Intelligent Evaluation Model for Cementing Quality Based on PSO-SVM and Application

Liu, Jing Cheng; Wang, Hong Tu; Zeng, Shun; Yuan, Zhengwu

doi:10.4028/www.scientific.net/amm.71-78.4293

Cited by 6 publications

(4 citation statements)

References 6 publications

(8 reference statements)

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“…Since a particle swarm algorithm not only has a fast convergence speed, it also contains less parameters to set, and so has good applicability for the parameter optimization of the SVM. Through cooperation and competition among individuals, the complex solution space is optimized to obtain the optimal solution of the problem [32][33][34].…”

Section: Pso-based Svm Parameter Optimizationmentioning

confidence: 99%

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

Bie,

Shu,

Lyu

et al. 2024

Sensors

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As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations.

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Section: Pso-based Svm Parameter Optimizationmentioning

confidence: 99%

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

Bie,

Shu,

Lyu

et al. 2024

Sensors

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“…Determining the kernel parameter g and the penalty factor c in the kernel function is a key step in the SVM, and usually, these two parameters are determined by empirical or grid search methods, but neither of these methods could ensure the global optimal solution [24]. In contrast, the particle swarm algorithm can primarily initialize a set of random particles and then find the optimal solution by certain iterations [25]. e particle adjusts its speed and position during the iterative process based on its own and its companion's dispersion experience.…”

Section: Power Spectrum Entropymentioning

confidence: 99%

An Improved CEEMDAN Time‐Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

Bie

Miao

Lyu

et al. 2021

Shock and Vibration

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As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time-domain energy entropy-based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time-domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO-SVM (particle swarm optimization-support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time-domain energy entropy and PSO-SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.

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“…SVM [7][8][9] is based on VC dimension and structural risk minimization principle of the statistical learning theory its basic idea is to map the input vector into a high-dimensional feature space via certain pre-selected nonlinear mapping to construct an optimal separation hyperplane in this space.…”

Section: The Principle Of Svmmentioning

confidence: 99%

“…Many scholars have carried out in-depth research and achieved preferable results using the above methods.The methods mentioned above have their own advantages. However, they have their own limitations as well.The intelligent evaluation model for cementing quality of PSO-SVM is proposed, and the effectiveness of this model has been proved using engineering practices [7].Then,combining the SVM and GA by fully utilizing the characteristic of the unique superiority of SVM in dealing with the problem of classified learning in small set of samples as well as the characteristic of global optimization of parallel search by using GA for intelligent for cementing quality evaluation.…”

mentioning

confidence: 99%

Intelligent Evaluation Model for Cementing Quality Based on GA-SVM and Application

Liu

Zeng

Yuan

2011

AMM

Self Cite

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The cementing quality is directly related to the normal operation of the gas well, therefore, the evaluation of cementing quality is key to the correctly use the gas well as well as to take measures to protect the gas well. In this paper, four first wave amplitudes at the same depth point when using the borehole compensated sonic logger with double transceiver technique to carry out the acoustic amplitude log operation are served as the discriminant factors to evaluate the cementing quality. Taking the engineering actual measured data as the learning samples and using the particle swarm optimization to optimize the parameters of support vector machine, this paper established the intelligent evaluation model for cementing quality based on genetic algorithm (GA) and support vector machine (SVM). The model employs the excellent characteristic of SVM which has high speed of solving and could describe nonlinear relation as well as the characteristic of GA which has global optimization. Through test of engineering samples, the research result showed that this model has fast astringency and high precision, providing a new method and approach for the fast and accurate evaluation of the well cementing quality.

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Intelligent Evaluation Model for Cementing Quality Based on PSO-SVM and Application

Cited by 6 publications

References 6 publications

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

An Improved CEEMDAN Time‐Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

Intelligent Evaluation Model for Cementing Quality Based on GA-SVM and Application

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