A generalized fault diagnosis framework for rotating machinery based on phase entropy

To enhance the generalization capability of rotating machinery fault diagnosis, a novel generalized fault diagnosis framework is proposed. Phase entropy is introduced as a new method for measuring mechanical signal complexity. Furthermore, it is extended to refined time-shift multi-scale phase entropy. The extended method effectively captures dynamic characteristic information across multiple scales, providing a comprehensive reflection of the equipment's state. Based on signal amplitude, multiple time-shift multi-scale decomposition sub-signals are constructed, and a scatter diagram is generated for each sub-signal. Subsequently, the diagram is partitioned into several regions, and the distribution probability of each region is calculated, enabling the extraction of stable and easily distinguishable features through the refined operation. Next, the one-versus-one-based twin support vector machine classifier is employed to achieve high-accuracy fault identification. Case analyses of a wind turbine, an aero-engine, a train transmission system, and an aero-bearing demonstrate that the accuracy, precision, recall, and F1 score of the proposed framework are over 99.51 %, 99.52 %, 99.51 %, and 99.51 %, respectively, using only five training samples per state. The proposed framework achieves higher accuracy compared to nine existing models via deep learning or machine learning. The aforementioned analysis results validate the accuracy and generalizability of the proposed framework.