Gu Yikuan, Wang Yan, Li Zhong, Zhang Tiantian, Li Yuanhao, Wang Guodong, Cao Huiliang
School of Software, North University of China, Taiyuan 030051, China.
Shanxi Software Engineering Technology Research Center, Taiyuan 030051, China.
Micromachines (Basel). 2023 Jun 23;14(7):1287. doi: 10.3390/mi14071287.
In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.
本文提出了一种将信号处理算法与机器学习算法相结合的故障识别算法，利用四质量振动微机电系统陀螺仪（FMVMG）进行信号采集工作，构建陀螺仪故障数据集，并基于该数据集执行模型训练任务。将改进的经验小波变换（EWT）算法与SEResNeXt-50相结合，减少了信号中白噪声对识别任务的影响，显著提高了故障识别的准确率。EWT算法是一种具有自适应小波分析的小波分析算法，能显著降低边界效应的影响，对短长度信号段的分解效果良好，但需要一种重构方法来有效分离噪声信号和有效信号，因此本文采用多尺度排列熵进行计算。由于神经网络具有更好的高维信号表征能力，将一维信号重构为二维图像信号并提取信号特征。然后，将构建的图像信号输入SEResNeXt-50网络，通过添加挤压激励（Squeeze-and-Excitation）模块在网络中进一步提高模型的表征能力。最后，将所提模型应用于FMVMG故障数据集，并与其他模型进行比较。在识别准确率方面，所提方法比BP神经网络提高了约30.25%，比ResNeXt-50提高了约1.85%，证明了所提方法的有效性。
