鉴别器
人工智能
计算机科学
特征提取
模式识别(心理学)
断层(地质)
人工神经网络
过度拟合
分类器(UML)
电信
探测器
地震学
地质学
作者
Hao Wu,Jimeng Li,Qingyu Zhang,Jinxin Tao,Zong Meng
标识
DOI:10.1016/j.isatra.2022.04.026
摘要
As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize the adversarial learning of the feature extractor and domain discriminator to extract the domain-invariant features, thus realizing fault identification of rolling bearings. In the cross-domain diagnosis of rolling bearing faults, how to obtain fault-related discriminative domain-invariant features from the noisy signals is a key to improving the diagnostic result. In response to this, this paper proposes an intelligent diagnosis model based on the DANN and attention mechanism to identify rolling bearing faults. In order to relieve the influence of noisy data on feature extraction and improve the quality of the learned features, the ensemble empirical mode decomposition (EEMD) is first adopted to denoise the raw sample data to weaken the influence of noise on feature extraction. Secondly, a feature extractor composed of three feature extraction modules in series is designed, and each feature extraction module is composed of a convolution layer, an attention mechanism module and a pooling layer. The feature extractor with attention mechanism enables the model to learn and retain key features related to the faults during training process. Meanwhile, the global average pooling layer is used to replace some fully connected layers in the fault classifier and domain discriminator to reduce model parameters and avoid model overfitting. Finally, the analysis using two sets of rolling bearing experimental about the performance of the presented method show that the proposed method has the potential to become a promising tool for the fault diagnosis of rolling bearings.
科研通智能强力驱动
Strongly Powered by AbleSci AI