High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

中文翻译：

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使用 CNN 进行智能状态监测以及欠采样信号的信号增强

振动和声发射等高频信号对于状态监测至关重要，但它们的高采样率对数据采集提出了挑战，尤其是在线监测。我们的研究开发了一种新方法，使用与信号增强方法集成的改进的卷积神经网络来识别欠采样信号的状态。应用频域滤波来抑制相似的边带并获得不同条件下更具辨别力的特征，然后在时域中基于插值的上采样来恢复信号长度并增强低频谐波信息。增强信号被转换为二维灰度图像以进行神经网络分析。通过对轴承数据集和再生式热氧化器提升阀泄漏的真实数据进行测试，我们的方法有效地从低频信号中提取特征，实现了超过 95% 的故障识别精度。