Time-frequency self-similarity enhancement network and its application in wind turbines fault analysis

作者：Dezun Zhao, Depei Shao, Tianyang Wang, Lingli Cui · 发表于：Advanced Engineering Informatics · 年份：2025 · DOI：10.1016/j.aei.2025.103322 · 被引用次数：37 · 研究领域：Machine Fault Diagnosis Techniques、Advanced Computational Techniques and Applications、Engineering Diagnostics and Reliability

The data-driven time–frequency analysis (TFA) method has garnered widespread attention due to its robust feature learning and representation capabilities. However, existing methods still require further development in characterizing nonstationary signals with closely-spaced and crossing frequencies generated from wind turbines , and realizing mechanical fault detection. To this end, a novel method, termed time–frequency self-similarity enhancement network (TFSSEN), is proposed. First, an adaptive time–frequency characterizing module (ATFCM), consisting of the time–frequency convolutional layer and adaptive convolutional pooling unit, is designed to represent random scale vibration signals to an appropriate scale time–frequency representation (TFR). Second, a non-local and global attention residual group (NGARG) is constructed, where a single-scale self-similarity exploitation module is introduced to calculate feature correlations within single-scale TFR, and an improved-global context attention mechanism is developed to explore the most informative components in multi-scale time–frequency features, thereby achieving precise feature reconstruction. Finally, the self-similarity mixed-scale time–frequency enhancement module (SMTEM) is constructed by multiple cascaded NGARGs, and it can extract frequency information from similar time–frequency features and gradually enhance energy concentration. Simulation results show that the TFSSEN can effectively characterize nonstationary si...