Biography

I have obtained my bachelor’s degree in Physics from Nanjing University. I am pursuing M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research lies at process control, data mining, and deep learning, currently focusing on the study of processing the times series data.

Research and Interests

• Data Mining
• Process Control

Publications

• Rongyao Cai, Wang Gao, Linpeng Peng, Zhengming Lu, Kexin Zhang, and Yong Liu. Debiased Contrastive Learning With Supervision Guidance for Industrial Fault Detection. IEEE Transactions on Industrial Informatics, 2024.
  [BibTeX] [Abstract] [DOI]

  The time series self-supervised contrastive learning framework has succeeded significantly in industrial fault detection scenarios. It typically consists of pretraining on abundant unlabeled data and fine-tuning on limited annotated data. However, the two-phase framework faces three challenges: Sampling bias, task-agnostic representation issue, and angular-centricity issue. These challenges hinder further development in industrial applications. This article introduces a debiased contrastive learning with supervision guidance (DCLSG) framework and applies it to industrial fault detection tasks. First, DCLSG employs channel augmentation to integrate temporal and frequency domain information. Pseudolabels based on momentum clustering operation are assigned to extracted representations, thereby mitigating the sampling bias raised by the selection of positive pairs. Second, the generated supervisory signal guides the pretraining phase, tackling the task-agnostic representation issue. Third, the angular-centricity issue is addressed using the proposed Gaussian distance metric measuring the radial distribution of representations. The experiments conducted on three industrial datasets (ISDB, CWRU, and practical datasets) validate the superior performance of the DCLSG compared to other fault detection methods.

  @article{cai2024dcl,
  title = {Debiased Contrastive Learning With Supervision Guidance for Industrial Fault Detection},
  author = {Rongyao Cai and Wang Gao and Linpeng Peng and Zhengming Lu and Kexin Zhang and Yong Liu},
  year = 2024,
  journal = {IEEE Transactions on Industrial Informatics},
  doi = {10.1109/TII.2024.3424561},
  abstract = {The time series self-supervised contrastive learning framework has succeeded significantly in industrial fault detection scenarios. It typically consists of pretraining on abundant unlabeled data and fine-tuning on limited annotated data. However, the two-phase framework faces three challenges: Sampling bias, task-agnostic representation issue, and angular-centricity issue. These challenges hinder further development in industrial applications. This article introduces a debiased contrastive learning with supervision guidance (DCLSG) framework and applies it to industrial fault detection tasks. First, DCLSG employs channel augmentation to integrate temporal and frequency domain information. Pseudolabels based on momentum clustering operation are assigned to extracted representations, thereby mitigating the sampling bias raised by the selection of positive pairs. Second, the generated supervisory signal guides the pretraining phase, tackling the task-agnostic representation issue. Third, the angular-centricity issue is addressed using the proposed Gaussian distance metric measuring the radial distribution of representations. The experiments conducted on three industrial datasets (ISDB, CWRU, and practical datasets) validate the superior performance of the DCLSG compared to other fault detection methods.}
  }

• Rongyao Cai, Xiao Xv, Zhengming Lu, Kexin Zhang, and Yong Liu. Fusion Assessment of Safety and Security for Intelligent Industrial Unmanned Systems. In 7th International Symposium on Autonomous Systems (ISAS), 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Fault tree analysis is the most commonly used methodology in industrial safety analysis to predict the probability or frequency of system failure. Although fault tree analysis has been proposed for more than six decades, the assumptions used in most commercial fault tree analysis codes have not changed significantly, which limits the ability of the method to represent design, operation, and maintenance characteristics in the context of the increasing complexity and specialization of modern industrial systems. The basic setup of traditional fault trees is unable to include dependencies between events, time-varying failures, and repair rate realities to explain complex maintenance strategies. To address the above shortcomings, we propose a fusion tree model combining fault tree and attack tree, and simplify the causal structure of the fusion tree by modularization, and utilize the dynamic Markov model to represent the complex coupling relationship between components or nodes. Finally, we demonstrate the calculation process of fusion tree in pressure vessel systems with temporal control.

  @inproceedings{cai2024fas,
  title = {Fusion Assessment of Safety and Security for Intelligent Industrial Unmanned Systems},
  author = {Rongyao Cai and Xiao Xv and Zhengming Lu and Kexin Zhang and Yong Liu},
  year = 2024,
  booktitle = {7th International Symposium on Autonomous Systems (ISAS)},
  doi = {10.1109/ISAS61044.2024.10552597},
  abstract = {Fault tree analysis is the most commonly used methodology in industrial safety analysis to predict the probability or frequency of system failure. Although fault tree analysis has been proposed for more than six decades, the assumptions used in most commercial fault tree analysis codes have not changed significantly, which limits the ability of the method to represent design, operation, and maintenance characteristics in the context of the increasing complexity and specialization of modern industrial systems. The basic setup of traditional fault trees is unable to include dependencies between events, time-varying failures, and repair rate realities to explain complex maintenance strategies. To address the above shortcomings, we propose a fusion tree model combining fault tree and attack tree, and simplify the causal structure of the fusion tree by modularization, and utilize the dynamic Markov model to represent the complex coupling relationship between components or nodes. Finally, we demonstrate the calculation process of fusion tree in pressure vessel systems with temporal control.}
  }

• Rongyao Cai, Linpeng Peng, Zhengming Lu, Kexin Zhang, and Yong Liu. DCS: Debiased Contrastive Learning with Weak Supervision for Time Series Classification. In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 5625-5629, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Self-supervised contrastive learning (SSCL) has performed excellently on time series classification tasks. Most SSCL-based classification algorithms generate positive and negative samples in the time or frequency domains, focusing on mining similarities between them. However, two issues are not well addressed in the SSCL framework: the sampling bias and the task-agnostic representation problems. Sampling bias indicates fake negative sample selection in SSCL, and task-agnostic representation results in the unknown correlation between the extracted feature and downstream tasks. To address the issues, we propose Debiased Contrastive learning with weak Supervision framework, abbreviated as DCS. It employs the clustering operation to remove fake negative samples and introduces weak supervisory signals into the SSCL framework to guide feature extraction. Additionally, we propose a channel augmentation method that allows the DCS to extract features from local and global perspectives simultaneously. The comprehensive experiments on the widely used datasets show that DCS achieves performance superior to state-of-the-art methods on the widely used popular benchmark datasets.

  @inproceedings{cai2024dcs,
  title = {DCS: Debiased Contrastive Learning with Weak Supervision for Time Series Classification},
  author = {Rongyao Cai and Linpeng Peng and Zhengming Lu and Kexin Zhang and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  pages = {5625-5629},
  doi = {10.1109/ICASSP48485.2024.10446381},
  abstract = {Self-supervised contrastive learning (SSCL) has performed excellently on time series classification tasks. Most SSCL-based classification algorithms generate positive and negative samples in the time or frequency domains, focusing on mining similarities between them. However, two issues are not well addressed in the SSCL framework: the sampling bias and the task-agnostic representation problems. Sampling bias indicates fake negative sample selection in SSCL, and task-agnostic representation results in the unknown correlation between the extracted feature and downstream tasks. To address the issues, we propose Debiased Contrastive learning with weak Supervision framework, abbreviated as DCS. It employs the clustering operation to remove fake negative samples and introduces weak supervisory signals into the SSCL framework to guide feature extraction. Additionally, we propose a channel augmentation method that allows the DCS to extract features from local and global perspectives simultaneously. The comprehensive experiments on the widely used datasets show that DCS achieves performance superior to state-of-the-art methods on the widely used popular benchmark datasets.}
  }