AESGRU: An Attention-Based Temporal Correlation Approach for End-to-End Machine Health Perception

Abstract

Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

“…Shi et al [52] presented a fault diagnosis framework based on SDAE and LSTM, which can effectively detect initial anomalies of rolling bearing and accurately describe the deterioration trend. To improve the diagnostic accuracy, Zhang et al [142] presented an attention-based equitable segmentation gated recurrent unit network, which consists of an equitable segmentation approach and an improved deep model.…”