The Application of Particle Swarm Optimization Algorithm in the Extremum Optimization of Nonlinear Function

Liu, Jin Yue; Zhu, Bao Ling

doi:10.1109/cit.2012.74

Cited by 9 publications

(5 citation statements)

References 1 publication

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“…However, the decomposition results are also affected by the frequency bands in FMD. In later stage of PSO algorithm optimization, it will fall into local optimal solution [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Jia,

Meng,

Qin

2024

SDHM

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The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is crucial in the application of deep belief networks. The improved deep belief networks are proposed based on Hard swish activation function and the improved whale optimization algorithm. The optimized diagnosis model is applied to weak fault diagnosis of bearings. The experimental results show that this method has faster convergence speed and anti-noise performance under the interference of −5 db noise, and effectively improves the adaptive feature extraction ability of the model and the accuracy of fault diagnosis. Under different conditions, the average accuracy reached 97.8% and 97.6%, with good generalization performance.

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“…However, the decomposition results are also affected by the frequency bands in FMD. In later stage of PSO algorithm optimization, it will fall into local optimal solution [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Jia,

Meng,

Qin

2024

SDHM

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“…Yan et al [25] proposed a PSO method to optimize the decomposition number of mode and flter length of FMD, which guarantees the parametric adaptability of feature mode decomposition. However, the infuence of the number of frequency bands on the decomposition results is neglected, and the iteration of PSO is prone to premature phenomenon and falls into local optimal solution [26,27]. Mirjalili and Lewis [28] proposed WOA to observe and simulate the predatory behavior of whales in 2016.…”

Section: Introductionmentioning

confidence: 99%

Study on Fault Feature Extraction of Rolling Bearing Based on Improved WOA-FMD Algorithm

Jia

Meng

2023

Shock and Vibration

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The vibration signal of rolling bearing fault is nonlinear and nonstationary under the interference of background noise, and it is difficult to extract fault features from it. When feature mode decomposition is used to analyze signals, prior parameter settings can easily affect the decomposition results. Therefore, a fault feature extraction method based on improved whale optimization algorithm is proposed to optimize feature modal decomposition parameters. The improved WOA integrates Lévy flight and adaptive weight, and envelope entropy is used as fitness function to optimize feature modal decomposition parameters. The feature mode decomposition of the original signal is performed using the optimal combination of parameters to obtain multiple IMF components. The optimal IMF component envelope demodulation analysis is selected according to the kurtosis value, and the fault feature is extracted through the envelope spectrum. Comparing the LMWOA method with PSO and WOA methods by simulated and experimental signals, the results show that the optimization speed of LMWOA is faster than that of other methods. Compared with CEEMD, VMD, and FMD methods, the improved WOA-FMD method has higher fault feature ratio and can accurately extract fault features under noise interference. This method can effectively solve the parameter adaptive ability and improve the accuracy of fault diagnosis, which has practical significance.

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“…It searches for the optimal value by iterative search and finds the global optimal value which allows the selection of parameters to best reflect the characteristics of the entire sample space [16]. However, PSO algorithm is easy to premature and oscillate near the global optimal solution in the later period [17]. This paper introduces the improved LDW algorithm to design the inertia weight reasonably and it can effectively improve the qualities of solutions and avoid the local optimization.…”

Section: Introductionmentioning

confidence: 99%

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

2020

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The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

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The Application of Particle Swarm Optimization Algorithm in the Extremum Optimization of Nonlinear Function

Cited by 9 publications

References 1 publication

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Study on Fault Feature Extraction of Rolling Bearing Based on Improved WOA-FMD Algorithm

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

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