Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Jin, Zhihao; Chen, Guangdong; Yang, Zhengxin

doi:10.3390/e24070927

Cited by 26 publications

(18 citation statements)

References 41 publications

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“…Some of this method’s advantages are that it has fewer parameters to adjust, and its simplicity makes it easy to implement and combine with other optimization strategies. This stochastic method imitates the behavior of large populations of individuals, moving through the “hyperspace of the possible solutions”, evaluating the input data as the coordinates of their position in the objective Equation (fitness) and sharing information of the best results with each other [ 36 ].…”

Section: Numerical Methodologymentioning

confidence: 99%

Parameter Identification of Fractional Index Viscoelastic Model for Vegetable-Fiber Reinforced Composite

et al. 2022

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In the present work, parameters for adapting the behavior of the uniaxial three-element viscoelastic constitutive model with integer and fractional index derivatives to the mechanical evolution of an epoxy-composite material reinforced with long random henequen fibers, were determined. Cyclic loading–unloading with 0.1%, 0.2%, 0.3%, …, 1.0% controlled strain and staggered fluency experiments at 5 MPa, 10 MPa, and 15 MPa constant tension were performed in stages, and the obtained data were used to determine and validate the model’s parameter values. The Inverse Method of Identification was used to calculate the parameters, and the Particle Swarm Optimization (PSO) method was employed to achieve minimization of the error function. A comparison between the simulated uniaxial results and the experimental data is demonstrated graphically. There exists a strong dependence between properties of the composite and the fiber content (0 wt%, 9 wt%, 14 wt%, 22 wt%, and 28 wt% weight percentage fiber/matrix), and therefore also of the model parameter values. Both uniaxial models follow the viscoelastic behavior of the material and the fractional index version presents the best accuracy. The latter method was noted to be adequate for determination of the aforementioned constants using non-large experimental data and procedures that are easy to implement.

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Section: Numerical Methodologymentioning

confidence: 99%

Parameter Identification of Fractional Index Viscoelastic Model for Vegetable-Fiber Reinforced Composite

et al. 2022

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“…In this study, the optimization capability of particle swarm optimization (PSO) is selected to optimize the parameters of LSSVM [19][20][21] so as to further improve the model performance. The PSO algorithm updates its own speed and position after each iteration according to the optimal solution of the particle itself and the global optimal solution.…”

Section: Improved Lssvm By Particle Swarm Optimizationmentioning

confidence: 99%

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Li¹,

Zhao²,

Zhang³

et al. 2022

Agriculture

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Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

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“…Experiments have shown that the method proposed in this paper has good diagnostic performance. In addition, some other researchers have used algorithms such as the fruit fly optimization algorithm [15], the sparrow search algorithm (SSA) [16] and whale optimization (WOA) [17] to determine the optimal number of decomposition k layers. Although better results were achieved by using various optimization algorithms to determine the k-value, the optimization algorithms need to determine many parameters, such as: batching and number of iterations, and the selection of batching and number of iterations will seriously affect the decomposition efficiency of VMD [18].…”

Section: Introductionmentioning

confidence: 99%

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

et al. 2022

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The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.

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Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Cited by 26 publications

References 41 publications

Parameter Identification of Fractional Index Viscoelastic Model for Vegetable-Fiber Reinforced Composite

Parameter Identification of Fractional Index Viscoelastic Model for Vegetable-Fiber Reinforced Composite

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

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