AI 中文总结
研究旋转机械故障诊断，提出结合度量引导的时频配置选择与特定类自动编码器的细粒度开放集故障诊断方法，在轴承数据集实验中实现高 H 分数，大幅降低配置选择成本，支持准确开放集严重程度诊断。
AI 中文摘要
旋转机械的可靠故障诊断对工业系统的安全稳定运行至关重要。深度学习方法在封闭集条件下表现良好，但实际机器可能遇到未见故障状态。现有开放集故障诊断（OSFD）方法在细粒度严重程度诊断方面受限。本文提出一种细粒度 OSFD 方法，将度量引导的数据中心（MGDC）STFT 配置选择与特定类自动编码器（CSAE）相结合。MGDC 利用从频谱图表示计算的轮廓分数筛选候选 STFT 配置。诊断模型使用一组 CSAE 学习已知退化状态的紧凑特定类流形。推理时，基于重建误差的类亲和力识别已知类，基于潜在维度边界和特定类重建误差的双标准机制拒绝未知样本。在 Case Western Reserve University（CWRU）和 Paderborn University（PU）轴承数据集上的实验表明，该方法在细粒度故障严重程度诊断中实现了 0.9924 和 0.9509 的 H 分数。MGDC 在 CWRU 上仅评估 38 个候选配置中的 9 个，在 PU 上仅评估 39 个候选配置中的 2 个，分别将选择成本降低了 5.69 倍和 29.�7 倍。结果表明该方法支持准确的开放集严重程度诊断，且配置选择成本大幅降低。
英文摘要
Reliable fault diagnosis of rotating machinery is essential for the safe and stable operation of industrial systems. Although deep learning methods perform well under closed-set conditions, real machinery may encounter previously unseen fault states. Existing open-set fault diagnosis (OSFD) methods remain limited in fine-grained severity diagnosis because they often rely on coarse type levels, heuristically selected Short-Time Fourier Transform (STFT) settings, and global class boundaries. We propose a fine-grained OSFD method that combines metric-guided data-centric (MGDC) STFT configuration selection with class-specific autoencoder (CSAE)-based anomaly rejection. MGDC screens candidate STFT configurations using the Silhouette score computed from spectrogram representations, identifying promising time-frequency representations before network training. The diagnostic model then uses a bank of CSAEs to learn compact class-specific manifolds for known degradation states. During inference, reconstruction-error-based class affinity identifies known classes, while a dual-criteria mechanism based on latent dimension-wise boundaries and class-specific reconstruction error rejects unknown samples. Experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method achieves H-scores of 0.9924 and 0.9509 for fine-grained fault severity diagnosis. MGDC also identifies the best-performing configuration found by exhaustive search while evaluating only 9 of 38 candidates on CWRU and 2 of 39 candidates on PU, reducing the selection cost by factors of 5.69 and 29.87, respectively. These results indicate that the proposed method supports accurate open-set severity diagnosis with substantially lower configuration-selection cost.
Comments42 pages, 25 figures, 14 tables
