scholarly journals Discrete Wavelet Design for Bearing Fault Diagnosis Using Particle Swarm Optimization

2020 ◽  
Vol 53 (05) ◽  
pp. 705-713
Author(s):  
Jalel Khelil ◽  
Khaled Khelil ◽  
Messaoud Ramdani ◽  
Nadir Boutasseta
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Industrial Applications ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Vibration Signal Analysis ◽  
Wavelet Design

Rolling bearings are widely used in a large variety of industrial applications. Therefore, it is necessary to provide an efficient fault detection and diagnosis mechanism to prevent component failure and poor performance during operation. This paper proposes a novel classification scheme based on the design of discrete wavelets best adapted to vibration signal analysis in order to identify and properly classify rolling bearing defects. Through polyphase representation of the wavelet filter bank, and using the particle swarm optimization (PSO) algorithm, the appropriate discrete wavelet associated filters are optimized to achieve the best fault classification accuracy. Simulation results show that the proposed wavelet design approach outperforms the well-known standard wavelets regardless the employed classifier and leads to an average fault classification improvement of about 2%.

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

scholarly journals Adaptive Phase-Shifted PWM based on Particle Swarm Optimization for Cascaded H-Bridge Inverter with Unequal Dc-link Voltages

2018 ◽  
Vol 7 (4.15) ◽  
pp. 469
Author(s):  
Pakedam Lare ◽  
Byamakesh Nayak ◽  
Srikanta Dash ◽  
Jiban Ballav Sahu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Industrial Applications ◽  
High Order ◽  
Control Technique ◽  
Voltage Source ◽  
Switching Frequency ◽  
Swarm Optimization ◽  
The Cost

The cascaded H-Bridge Multilevel Inverter has been found a promising technology in industrial applications because of its higher voltage with less distortion production. Various PWMs techniques have been proposed to push the harmonics frequencies higher than the switching frequency and thus reduces the THD as compared to non-carrier control technique based upon grid frequency. The Phase-Shifted PWM technique has an advantage over others PWM techniques because its harmonics orders are multiples of switching frequency and also depend on the number of levels of the inverter. The phase shifting angle is uniform when the equal voltage sources are adopted. However, in applications where sets of different voltage source levels feed the H-Bridge cells, the Phase Shifted PWM suffers its high order harmonics elimination capability. As a solution to alleviate this problem, an adaptive variable angle approach is proposed in this paper using Particle Swarm Optimization (PSO) algorithm to eliminate desired higher order harmonics. The algorithm is used to minimize the cost function based on high order sideband harmonics elimination equations. The results through MATLAB/Simulink environment shown in this paper confirm the reduction of sideband harmonics of higher orders, and the overall THD.  

scholarly journals A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hakan Gökdağ
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Particle Swarm ◽  
Dynamic Responses ◽  
Crack Identification ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Coupled Equations ◽  
Identification Method ◽  
Bridge Type

In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.

scholarly journals Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ming-Yuan Cho ◽  
Thi Thom Hoang
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Time Domain Reflectometry ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Swarm Optimization

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

scholarly journals EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 12 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Abdullah ◽  
Norhashimah Mohd Saad ◽  
Weihown Tee
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Feature Reduction ◽  
Binary Particle Swarm Optimization ◽  
Discrete Wavelet ◽  
Feature Selection Problem ◽  
Swarm Optimization ◽  
Emg Signal

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

scholarly journals Software Module Clustering Using Grid-Based Many-Objective Particle Swarm Optimization

2021 ◽  
Author(s):  
Amarjeet Prajapati
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Poor Performance ◽  
Software Module ◽  
Swarm Optimization ◽  
Software Module Clustering ◽  
The Many ◽  
Grid Based

Abstract The poor performance of traditional multi-objective optimization algorithms over the many-objective optimization problems has led to the development of a variety of many-objective optimization algorithms. Recently, several many-objective optimization algorithms have been proposed to address the different class of many-objective optimization problems. Most of the existing many-objective optimization algorithms were designed from the perspective of synthetic many-objective optimization problems. Despite the tremendous work made in the development of the many-objective optimization algorithms for solving the synthetic many-objective optimization problems, still real-world many-objective optimization problems gained little attention. In this work, we propose a grid-based many-objective particle swarm optimization (GrMaPSO) for the many-objective software optimization problem. In this contribution, the grid-based selection strategies along with other supportive strategies such as two-archive storing and crowding distance have been exploited in the framework of particle swarm optimization. The performance of the proposed approach is evaluated and compared to three existing approaches over five problem instances. The results demonstrate that the proposed approach is more effective and has significant advantages over existing many-objective approaches designed for the software module clustering problems.

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