Biography

I received the B.S. and the M.S. degrees in engineering from Chine University of Geosciences, Wuhan, China, in 2016 and 2019, respectively, and the Ph.D. degree in control engineering and science from Zhejiang University, Hangzhou, China in 2023. My major research interests include intelligent time series analysis, deep learning, data-driven industrial applications, and artificial intelligence security.

Research and Interests

• Intelligent Time Series Analysis
• Prognostics and Health Management (PHM)
• Industrial Intelligence and Industrial Embodied Intelligence
• Artificial Intelligence Security

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.}
  }

• Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, and Shirui Pan. Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46:6775-6794, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time series, and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based, contrastive-based, and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions, main frameworks, advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods, we also summarize datasets commonly used in time series forecasting, classification, anomaly detection, and clustering tasks. Finally, we present the future directions of SSL for time series analysis.

  @article{zhang2024ssl,
  title = {Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects},
  author = {Kexin Zhang and Qingsong Wen and Chaoli Zhang and Rongyao Cai and Ming Jin and Yong Liu and James Y. Zhang and Yuxuan Liang and Guansong Pang and Dongjin Song and Shirui Pan},
  year = 2024,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = 46,
  pages = {6775-6794},
  doi = {10.1109/TPAMI.2024.3387317},
  abstract = {Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time series, and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based, contrastive-based, and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions, main frameworks, advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods, we also summarize datasets commonly used in time series forecasting, classification, anomaly detection, and clustering tasks. Finally, we present the future directions of SSL for time series analysis.}
  }

• Kexin Zhang, Rongyao Cai, Chunlin Zhou, and Yong Liu. Debiased Contrastive Learning for Time-Series Representation Learning and Fault Detection. IEEE Transactions on Industrial Informatics, 20:7641-7653, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Building reliable fault detection systems through deep neural networks is an appealing topic in industrial scenarios. In these contexts, the representations extracted by neural networks on available labeled time-series data can reflect system states. However, this endeavor remains challenging due to the necessity of labeled data. Self-supervised contrastive learning (SSCL) is one of the effective approaches to deal with this challenge, but existing SSCL-based models suffer from sampling bias and representation bias problems. This article introduces a debiased contrastive learning framework for time-series data and applies it to industrial fault detection tasks. This framework first develops the multigranularity augmented view generation method to generate augmented views at different granularities. It then introduces the momentum clustering contrastive learning strategy and the expert knowledge guidance mechanism to mitigate sampling bias and representation bias, respectively. Finally, the experiments on a public bearing fault detection dataset and a widely used valve stiction detection dataset show the effectiveness of the proposed feature learning framework.

  @article{zhang2024dcl,
  title = {Debiased Contrastive Learning for Time-Series Representation Learning and Fault Detection},
  author = {Kexin Zhang and Rongyao Cai and Chunlin Zhou and Yong Liu},
  year = 2024,
  journal = {IEEE Transactions on Industrial Informatics},
  volume = 20,
  pages = {7641-7653},
  doi = {10.1109/TII.2024.3359409},
  abstract = {Building reliable fault detection systems through deep neural networks is an appealing topic in industrial scenarios. In these contexts, the representations extracted by neural networks on available labeled time-series data can reflect system states. However, this endeavor remains challenging due to the necessity of labeled data. Self-supervised contrastive learning (SSCL) is one of the effective approaches to deal with this challenge, but existing SSCL-based models suffer from sampling bias and representation bias problems. This article introduces a debiased contrastive learning framework for time-series data and applies it to industrial fault detection tasks. This framework first develops the multigranularity augmented view generation method to generate augmented views at different granularities. It then introduces the momentum clustering contrastive learning strategy and the expert knowledge guidance mechanism to mitigate sampling bias and representation bias, respectively. Finally, the experiments on a public bearing fault detection dataset and a widely used valve stiction detection dataset show the effectiveness of the proposed feature learning framework.}
  }

• 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.}
  }

• Kexin Zhang, Qingsong Wen, Chaoli Zhang, Liang Sun, and Yong Liu. Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection. In 2024 IEEE lnternational Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7065-7069, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Self-supervised learning (SSL) shows impressive performance in many tasks lacking sufficient labels. In this paper, we study SSL in time series anomaly detection (TSAD) by incorporating the characteristics of time series data. Specifically, we build an anomaly detection algorithm consisting of global pattern learning and local association learning. The global pattern learning module builds encoder and decoder to reconstruct the raw time series data to detect global anomalies. To complement the limitation of the global pattern learning that ignores local associations between anomaly points and their adjacent windows, we design a local association learning module, which leverages contrastive predictive coding (CPC) to transform the identification of anomaly points into positive pairs identification. Motivated by the observation that adjusting the distance between the history window and the time point to be detected directly impacts the detection performance in the CPC framework, we further propose a skip-step CPC scheme in the local association learning module which adjusts the distance for better construction of the positive pairs and detection results. The experimental results show that the proposed algorithm achieves superior performance on SMD and PSM datasets in comparison with 12 state-of-the-art algorithms.

