Výsledky vyhledávání - "Li, Mengxuan"

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TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection

Autor: Li, Mengxuan, Liu, Ke, Chen, Hongyang, Bu, Jiajun, Wang, Hongwei, Wang, Haishuai

Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this task, which learn point-wise representation via unsupervised learning.

Externí odkaz: http://arxiv.org/abs/2411.11641

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Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis

Autor: Wang, Zixuan, Qin, Bo, Li, Mengxuan, Zhan, Chenlu, Butala, Mark D., Peng, Peng, Wang, Hongwei

The efficient utilization of wind power by wind turbines relies on the ability of their pitch systems to adjust blade pitch angles in response to varying wind speeds. However, the presence of multiple health conditions in the pitch system due to the

Externí odkaz: http://arxiv.org/abs/2306.14701

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An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis

Autor: Li, Mengxuan, Peng, Peng, Wang, Min, Wang, Hongwei

Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fau

Externí odkaz: http://arxiv.org/abs/2302.06243

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SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data

Autor: Peng, Peng, Zhang, Hanrong, Li, Mengxuan, Peng, Gongzhuang, Wang, Hongwei, Shen, Weiming

Intelligent fault diagnosis has made extraordinary advancements currently. Nonetheless, few works tackle class-incremental learning for fault diagnosis under limited fault data, i.e., imbalanced and long-tailed fault diagnosis, which brings about var

Externí odkaz: http://arxiv.org/abs/2302.05929

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SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

Autor: Li, Mengxuan, Peng, Peng, Zhang, Jingxin, Wang, Hongwei, Shen, Weiming

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field

Externí odkaz: http://arxiv.org/abs/2302.01599

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