Supervised Self-Encoder-Based Feature Learning Study for Fault Diagnosis of Variable Operating Condition Rotor Blade Systems

When a steam turbine blade has cracks, fractures, or other flaws, the steam turbine’s operating circumstances will change the vibration characteristics of the blades, complicating the problem identification process. The important defect features are difficult to automatically and effectively extract from the recorded vibration signals. In this study, the input signal characteristics for a particular operating situation are used as labels to reconstruct a trained autoencoder utilizing a reverse error. The supervised autoencoder receives the fault features for various speed circumstances, which it then protectively maps to a series of reference condition features. The goal is to eliminate the disruption brought on by variations in fault feature values brought on by alterations in operating circumstances. The experimental findings demonstrate that this approach can more effectively convert feature sequences under various working situations and address the issue of fault feature distortion brought on by changes in working conditions. In addition, comparison of clustering visualization and accuracy of classification methods on data before and after commutation demonstrates that the proposed supervised autoencoder model can extract accurate classifiable features for fault classification.