  @inproceedings{zhang2024ssc,
  title = {Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection},
  author = {Kexin Zhang and Qingsong Wen and Chaoli Zhang and Liang Sun and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE lnternational Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {7065-7069},
  doi = {10.1109/ICASSP48485.2024.10447104},
  abstract = {Self-supervised learning (SSL) shows impressive performance in many tasks lacking sufficient labels. In this paper, we study SSL in time series anomaly detection (TSAD) by incorporating the characteristics of time series data. Specifically, we build an anomaly detection algorithm consisting of global pattern learning and local association learning. The global pattern learning module builds encoder and decoder to reconstruct the raw time series data to detect global anomalies. To complement the limitation of the global pattern learning that ignores local associations between anomaly points and their adjacent windows, we design a local association learning module, which leverages contrastive predictive coding (CPC) to transform the identification of anomaly points into positive pairs identification. Motivated by the observation that adjusting the distance between the history window and the time point to be detected directly impacts the detection performance in the CPC framework, we further propose a skip-step CPC scheme in the local association learning module which adjusts the distance for better construction of the positive pairs and detection results. The experimental results show that the proposed algorithm achieves superior performance on SMD and PSM datasets in comparison with 12 state-of-the-art algorithms.}
  }

• 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.}
  }

• Weiwei Liu, Wei Jing, Shanqi Liu, Yudi Ruan, Kexin Zhang, Jian Yang, and Yong Liu. Expert Demonstrations Guide Reward Decomposition for Multi-Agent Cooperation. Neural Computing and Applications, 35:19847-19863, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Humans are able to achieve good teamwork through collaboration, since the contributions of the actions from human team members are properly understood by each individual. Therefore, reasonable credit assignment is crucial for multi-agent cooperation. Although existing work uses value decomposition algorithms to mitigate the credit assignment problem, since they decompose the global value function at multi-agents’ local value function level, the overall evaluation of the value function can easily lead to approximation errors. Moreover, such strategies are vulnerable to sparse reward scenarios. In this paper, we propose to use expert demonstrations to guide the team reward decomposition at each time step, rather than value decomposition. The proposed method computes the reward ratio of each agent according to the similarity between the state-action pair of the agent and the expert demonstrations. In addition, under this setting, each agent can independently train its value function and evaluate its behavior, which makes the algorithm highly robust to team rewards. Moreover, the proposed method constrains the policy to collect data with similar distribution to the expert data during the exploration, which makes policy update more robust. We conduct extensive experiments to validate our proposed method in various MARL environments, the results show that our algorithm outperforms the state-of-the-art algorithms in most scenarios; our method is robust to various reward functions; and the trajectories by our policy is closer to that of the expert policy.

  @article{liu2023edg,
  title = {Expert Demonstrations Guide Reward Decomposition for Multi-Agent Cooperation},
  author = {Weiwei Liu and Wei Jing and Shanqi Liu and Yudi Ruan and Kexin Zhang and Jian Yang and Yong Liu},
  year = 2023,
  journal = {Neural Computing and Applications},
  volume = 35,
  pages = {19847-19863},
  doi = {10.1007/s00521-023-08785-6},
  abstract = {Humans are able to achieve good teamwork through collaboration, since the contributions of the actions from human team members are properly understood by each individual. Therefore, reasonable credit assignment is crucial for multi-agent cooperation. Although existing work uses value decomposition algorithms to mitigate the credit assignment problem, since they decompose the global value function at multi-agents' local value function level, the overall evaluation of the value function can easily lead to approximation errors. Moreover, such strategies are vulnerable to sparse reward scenarios. In this paper, we propose to use expert demonstrations to guide the team reward decomposition at each time step, rather than value decomposition. The proposed method computes the reward ratio of each agent according to the similarity between the state-action pair of the agent and the expert demonstrations. In addition, under this setting, each agent can independently train its value function and evaluate its behavior, which makes the algorithm highly robust to team rewards. Moreover, the proposed method constrains the policy to collect data with similar distribution to the expert data during the exploration, which makes policy update more robust. We conduct extensive experiments to validate our proposed method in various MARL environments, the results show that our algorithm outperforms the state-of-the-art algorithms in most scenarios; our method is robust to various reward functions; and the trajectories by our policy is closer to that of the expert policy.}
  }

