Zhang Sikai, Lin Qizhe, Lin Jiayao
Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325027, China.
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325027, China.
Sensors (Basel). 2024 Jun 25;24(13):4123. doi: 10.3390/s24134123.
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.
旋转机械中转子部件脱落的可能性带来了重大风险，因此需要开发一种早期精确的故障诊断技术，以防止灾难性故障并降低维护成本。本研究引入了一种数据驱动的方法，在转子部件脱落开始时进行检测，从而提高运行安全性并最大限度地减少停机时间。利用频率分析，本研究将转子振动数据中的谐波幅度识别为即将发生故障的关键指标。该方法采用主成分分析（PCA）对来自转子传感器的振动数据进行正交化和降维，然后通过k折交叉验证选择重要特征的子集，确保检测算法的鲁棒性和通用性。然后将这些特征集成到线性判别分析（LDA）模型中，该模型作为诊断引擎来预测转子部件脱落的概率。通过将该方法应用于16台工业压缩机和涡轮机，证明了该方法的有效性，证明了其在提供及时故障预警和提高运行可靠性方面的价值。
