基于特征提取的石油机械设备故障自动诊断方法

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中图分类号：TH165.3 文献标志码：B 文章编号：1671-5276（2025）05-0186-05

Abstract：A feature extraction basedautomatic fault diagnosis method for petroleum machinery equipment is proposed to addressthe issuelow acuracyinfault diagnosiscaused by noise interference fromon一site environmentsand facilities inthe vibrationsignal petroleummachineryequipment faults.Wavelet packet analysis isused todecompose and denoise the vibration signals petroleum machinery equipment faults.The EDM analysis method based on KPCA is applied to extract the denoisedfaulttype features，and thefault typefeature set isobtained.Combined with thefeature set，an ELM fault diagnosis model is constructed to achieve automatic diagnosis petroleum machinery equipment faults.The experimental results show thatthe proposed method hasa significant denoising effecton fault vibration signals，and the automatic fault diagnosis method，lessafected by noise interference，has high recognition rate and accuracy.

Keywords：feature extraction；petroleum machinery；wavelet packet analysis；EDM analysis；fault diagnosis

0 引言

为了保障石油机械设备的稳定运行，实时对石油机械设备故障展开自动诊断是非常重要的。（剩余5708字）