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

IF 3.4 2区 物理与天体物理 Q1 ACOUSTICS
Yonghua Jiang , Zengjie Qiu , Linjie Zheng , Zhilin Dong , Weidong Jiao , Chao Tang , Jianfeng Sun , Zhongyi Xuan
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引用次数: 0

Abstract

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.
具有坐标关注的递归原型网络:一种多工况轴承故障诊断模型
在实际的工业场景中,轴承运行条件的可变性使足够数量的标记样本的收集变得复杂,从而限制了传统的基于深度学习的故障诊断方法的有效性。此外,异常样本对原型特征的影响也严重限制了原型网络在小样本故障诊断中的性能。针对上述问题,提出了一种基于元学习的递归原型网络,用于轴承多工况交叉故障诊断。首先,开发了一种具有坐标注意机制的特征提取器,能够深度提取复杂振动信号中的有效特征;此外,引入递归原型计算模块,减轻了因样本异常而产生的原型偏差,从而实现了更精确的原型表示。最后,利用度量模块获取原型与查询集样本之间的相似度,实现故障的准确分类。为了验证该方法的有效性和优越性,在两个轴承振动数据集上进行了性能评估。实验结果表明,该方法具有较高的准确率和泛化能力,明显优于其他深度学习方法,更适合于少镜头交叉条件轴承故障诊断任务。
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来源期刊
Applied Acoustics
Applied Acoustics 物理-声学
CiteScore
7.40
自引率
11.80%
发文量
618
审稿时长
7.5 months
期刊介绍: Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense. Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems. Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.
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