Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan.
Department of Electrical and Electronic Engineering, Thu Dau Mot University, Thu Dau Mot 75000, Binh Duong, Vietnam.
Sensors (Basel). 2022 Jun 22;22(13):4705. doi: 10.3390/s22134705.
This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of -4 dB.
本研究提出了一种新的基于改进快速峭度图、Gramian 角场和卷积神经网络的非线性模态分解的智能诊断方法，用于检测旋转机械的轴承状态。基于改进快速峭度图的非线性模态分解继承了原始算法的优点，同时提高了计算效率和信噪比。Gramian 角场可以在不破坏信号时间关系的情况下构建二维图像。因此，所提出的方法可以在复杂的工作条件下对旋转机械进行故障诊断。所提出的方法在 Paderborn 数据集上进行了重噪声和多种工作条件下的验证，以评估其有效性。实验结果表明，所提出的模型在该数据集提供的所有四种工作条件下的准确率均超过 99.6%，在信噪比为-4dB 的强噪声环境下的准确率为 93.8%。
