强噪声背景下基于CEEMDAN与BRECAN的船舶电机故障诊断

Marine motor fault diagnosis based on CEEMDAN and BRECAN under strong noise conditions

  • 摘要:
    目的 船舶实际航行中机舱存在的背景噪声导致故障诊断方法在实际使用时存在精度差的问题,针对上述问题,提出一种基于自适应噪声的完备经验模态分解(CEEMDAN)与贝叶斯残差高效通道注意力网络(BRECAN)的船舶电机故障诊断方法。
    方法 首先,含噪声电机故障信号通过CEEMDAN分解成多个本征模态函数(IMF)分量,并基于去趋势波动分析(DFA)划分IMF中的噪声主导信号和信息主导信号,对于噪声主导信号使用经验小波变化(EWT)进行降噪;然后,构建BRECAN网络,基于变分贝叶斯(VI-Bayesian )原理使用网络参数代替传统网络点估计训练方式,参数建模拟合噪声对模型训练的干扰,并通过残差高效通道注意力(RECA)模块引导网络提取故障差异特征;最后,通过电机故障模拟实验台,验证所提方法的有效性。
    结果 结果表明,在强噪声下所提方法能够实现船舶电机故障的精确诊断,在信噪比 SNR = - 12\; \mathrmdB条件下仍能够保持90%以上的诊断精度。
    结论 研究成果可为强噪声下船舶电机故障诊断提供参考。

     

    Abstract:
    Objective The background noise in the engine room during actual ship navigation leads to the poor accuracy in fault diagnosis methods. To address this issue, this paper proposes a ship motor fault diagnosis method based on complementary ensemble empirical mode decomposition (EEMD) with adaptive noise (CEEMDAN) and a Bayesian residual efficient channel attention network (BRECAN).
    Methods First, the noisy motor fault signal is decomposed into multiple intrinsic mode components (IMFs) through adaptive noise CEEMDAN, the noise dominant signal and information dominant signal in the IMF are divided on the basis of detrended fluctuation analysis, and empirical wavelet transform (EWT) is used to de-noise the noise dominant signal. Next, the BRECAN network is constructed, based on the principle of Variational Bayesian (VI-Bayesian ) using the network parameters instead of the traditional network point estimation training method, the parameters are built to simulate the interference of synthetic noise on the model training, and the network is guided by the Residual Efficient Channel Attention (RECA) module to extract the fault difference features. Finally, the effectiveness of the method is verified via a motor fault simulation experimental platform.
    Results The results show that the proposed method can achieve the accurate diagnosis of ship motor faults under strong noise conditions while still maintaining a diagnostic accuracy of over 90% under signal-to-noise ratio SNR = - 12\; \mathrmdB.
    Conclusion The results of this study can provide valuable references for the diagnosis of ship motor faults under strong noise conditions.

     

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