Ball screw is widely used in the engineering field, and accurate estimation of their state is crucial for the reliability of system operation. However, existing methods often overlook the time series characteristics and spatial correlation of vibration signals, unable to provide complete degradation information and divide the degradation process, resulting in limited prediction accuracy. Therefore, a state estimation method for ball screw based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Networks (LSTM) is proposed. An experiment of ball screw transmission equipment was conducted to collect vibration signals throughout the entire life cycle and verify the proposed method. Firstly, the frequency domain amplitude signal of the transformed ball screw is normalized to eliminate scale differences, which serves as the input for CNN feature extraction. Then, these deep features are input into the LSTM network to capture the fault evolution patterns that reveal the degradation of ball screw performance, and achieve accurate estimation of ball screw state. The final prediction accuracy was 97.87%, verifying the effectiveness of the proposed method.

中文翻译：

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一种基于CNN-LSTM的滚珠丝杠状态估计方法

滚珠丝杠在工程领域中应用广泛，准确估计其状态对于系统运行的可靠性至关重要。然而，现有方法往往忽视振动信号的时间序列特征和空间相关性，无法提供完整的退化信息并划分退化过程，导致预测精度有限。因此，提出一种基于卷积神经网络（CNN）和长短期记忆神经网络（LSTM）的滚珠丝杠状态估计方法。通过滚珠丝杠传动设备的实验，采集整个生命周期的振动信号，并对所提出的方法进行验证。首先，对变换后的滚珠丝杠的频域幅度信号进行归一化，消除尺度差异，作为CNN特征提取的输入。然后，将这些深层特征输入到 LSTM 网络中，捕获揭示滚珠丝杠性能退化的故障演化模式，实现滚珠丝杠状态的准确估计。最终预测准确率为97.87%，验证了所提方法的有效性。