Recursive prototypical network with coordinate attention: A model for few-shot cross-condition bearing fault diagnosis

• Published in: Applied acoustics 2025-03, Vol.231, p.110442, Article 110442
• Main Authors: Jiang, Yonghua, Qiu, Zengjie, Zheng, Linjie, Dong, Zhilin, Jiao, Weidong, Tang, Chao, Sun, Jianfeng, Xuan, Zhongyi
• Format: Article
• Language: English
• Subjects: Attention mechanism; Fault diagnosis; Few-shot cross-condition; Meta-learning; Recursive prototypical network
• ISSN: 0003-682X
• DOI: 10.1016/j.apacoust.2024.110442

•A novel LSTM-based Recursive Prototypical Network with CoordinateAttention is proposed for few-shot cross-condition bearing fault diagnosis.•A feature extractor with coordinate attention is proposed to reduce the impact of redundant information.•A LSTM-based recursive prototype computation module is developed to calculate more superior class prototypes, enhancing the distinguishability between different categories. In a practical industrial scenario, the variability in bearing operating conditions complicates the collection of a sufficient number of labeled samples, thereby limiting the effectiveness of traditional deep learning-based fault diagnosis methods. In addition, the influence of abnormal samples on the prototype features also severely limits the performance of prototypical network in few-shot fault diagnosis. To address the above issues, a recursive prototypical network based on meta-learning is proposed for few-shot cross-condition bearing fault diagnosis. Firstly, a feature extractor with coordinate attention mechanism is developed, which is able to deeply extract effective features in complex vibration signals. Furthermore, a recursive prototype computation module is introduced to alleviate prototype bias arising from abnormal samples, thereby achieving a more precise representation of prototypes. Finally, a metric module is utilized to obtain the similarity between the prototypes and the query set samples to achieve an accurate classification of faults. To verify the efficacy and superiority of the proposed method, its performance was evaluated on two bearing vibration datasets. The experimental results demonstrated that the method is significantly better than other deep learning methods with high accuracy and generalization, and greater suitability for few-shot cross-condition bearing fault diagnosis tasks.