An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

Chen, Weijia; Xiao, Yancai

doi:10.3390/a12040072

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…In the formula,

is the number of iterations of the chaotic sequence,

is the control parameter of the chaotic sequence, and the value range is

[ 39 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion

Xiao

Xue

Zhang

et al. 2021

Entropy

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Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors’ dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models.

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“…In the formula,

is the number of iterations of the chaotic sequence,

is the control parameter of the chaotic sequence, and the value range is

[ 39 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…(2) Lévy flight was introduced in the evolution strategy to improve the performance of the algorithm and achieve good results [ 39 ]. The calculation method is based on

where

is the characteristic index, which usually satisfies

.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion

Xiao

Xue

Zhang

et al. 2021

Entropy

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“…Among the bio-inspired methods, this article is focused on the ABC algorithm, which is a swarm intelligence algorithm that is widespread and inspired by the social behavior of honeybees [27]. The ABC algorithm has been widely applied in distinct fields of electrical engineering such as: in fault diagnosis [28], [29], optimized PID controller design [30], optimal power dispatching [31], automatic voltage regulator system [32], MPPT power extraction [23], [33],and antenna design [34], [35].…”

Section: Introductionmentioning

confidence: 99%

MPPT Algorithm Based on Artificial Bee Colony for PV System

et al. 2021

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Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This paper proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardwarein-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance. INDEX TERMS Maximum power point tracking, Photovoltaic system, Artificial bee colony, Hardware in the loop testing. ABBREVIATION Term Description ABC Artificial bee colony algorithm. ACO Ant colony optimization. ADE Adaptive differential evolution. ANFIS Adaptive neuro-fuzzy inference system. ANNs Artificial neural networks. BA Bat algorithm. BI Bio-inspired methods. COA Coyote optimization algorithm. DSC Digital signal controller. ESC Extremum-seeking control. FL Fuzzy logic algorithm. FPA Flower pollination algorithm. FPGA Field-programmable gate arrays. GA Genetic algorithm. HIL Hardware-in-the-loop. INC Incremental conductance algorithm. MOA Moth-flame optimization algorithm.

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“…Statistical techniques such as Linear Discriminant Analysis (LDA) (Zhao et al, 2014;Mjahed & Proriol, 1989) were the first to be exploited in this domain. Recently, artificial intelligent techniques, such as Genetic Algorithms (GA) (Mor & Gupta, 2014;Mjahed, 2006), Particle Swarm Optimization (PSO) (Rini et al, 2011;Jena et al, 2015), Artificial Bee Colony Algorithm (Chen & Xiao, 2019), Fuzzy Logic (Raj & Murali, 2013;Xiao et al, 2013) and Artificial Neural Networks (Chandra et al, 2013;Devi & Kumar, 2014) have been applied successfully to automatic detection and to diagnosis. GA and PSO algorithms have been effectively used to select the attributes of interest (Karimova et al, 2004) and for pattern detection and classification tasks (Hewahi, 2017;Mjahed, 2010).…”

Section: Introductionmentioning

confidence: 99%

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Mjahed¹,

Hadaj²,

Bouzaachane³

et al. 2020

STUD INFORM CONTROL

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This paper presents an improvement in the recognition of faulty signals, encountered in the case of the Gazelle helicopter's main rotor, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. The main focus is on the distinction between faulty and healthy signals and then between the three subclasses of faulty signals, i.e. faulty bearings, joints problem and mechanical loosening. This research work is divided into three parts. The first part approaches the two above-mentioned classes of signals at the same time, and, to this purpose, the Linear Discriminant Analysis (LDA), Non Linear Discriminant Analysis (NLDA) and Back-propagation Neural Network (BPNN) are used. In the second and third part of the paper, GA and PSO are employed for optimizing the hyperplanes and hypersurfaces which separate the above-mentioned classes of signals, as well as the architecture and connection weights of a neural network (NN). Real data are used, which correspond to the vibration signals measured during periodic technical inspections, and are characterized by amplitudes and frequencies typical of the eight highest peaks of the Welch spectrum. The results obtained confirm the validity of the above-mentioned approaches and comparable favorably with those of other multivariate methods. The GA-or PSO-based neural networks` diagnosis can therefore be established for helicopter computers so that faults can be detected.

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An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

Cited by 16 publications

References 14 publications

Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion

Misalignment Fault Diagnosis for Wind Turbines Based on Information Fusion

MPPT Algorithm Based on Artificial Bee Colony for PV System

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

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