Research on Fault Diagnosis of Diesel Engine Based on PSO-SVM

Zhang, Zhongxing; Guo, Hongyan

doi:10.2991/978-94-6239-145-1_48

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…It is widely used in data optimization and data mining. This research adopts the PSO to search and optimize the kernel parameters and penalty factors of the SVM model instead of the traditional parameter optimization method [74]. By constructing the PSO-SVM prediction model, the learning ability and prediction effect of the safety risk-prediction model of metro construction are improved.…”

Section: Parameter Optimization Of Svm Model Based On Particle Swarm mentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

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Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

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Section: Parameter Optimization Of Svm Model Based On Particle Swarm mentioning

confidence: 99%

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Liu

Xie

Bian

et al. 2020

IJERPH

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“…Therefore, methods can be used to improve the selection of parameters of particles, introduce factors such as multitargeting, constraints, or combine chaos search and PSO [10,11]. Some scholars have also proposed PSO-based hybrid optimization algorithms, such as PSO-SVM, PSO-ANN [12]. In terms of application, PSO algorithms have been widely used in engineering optimization, machine learning and other fields [13].…”

Section: Pso Algorithmmentioning

confidence: 99%

Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

Lu,

Wang,

Wang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The research on optimization algorithms has kept pace with the times, and one of the hot spots that has attracted much attention is the improvement of Particle Swarm Optimization algorithm (PSO) in the field of swarm intelligence. In this paper, a hybrid PSO algorithm using Adaptive Learning strategy (ALPSO) is introduced, and a hybrid PSO based on Arena Competitive Learning strategy (ACLPSO) is proposed from the perspective of search space and candidate particle generation, effectively enriches population diversity, expands the initial particle swarm search space, and improves convergence speed and accuracy.

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“…Particle swarm optimization is a swarm intelligence optimization algorithm, proposed by Eberhart and Kennedy in 1995 [27]. The feasible solution space is firstly set and a certain scale of particle swarm are initialized in PSO algorithm, then a set of possible solutions can be represented by the coordinate vector of each particle.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

Xiao

Hong

Xiu-hai

et al. 2017

Entropy

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Misalignment is one of the common faults for the doubly-fed wind turbine (DFWT), and the normal operation of the unit will be greatly affected under this state. Because it is difficult to obtain a large number of misaligned fault samples of wind turbines in practice, ADAMS and MATLAB are used to simulate the various misalignment conditions of the wind turbine transmission system to obtain the corresponding stator current in this paper. Then, the dual-tree complex wavelet transform is used to decompose and reconstruct the characteristic signal, and the dual-tree complex wavelet energy entropy is obtained from the reconstructed coefficients to form the feature vector of the fault diagnosis. Support vector machine is used as classifier and particle swarm optimization is used to optimize the relevant parameters of support vector machine (SVM) to improve its classification performance. The results show that the method proposed in this paper can effectively and accurately classify the misalignment of the transmission system of the wind turbine and improve the reliability of the fault diagnosis.

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Research on Fault Diagnosis of Diesel Engine Based on PSO-SVM

Cited by 9 publications

References 6 publications

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

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