关键词: HHT 算法, 呼吸机运行状态, 小波神经网络, EMD 分解, Hilbert 谱变换, MLP 神经网络分类器

Abstract: In order to ensure the normal operation of the ventilator, an intelligent monitoring method for the operating status of the ventilator based on the HHT(Hilbert-Huang Transform) algorithm is proposed. Firstly,wavelet neural network is used to denoise the running signal of the ventilator; Secondly, combined with the HHT algorithm, the denoised ventilator operation signal is decomposed by EMD(Empirical Mode Decomposition), and the decomposed IMF( Intrinsic Mode Functions) component is transformed by Hilbert spectrum to obtain the signal spectrum as the signal feature. Finally, the obtained signal spectrum is placed in the MLP neural network
classifier, and the backpropagation algorithm is used to train the MLP neural network to achieve recognition of the operating status of the ventilator. The experimental results show that the proposed method has a good denoising effect, and the monitored results are consistent with the actual spectrum. At the same time, the sensitivity of monitoring is above 96% , and the accuracy of operating status recognition is above 95% . This indicates that the proposed method can effectively monitor the operating status of the ventilator and has good monitoring performance.

Key words: Hilbert-Huang transform(HHT) algorithm, the operating status of the ventilator, wavelet neural network, empirical mode decompostion ( EMD), Hilbert spectral transformation, multi-layer perceptron(MLP) neural network classifier