• Kexin Zhang, Yong Liu, Yong Gu, Jiadong Wang, and Xiaojun Ruan. Valve Stiction Detection Using Multitimescale Feature Consistent Constraint for Time-Series Data. IEEE-ASME Transactions on Mechatronics, 28:1488-1499, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Using neural networks to build a reliable fault detection model is an attractive topic in industrial processes but remains challenging due to the lack of labeled data. We propose a feature learning approach for industrial time-series data based on self-supervised contrastive learning to tackle this challenge. The proposed approach consists of two components: data transformation and representation learning. The data transformation converts the raw time-series to temporal distance matrices capable of storing temporal and spatial information. The representation learning component uses a convolution-based encoder to encode the temporal distance matrices to embedding representations. The encoder is trained using a new constraint called multitimescale feature consistent constraint. Finally, a fault detection framework for the valve stiction detection task is developed based on the feature learning method. The proposed framework is evaluated not only on an industrial benchmark dataset but also on a hardware experimental system and real industrial environments.

  @article{zhang2023vsd,
  title = {Valve Stiction Detection Using Multitimescale Feature Consistent Constraint for Time-Series Data},
  author = {Kexin Zhang and Yong Liu and Yong Gu and Jiadong Wang and Xiaojun Ruan},
  year = 2023,
  journal = {IEEE-ASME Transactions on Mechatronics},
  volume = 28,
  pages = {1488-1499},
  doi = {10.1109/TMECH.2022.3227960},
  abstract = {Using neural networks to build a reliable fault detection model is an attractive topic in industrial processes but remains challenging due to the lack of labeled data. We propose a feature learning approach for industrial time-series data based on self-supervised contrastive learning to tackle this challenge. The proposed approach consists of two components: data transformation and representation learning. The data transformation converts the raw time-series to temporal distance matrices capable of storing temporal and spatial information. The representation learning component uses a convolution-based encoder to encode the temporal distance matrices to embedding representations. The encoder is trained using a new constraint called multitimescale feature consistent constraint. Finally, a fault detection framework for the valve stiction detection task is developed based on the feature learning method. The proposed framework is evaluated not only on an industrial benchmark dataset but also on a hardware experimental system and real industrial environments.}
  }

• Rongyao Cai, Kexin Zhang, and Yong Liu. Industrial Fault Detection Based on Time-Frequency Distillation Autoencoder. In The 42nd Chinese Control Conference (CCC), pages 5120-5125, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Data-driven feature extraction is a crucial research area in control loop performance assessment (CLPA). Deep learning is a widely used technique for building feature learning models based on neural networks (NNs). However, most NN-based CLPA methods require a large amount of labeled data and do not fully leverage the potential of frequency features. We propose a novel model called time-frequency distillation autoencoder (TFDAE) to address these limitations. The TFDAE consists of a frequency distillation encoder and a representation extraction decoder. The encoder leverages self-supervised contrastive learning to learn time features that guide the distillation of key frequency information. Additionally, a multi-kernel pooling block is incorporated in the encoder, enabling multi-scale information refinement for time feature extraction. The decoder uses the distilled information to extract informative representations and reconstruct the original input series. Taking valve stiction detection in CLPA as the evaluation task, we developed a stiction detection method based on TFDAE. Finally, We evaluate our model on the benchmark dataset: International Stiction Data Base (ISDB), and the experimental results show that TFDAE outperforms traditional knowledge-based and recent NN-based methods.

  @inproceedings{cai2023ifd,
  title = {Industrial Fault Detection Based on Time-Frequency Distillation Autoencoder},
  author = {Rongyao Cai and Kexin Zhang and Yong Liu},
  year = 2023,
  booktitle = {The 42nd Chinese Control Conference (CCC)},
  pages = {5120-5125},
  doi = {10.23919/CCC58697.2023.10239980},
  abstract = {Data-driven feature extraction is a crucial research area in control loop performance assessment (CLPA). Deep learning is a widely used technique for building feature learning models based on neural networks (NNs). However, most NN-based CLPA methods require a large amount of labeled data and do not fully leverage the potential of frequency features. We propose a novel model called time-frequency distillation autoencoder (TFDAE) to address these limitations. The TFDAE consists of a frequency distillation encoder and a representation extraction decoder. The encoder leverages self-supervised contrastive learning to learn time features that guide the distillation of key frequency information. Additionally, a multi-kernel pooling block is incorporated in the encoder, enabling multi-scale information refinement for time feature extraction. The decoder uses the distilled information to extract informative representations and reconstruct the original input series. Taking valve stiction detection in CLPA as the evaluation task, we developed a stiction detection method based on TFDAE. Finally, We evaluate our model on the benchmark dataset: International Stiction Data Base (ISDB), and the experimental results show that TFDAE outperforms traditional knowledge-based and recent NN-based methods.}
  }

