IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-05 DOI: 10.1007/s10489-025-06811-9

Cunli Song, Xiaomeng Yuan

{"title":"A transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions","authors":"Cunli Song, Xiaomeng Yuan","doi":"10.1007/s10489-025-06811-9","DOIUrl":"10.1007/s10489-025-06811-9","url":null,"abstract":"<div><p>Aiming at the problem that fault feature information cannot be completely extracted and it is difficult to obtain a large amount of sample data for fault labeling in real production life, we propose a transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions. First, in order to make up for the single limitation of the feature extraction of the original vibration signal, a new feature signal is formed by fusing the time domain features on the basis of the original vibration signal, which is used as the input of the model, and a lightweight one-dimensional convolutional neural network(1d-CNN) is constructed, and an efficient channel attention mechanism is introduced to extract the fault features, so as to get the source domain diagnostic model. Then, according to the idea of transfer learning, the vibration signals under different working conditions are input into the fine-tuned model to realize the rolling bearing fault diagnosis under multiple working conditions. The results show that the method can realize migration under different working conditions and accurately and efficiently realize rolling bearing fault diagnosis.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Root Cause Analysis Using Twin Attentional-Generative Adversarial and Walrus-Based Deep Coupling Recurrent Autoencoder 基于双注意生成对抗和海象深度耦合循环自编码器的无监督根本原因分析

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-09-05 DOI: 10.1002/dac.70241

T. S. Prabhakar, M. N. Veena

{"title":"Unsupervised Root Cause Analysis Using Twin Attentional-Generative Adversarial and Walrus-Based Deep Coupling Recurrent Autoencoder","authors":"T. S. Prabhakar, M. N. Veena","doi":"10.1002/dac.70241","DOIUrl":"https://doi.org/10.1002/dac.70241","url":null,"abstract":"<div>\u0000 \u0000 <p>The increasing complexity of long-term evolution (LTE) networks presents challenges in fault detection and automated diagnosis. These traditional supervised learning approaches need extensive labeled datasets, which are challenging to obtain in live network environments. In order to address this issue, this paper proposes an automatic root cause analysis model based on an unsupervised learning mechanism (ARCA-ULM) for self-healing LTE networks. The proposed ARCA-ULM adopts the twin attentional-generative adversarial network (TA-GAN) model for synthetic data generation, addressing class imbalance issues in training datasets. Then, the root cause pattern is identified using a lightweight walrus-based deep coupling gated recurrent autoencoder (WDCGRA) model, which incorporates coupling autoencoders (AEs), gated recurrent units (GRUs), and the walrus optimization algorithm (WOA) to distinguish hardware faults from software misconfigurations. The radio link control (RLC) layer optimization scheme is employed to improve network performance. The ARCA-ULM model is implemented in Python and evaluated in terms of different performance indicators. Simulation outcomes demonstrate superior performance, achieving 99.17% accuracy, a diagnosis error rate (DER) of 0.14, and an undetected error rate (UER) of 2.29, outperforming existing methods. The outcomes confirm the effectiveness of the ARCA-ULM model in enhancing the self-healing abilities of LTE networks, allowing more reliable fault detection and resolution.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 15","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Dual-Discriminator Generative Adversarial Network for Anomaly Detection 一种用于异常检测的双鉴别生成对抗网络

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-09-05 DOI: 10.1109/tnnls.2025.3585978

Da Ding, Youquan Wang, Haicheng Tao, Jia Wu, Jie Cao

引用次数: 0

Guest Editorial: Generative Foundation Models for Communications: Part 1 嘉宾评论：通信的生成基础模型：第一部分

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-09-05 DOI: 10.1109/mcom.2025.11152701

Dusit Niyato, Geng Sun, Octavia A. Dobre, Geoffrey Ye Li

引用次数: 0

Message from the Editor-in-Chief 总编辑寄语