Fault feature extraction method of gear based on optimized minimum entropy deconvolution and accugram

Zhong, Xianyou; Xiang, Gao; Mei, Quan; Huang, Tian-Wei; Zhao, Xiao

doi:10.3233/jifs-210405

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Two filters are constructed by using the boundaries of the level 1 and the highfrequency signal and the low-frequency signal are obtained after filtering. In the same way, three filters are constructed by employing the boundaries of the level 1.6 and the corresponding signals are obtained after filtering [26]. The remaining levels repeat the above steps to obtain the reconstructed signals.…”

Section: Fk Methodsmentioning

confidence: 99%

Nonlinear fast kurtogram for the extraction of gear fault features with shock interference

Ma¹,

Wang²,

Han³

et al. 2022

Meas. Sci. Technol.

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The fault feature extraction of gearbox under shock interference is an exceedingly difficult and valuable subject. Effective usage of resonance frequency band is one of the solutions for this subject. However, the existing fast kurtogram (FK) method is prone to misdiagnosis due to the sensitivity of this method to aperiodic shocks. To overcome the sensitivity of the FK method to irrelevant shock, this paper proposes a nonlinear fast kurtogram (NFK) method. First, Z-score normalization is performed on the signal. Then, Sigmoid is used to improve the fault representation under shock interference. Thirdly, the signal is divided into different frequency band, and the band with the largest kurtosis is selected for filtering. Finally, the gear fault is analyzed by the square envelope spectrum. Simulation and experimental verification are used to demonstrated the effectiveness of the proposed method. The results show that the gear fault features with shock interference can be extracted by the NFK method.

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Section: Fk Methodsmentioning

confidence: 99%

Nonlinear fast kurtogram for the extraction of gear fault features with shock interference

Ma¹,

Wang²,

Han³

et al. 2022

Meas. Sci. Technol.

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“…They showed that MED is effective in extracting fault-related impulses. Zhong et al proposed a method combining the improved MED and accugram to extract gear fault features [44]. They used MED to preprocess gear vibration signals and showed the effectiveness of MED.…”

Section: Minimum Entropy Deconvolution (Med)mentioning

confidence: 99%

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Zhang

Zhou

Wang

et al. 2022

IET Collab Intel Manufact

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Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

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“…SVM is a classification algorithm that performs nonlinear classification by a kernel method. e core idea of the SVM algorithm is to use mathematical methods to construct the optimal classification surface in the original space or the projected high-dimensional space, so that the given binary categories can be distinguished [11]. e specific procedure is as follows:…”

Section: Classification Recognition Algorithmmentioning

confidence: 99%

Sports-Assisted Education Based on a Support Vector Machine and Genetic Algorithm

2022

Mathematical Problems in Engineering

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A sports-assisted education method based on a support vector machine (SVM) is proposed to address the problem of complex and variable sports actions leading to easy ghosting of target detection and high dimensionality of feature extraction, which reduces the low accuracy of sports action recognition. The ViBe target detection algorithm is improved by using Wronskian function and the “4-linked algorithm” seed filling algorithm, which effectively solves the ghosting problem and obtains clearer human sports targets. By using the genetic algorithm to fuse the eight-star model with sports action features extracted by the Zernike moment, redundant features are reduced and differentiability between different classes is ensured. Sports action classification was achieved by using a one-to-one construction of an SVM classifier. The results show that the proposed method can effectively recognize sports movements with an average recognition accuracy of more than 96%, which can assist physical education and has a certain practical application value.

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Fault feature extraction method of gear based on optimized minimum entropy deconvolution and accugram

Cited by 4 publications

References 36 publications

Nonlinear fast kurtogram for the extraction of gear fault features with shock interference

Nonlinear fast kurtogram for the extraction of gear fault features with shock interference

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Sports-Assisted Education Based on a Support Vector Machine and Genetic Algorithm

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