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Abstract：
The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm\'s robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.
摘要：
旋转机械中转子部件脱落的可能性构成重大风险，需要开发早期和精确的故障诊断技术，以防止灾难性故障并降低维护成本。这项研究介绍了一种数据驱动的方法来检测转子部件脱落的开始，从而提高操作安全性和减少停机时间。利用频率分析，这项研究确定谐波振幅在转子振动数据作为即将发生的故障的关键指标。该方法采用主成分分析（PCA）来正交化并降低来自转子传感器的振动数据的维数。然后进行k折交叉验证，以选择重要特征的子集，保证检测算法的健壮性和泛化性。然后将这些特征集成到线性判别分析（LDA）模型中，作为诊断引擎，预测转子部件脱落的可能性。通过将其应用于16个工业压缩机和涡轮机，证明了该方法的有效性。证明其在提供及时的故障警告和提高运行可靠性方面的价值。
