. 607-615 ◽

Cited By ~ 7

Author(s):

A Nikranjbar ◽

M Ebrahimi ◽

A S Wood

Keyword(s):

Particle Swarm Optimization ◽

Fault Detection ◽

Induction Motors ◽

Particle Swarm ◽

Detection Algorithm ◽

Fault Detection And Diagnosis ◽

Adaptive Synchronization ◽

Swarm Optimization ◽

Model Based ◽

Model Free

Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.

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