The Modeling Method of a Vibrating Screen Efficiency Prediction Based on KPCA and LS-SVM

Chen, Bingsan; Huang, Dicheng; Zhang, Fujiang

doi:10.1142/s0218001419500095

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…KPCA is a kernel function improvement based on the PCA. KPCA overcomes the disadvantages of PCA, which can process linear data, and combines the kernel function to process nonlinear features, allowing it to deeply mine the features of nonlinear data [24,25]. The main steps of feature mining are as follows:…”

Section: Kpcamentioning

confidence: 99%

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Zhu

Liu

et al. 2022

Electronics

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As vital equipment in high-speed train power supply systems, the failure of onboard traction transformers affect the safe and stable operation of the trains. To diagnose faults in onboard traction transformers, this paper proposes a hybrid optimization method based on quickly and accurately using support vector machines (SVMs) as fault diagnosis systems for onboard traction transformers, which can accurately locate and analyze faults. Considering the limitations of traditional transformers for identifying faults, this study used kernel principal component analysis (KPCA) to analyze the feature quantity of dissolved gas analysis (DGA) data, electrical test data, and oil quality test data. The improved seagull optimization algorithm (ISOA) was used to optimize the SVM, and a Henon chaotic map was introduced to initialize the population. Combined with differential evolution (DE) based on the adaptive formula, the foraging formula of the seagull optimization algorithm (SOA) was improved to increase the diversity of the algorithm and enhance its ability to find the optimal parameters of SVM, which made the simulation results more accurate. Finally, the KPCA–ADESOA–SVM model was constructed and applied to fault diagnosis for the traction transformer. The example analysis compared the diagnosis results of the proposed diagnosis model with those of the traditional diagnosis model, showing further optimization of the feature quantity and improvements in the diagnosis accuracy. This proves that the proposed diagnosis model has high generalization performance and can effectively increase the fault diagnosis accuracy and speed of traction transformers.

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Section: Kpcamentioning

confidence: 99%

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Zhu

Liu

et al. 2022

Electronics

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“…The novel application of non-linear regression modeling with support vector machines (SVMs) was used to map the sample space of the operating parameters and vibrating screen configuration by Li [28]. The nonlinear principal component of the vibration signal was extracted, and a machine-learning model was constructed using LS-SVM, which reduced the AR coefficient and improved the learning ability and speed of the model [29]. In addition, a hybrid MACO-GBDT algorithm based on ant colony optimization (ACO) was also proposed to optimize the sieving performance of the vibrating screen by Chen [30].…”

Section: Introductionmentioning

confidence: 99%

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

et al. 2023

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The optimization of screening parameters will directly improve the screening performance of vibration screens, which has been a concern of the industry. In this work, the discrete element model of wet sand and gravel particles is established, and the vibration screening process is simulated using the discrete element method (DEM). The screening efficiency and time are used as evaluation indices, and the screening parameters including amplitude, vibration frequency, vibration direction angle, screen surface inclination, the long and short half-axis ratio of the track, feeding rate, and screen surface length are investigated. The results of an orthogonal experiment and range analysis show that the amplitude, screen surface inclination, and vibration frequency are significant factors affecting screening performance. Then, the support vector regression optimized with the grey wolf optimizer (GWO-SVR) algorithm is used to model the screening data. The screening model with excellent learning and prediction ability is obtained with the Gaussian kernel function setting. Moreover, the GWO-SVR algorithm is used to optimize the screening parameters, and the screening parameters with optimal screening efficiency and time are obtained. Furthermore, the effectiveness and reliability of the optimized model are verified using the discrete element calculation. The optimization strategy proposed in this work could provide guidance for the structural design of vibration screens and screening process optimization.

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“…Data-driven FD recognizes the fault according to the fault features found from a large volume of historical data [ 20 , 21 , 22 ]. This method has low modeling dependency and has attracted much interest from researchers [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven FD recognizes the fault according to the fault features found from a large volume of historical data [20][21][22]. This method has low modeling dependency and has attracted much interest from researchers [23][24][25]. Data-driven methods have achieved high performance in aircraft FD applications and have focused on aircraft actuator FD [26], aircraft sensors FD [27], aero engine systems FD [28,29], and fault data analysis [30,31].…”

Section: Introductionmentioning

confidence: 99%

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao

Wei

Zhou

et al. 2020

Sensors

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In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

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The Modeling Method of a Vibrating Screen Efficiency Prediction Based on KPCA and LS-SVM

Cited by 4 publications

References 20 publications

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

A Hybrid Method for the Fault Diagnosis of Onboard Traction Transformers

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

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