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

no code implementations • 26 Jun 2023 • Zixuan Wang, Bo Qin, Mengxuan Li, Chenlu Zhan, Mark D. Butala, Peng Peng, Hongwei Wang

The proposed method employs cosine similarity to identify hard samples and subsequently, leverages supervised contrastive learning to learn more discriminative representations by constructing hard sample pairs.

Contrastive Learning Representation Learning

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

no code implementations • 13 Feb 2023 • Mengxuan Li, Peng Peng, Min Wang, Hongwei Wang

The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution.

Chemical Process Clustering +1

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

no code implementations • 12 Feb 2023 • Peng Peng, Hanrong Zhang, Mengxuan Li, Gongzhuang Peng, Hongwei Wang, Weiming Shen

Finally, the model decision is biased toward the new classes due to the class imbalance.

Class Incremental Learning Incremental Learning +2

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

no code implementations • 3 Feb 2023 • Mengxuan Li, Peng Peng, Jingxin Zhang, Hongwei Wang, Weiming Shen

The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis.