A DeepWalk Graph Embedding-Enhanced Extreme Learning Machine Method for Online Gearbox Fault Diagnosis

Deep learning has become a popular topic among scholars and has attracted widespread attention. However, deep learning methods typically require large datasets to determine model parameters and can only process data in batches. To address the challenges of deep learning models, which rely on batch data and struggle to adapt to industrial streaming data scenarios in gearbox fault diagnosis, this study proposes an online gearbox fault diagnosis method based on a DeepWalk graph embedding-enhanced extreme learning machine (ELM) approach. The method constructs a graph structure in real time for each newly collected vibration signal, uses DeepWalk for unsupervised embedding learning, and extracts low-dimensional features with strong discriminative power. These features are then input into the ELM classifier to achieve adaptive fault type recognition and online incremental model updates. This method does not require historical data to be retrained, thus effectively overcoming the bottleneck of batch retraining and significantly improving diagnostic efficiency and resource utilization. The experimental results show that, under various operating conditions, the proposed method achieves fast and accurate diagnosis of multiple gearbox fault types, with an average accuracy consistently above 95%, thereby demonstrating excellent engineering applicability and real-time performance.