基于随机 LSTM 块映射特征提取的 旋转机械故障诊断方法.

In light of the complexity of failure mechanisms in rotating machinery and the significant differences in feature information, traditional diagnostic models often rely on prior knowledge, leading to low accuracy and poor adaptability. We propose a rotating machinery fault diagnosis method named "Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network" (RQ-RLBM-SCN). To address the challenges posed by limited feature information in failing machinery and insufficient training samples, we employ random quantization data augmentation to expand the original data samples from multiple sensors. This enhances the model's adaptability, accuracy and mitigating overfitting. Next, we employ a random LSTM block mapping method to extract features, solving the difficulty of extracting temporal data features in stochastic configuration network (SCN). Sub-sequently, we utilize a stochastic configuration network (SCN) for classification. SCN dynamically configures parameters without the need for backpropagation to update parameters, ensuring a proper learning rate while effectively avoiding issues such as gradient explosion or vanishing. Research results indicate that the RQ-RLBM-SCN method can accurately identify bearing and gear faults. In 10 repeated experiments, the average accuracy on the multi-sensor dataset for bearings and gears reaches 99.80% and 98.75% respectively which are higher than the original SCN, TSC-SCN, VMD-SCN, SVM and KNN fault diagnosis methods. This approach provides a dynamic method and diagnostic insight for establishing a health monitoring model for rotating machinery. [ABSTRACT FROM AUTHOR]