基于MPDCNN的强噪声环境下船舶电力推进器齿轮箱故障诊断方法

Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments

  • 摘要: 【目的】针对旋转机械在实际工作过程中存在的噪声干扰而导致的故障诊断性能下降的问题,通过提高影响故障诊断性能的两个重要因素,即从振动信号提取的故障特征质量和故障诊断模型能力,提出了基于mel-frequency倒谱系数(MFCC)的并行双通道卷积神经网络(PDCNN)故障诊断新方法。【方法】该方法通过提取更加有效的故障特征,降低了噪声对监测信号的影响。并在此基础上,设计了一种新的PDCNN,将提取出来的故障特征分别通过接受不同模态的并行双通道卷积网络,从而进一步提高在强噪声环境下的诊断性能。【结果】通过试验评估了该故障诊断方法在不同噪声环境下的可行性,其中所提方法在强噪声环境下的故障诊断精度高于98%,性能明显优于其他方法。【结论】实验结果表明,该方法具有较强的抗噪性能和较好的诊断性能。

     

    Abstract: Objectives In response to the issue of deteriorated fault diagnosis performance in rotating machinery due to noise interference during actual operation, a novel fault diagnosis approach based on Mel-frequency cepstral coefficients (MFCC) and Parallel Dual-Channel Convolutional Neural Network (PDCNN) is proposed. This approach aims to improve two crucial factors influencing fault diagnosis performance: the quality of fault features extracted from vibration signals and the capability of fault diagnosis models. Methods This method mitigates the impact of noise on the monitored signals by extracting more effective fault features. Building upon this, a novel PDCNN is designed to enhance the fault diagnosis performance in a high-noise environment. The extracted fault features are fed into parallel dual-channel convolutional networks that are designed to accept different modes of features. This further improves the diagnostic performance in the presence of strong noise. Results The feasibility of the fault diagnosis method was evaluated through experiments in different noise environments, where the proposed method achieved a fault diagnosis accuracy of over 98% in high noise environments, demonstrating a performance significantly superior to other methods.Conclusions The experimental results demonstrate that this method exhibits strong noise robustness and excellent diagnostic performance.

     

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