.png){kind=icon}
返回
Incrementally Generative Adversarial Diagnostics Using Few-Shot Enabled One-Class Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2024.3416945.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In real-world industrial scenarios, fault diagnosis often relies on a significant volume of normal data to detect faults with a few coming samples. The limited sample nature of newly introduced faults can pose challenges for deep learning-based fault diagnosis models. To address this issue, a novel framework named incrementally generative adversarial diagnostics was developed by using few-shot enabled one-class learning (FSEOCL) for effective anomaly detection and fault classification of the new-coming few samples. In the addressed method, a bi-directional generative adversarial network is first trained using only normal data to acquire an encoder for latent representation from time-series data. A few samples of each fault condition in latent space can be then identified and classified correctly through FSEOCL. The effectiveness of the proposed framework is demonstrated through fault diagnosis experiments conducted on both a benchmark bearing and an industrial robot. The results underscore the adaptability of this framework to industrial fault diagnosis scenarios, highlighting its ability to achieve accurate anomaly detection and fault classification compared to state-of-the-art peer methods.
Keyword:
Anomaly detection
Generators
Proposals
Kernel
Fault detection
Bidirectional generative adversarial network
fault diagnosis
few-shot learning
incremental learning
one-class learning
incremental learning
one-class learning
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor基于集成sae-dbn和二进制处理器的无监督旋转机械故障诊断方法
.png){kind=icon}
PREAI
分享收藏Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application基于少数聚类的不平衡分类综合少数过采样技术在风电机组故障检测中的应用
PREAI
分享收藏Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study使用凯斯西储大学数据进行滚动轴承诊断: 基准研究
PREAI
分享收藏A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis一种融合判别信息的轴承故障诊断深度自动编码器方法
PREAI
分享收藏
查看更多内容