Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Zhang, Qianqian; Pan, Haochi; Qing, Fan; Xu, Fujing; Wu, Yanpeng

doi:10.1155/2021/8552024

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…The six sigma robust design optimization objective function contains the mean and standard deviation of the optimization objective, integrates the mean and standard deviation of the optimization objective through the weight coefficients, and transforms the deterministic constraints in the optimization model into probabilistic constraints. In practical engineering applications, high reliability can also be achieved by bringing the performance of the product design variables to the 2-3 sigma level (Zhang et al, 2020). The robust optimization model and the deterministic optimization model of the motor hanger are displayed in Eq.…”

Section: Flower Pollination Algorithm and Dual Krigingmentioning

confidence: 99%

A novel robust design optimization method combining improved flower pollination algorithm and dual Kriging

Zhang,

Li,

Wang

et al. 2023

MMMS

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PurposeThis study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.Design/methodology/approachThe method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.FindingsThe DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.Originality/valueThis study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

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Section: Flower Pollination Algorithm and Dual Krigingmentioning

confidence: 99%

A novel robust design optimization method combining improved flower pollination algorithm and dual Kriging

Zhang,

Li,

Wang

et al. 2023

MMMS

View full text Add to dashboard Cite

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“…An optimal linear operator A is estimated to implement the mapping from X to Y as in equation (18). Clearly, for linear systems, the eigenvalues of A can represent the evolution from X to Y.…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

“…To extract the repeated pulses of bearing fault signal effectively, methods for rotating machinery fault diagnosis using fault cycle information have been proposed. Representative methods include minimum entropy deconvolution (MED) [15], maximum correlated kurtosis deconvolution (MCKD) [16], multipoint optimal MED adjusted (MOMEDA) [17], and blind deconvolution (BD) based on cyclostationarity maximization [18]. These four methods use kurtosis, correlation kurtosis, multiple D-norms (MDNs), and cyclic smoothness index as objective functions, ensuring that hidden periodic pulses in the measured signal can be enhanced effectively.…”

Section: Introductionmentioning

confidence: 99%

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Xiong¹,

Lv²,

Dang³

et al. 2022

Meas. Sci. Technol.

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Fault vibration signals of rolling bearings in early stages are affected by complex transmission paths and strong background noise, resulting in weak information about fault characteristics, which is difficult to extract clearly and accurately. To this end, a new diagnosis method for early faults of rolling bearings is proposed. First, the parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted (PA-MOMEDA) algorithm is used to preprocess the fault signals by strengthening their shock components and weakening the influence of noise on their results. Second, the maximum envelope-spectrum characteristic energy ratio is employed as the selection criterion for the optimal truncation order of dynamic mode decomposition (DMD) to decompose and reconstruct the signals. Finally, the processed signals are subjected to the Hilbert envelope spectral transformation to accurately extract early fault characteristic frequencies. An analysis of simulated signals, public database signals, and bearing signals from a wind turbine has shown that the proposed PA-MOMEDA–DMD method can successfully extract the early fault characteristics of rolling bearings. Compared with the traditional pattern decomposition algorithms, the proposed method is much better at extracting fault characteristics and diagnosing early faults of rolling bearings. The facts have proved that the proposed method is promising in engineering applications.

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“…Additionally, the CYCBD method, introduced by BUZZONI et al [20] in 2018, as a second-order cyclic stationary blind deconvolution technique, has demonstrated exceptional performance in complex background noise scenarios. It has been applied to signal filtering and envelope demodulation analysis in fault diagnosis [21,22], enhancing the effectiveness of fault feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Yang

2024

Symmetry

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In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method’s noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise.

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Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Cited by 7 publications

References 28 publications

A novel robust design optimization method combining improved flower pollination algorithm and dual Kriging

A novel robust design optimization method combining improved flower pollination algorithm and dual Kriging

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

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