基于Transformer的汽轮机滚动轴承早期微弱故障检测方法研究

为了应对滚动轴承早期微弱故障的挑战本文提出了一种新的方法。该方法首先采用PCA（主成分分析）对振动信号进行特征筛选，以降低数据维度，有效地简化了振动数据的结构，增强了特征的表达力。接着，使用CEEMDAN（完全自适应噪声集合经验模态分解）算法来分解被背景噪声干扰的微弱故障振动信号，它通过在经验模态分解（EMD）的基础上引入自适应噪声，增强了对微弱特征的识别能力，有够效地分离出趋势和噪声数据，显著提高了故障诊断的准确性。最后，引入Transformer模型，进一步优化了特征的提取和表征，实现对长序列数据的高效处理，用于微弱故障特征的提取和表征。这一综合方法具有降维、噪声抑制和长序列处理等多重优势，有望在滚动轴承故障检测中取得显著成果。

Research on Transformer Based Early Weak Fault Detection Method for Steam Turbine Rolling Bearings

In order to meet the challenge of early weak failure of rolling bearings, a new method is proposed. In this method, PCA (principal component analysis) is used to screen the features of the vibration signal to reduce the data dimension, effectively simplify the structure of the vibration data, and enhance the expressiveness of the features. Then, the CEEMDAN (Fully Adaptive Noise Set Empirical Mode Decomposition) algorithm is used to decompose the weak fault vibration signals disturbed by background noise, which enhances the ability to identify weak features by introducing adaptive noise on the basis of Empirical Mode Decomposition (EMD), effectively separates the trend and noise data, and significantly improves the accuracy of fault diagnosis. Finally, the Transformer model is introduced to further optimize the extraction and representation of features, and realize the efficient processing of long sequence data for the extraction and characterization of weak fault features. This comprehensive method has multiple advantages such as dimensionality reduction, noise suppression and long sequence processing, and is expected to achieve remarkable results in rolling bearing fault detection.