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Journal of Zhejiang University SCIENCE A
ISSN 1673-565X(Print), 1862-1775(Online), Monthly
2023 Vol.24 No.5 P.404-418
Fault diagnosis for gearboxes based on Fourier decomposition method and resonance demodulation
Abstract: Condition monitoring and fault diagnosis of gearboxes play an important role in the maintenance of mechanical systems. The vibration signal of gearboxes is characterized by complex spectral structure and strong time variability, which brings challenges to fault feature extraction. To address this issue, a new demodulation technique, based on the Fourier decomposition method and resonance demodulation, is proposed to extract fault-related information. First, the Fourier decomposition method decomposes the vibration signal into Fourier intrinsic band functions (FIBFs) adaptively in the frequency domain. Then, the original signal is segmented into short-time vectors to construct double-row matrices and the maximum singular value ratio method is employed to estimate the resonance frequency. Then, the resonance frequency is used as a criterion to guide the selection of the most relevant FIBF for demodulation analysis. Finally, for the optimal FIBF, envelope demodulation is conducted to identify the fault characteristic frequency. The main contributions are that the proposed method describes how to obtain the resonance frequency effectively and how to select the optimal FIBF after decomposition in order to extract the fault characteristic frequency. Both numerical and experimental studies are conducted to investigate the performance of the proposed method. It is demonstrated that the proposed method can effectively demodulate the fault information from the original signal.
Key words: Fourier decomposition method; Singular value ratio; Resonance frequency; Envelope demodulation; Fault diagnosis
1南方医科大学法医学学院，广州市法医多组学精准鉴定重点实验室，中国广州市，510515
2安徽医科大学基础医学院，中国合肥市，230031
3南方医科大学珠江医院检验医学科微生物组医学中心，中国广州市，510515
摘要：体液组织来源的鉴定可为刑事案件的侦查提供线索和证据。为了建立一种高效的法医学体液鉴定方法，本研究选取了8个新的体液特异性DNA甲基化标志物，并基于这些标志物构建了可用于5种常见体液（静脉血、唾液、经血、阴道液和精液）鉴定的多重单碱基延伸反应（SNaPshot）体系。结果表明，该系统具有良好的物种特异性和灵敏度，可用于混合生物样本的鉴定。同时，本研究利用前期研究数据构建了一个人工体液预测模型和两个分别基于支持向量机和随机森林算法的机器学习预测模型，并利用本研究获得的检测数据（n=95）对这些预测模型进行了测试。基于研究者经验建立的人工预测模型的准确率为95.79%，支持向量机预测模型对除唾液（96.84%）外的所有体液的预测准确率均为100.00%，随机森林预测模型对5种体液的预测准确率均为100.00%。综上所述，我们所构建的SNaPshot系统和随机森林预测模型能够实现体液组织来源的准确鉴定。
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DOI:
10.1631/jzus.A2200555
CLC number:
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On-line Access:
2023-04-25
Received:
2022-11-23
Revision Accepted:
2023-02-16
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2023-04-25