• Kexin Zhang, Rongyao Cai, and Yong Liu. Industrial Fault Detection using Contrastive Representation Learning on Time-series Data. In The 22nd World Congress of the International Federation of Automatic Control (IFAC), pages 3197-3202, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Deep learning (DL) has been known as one of the effective techniques for building data-driven fault detection methods. The successful DL-based methods require the condition that massive labeled data are available, but this is sometimes an inevitable obstacle in real industrial environments. As one of the solutions, autoencoders (AEs) are widely adopted since AEs can extract features from unlabeled data. However, some challenges in AE- based fault detection methods remain, such as the design of encoder architecture, the computational cost, and the usage of the limited labeled data. This paper proposes a new industrial fault detection method through learning instance-level representation of time-series based on the self-supervised contrastive learning framework (SSCL). The proposed method uses dilated-causal-convolution-based encoder-only architecture to extract the information from industrial time- series data. A new data augmentation method for time-series data is proposed based on the temporal distance distribution, which is used to construct positive pairs in SSCL. Moreover, the encoder is alternately trained by the new weighted contrastive loss and the traditional classification loss. Finally, the experiments are conducted on the industrial data set and a semi-physical system, showing the effectiveness of the proposed method.

  @inproceedings{zhang2023ifd,
  title = {Industrial Fault Detection using Contrastive Representation Learning on Time-series Data},
  author = {Kexin Zhang and Rongyao Cai and Yong Liu},
  year = 2023,
  booktitle = {The 22nd World Congress of the International Federation of Automatic Control (IFAC)},
  pages = {3197-3202},
  doi = {10.1016/j.ifacol.2023.10.1456},
  abstract = {Deep learning (DL) has been known as one of the effective techniques for building data-driven fault detection methods. The successful DL-based methods require the condition that massive labeled data are available, but this is sometimes an inevitable obstacle in real industrial environments. As one of the solutions, autoencoders (AEs) are widely adopted since AEs can extract features from unlabeled data. However, some challenges in AE- based fault detection methods remain, such as the design of encoder architecture, the computational cost, and the usage of the limited labeled data. This paper proposes a new industrial fault detection method through learning instance-level representation of time-series based on the self-supervised contrastive learning framework (SSCL). The proposed method uses dilated-causal-convolution-based encoder-only architecture to extract the information from industrial time- series data. A new data augmentation method for time-series data is proposed based on the temporal distance distribution, which is used to construct positive pairs in SSCL. Moreover, the encoder is alternately trained by the new weighted contrastive loss and the traditional classification loss. Finally, the experiments are conducted on the industrial data set and a semi-physical system, showing the effectiveness of the proposed method.}
  }

• Kexin Zhang, Yong Liu, Yong Gu, Xiaojun Ruan, and Jiadong Wang. Multiple Timescale Feature Learning Strategy for Valve Stiction Detection Based on Convolutional Neural Network. IEEE/ASME Transactions on Mechatronics, 27(3):1478-1488, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  This article proposes a valve stiction detection strategy based on a convolutional neural network. Considering the commonly existing characteristics of industrial time-series signals, the strategy is developed to learn features on multiple timescales automatically. Unlike the traditional approaches using hand-crafted features, the proposed strategy can automatically learn representative features on the time-series data collected from industrial control loops. The strategy is composed of two complementary data conversion methods, a mixed feature learning stage and a fusion decision stage, and it has the following merits: 1) the interaction of different pairs of time series can be effectively captured; and 2) the whole process of feature learning is automatic, and no manual feature extraction is needed. The effectiveness of the proposed strategy is evaluated through the comprehensive data, including the International Stiction Data Base, and the real data collected from the real hardware experimental system and the industrial environment. Compared with four traditional methods and three deep-learning-based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we give the implementation procedure of practical application of the proposed strategy and provide the detailed analysis from the perspective of the data conversion methods and the number of timescales.

  @article{zhang2022mtf,
  title = {Multiple Timescale Feature Learning Strategy for Valve Stiction Detection Based on Convolutional Neural Network},
  author = {Kexin Zhang and Yong Liu and Yong Gu and Xiaojun Ruan and Jiadong Wang},
  year = 2022,
  journal = {IEEE/ASME Transactions on Mechatronics},
  volume = {27},
  number = {3},
  pages = {1478-1488},
  doi = {10.1109/TMECH.2021.3087503},
  abstract = {This article proposes a valve stiction detection strategy based on a convolutional neural network. Considering the commonly existing characteristics of industrial time-series signals, the strategy is developed to learn features on multiple timescales automatically. Unlike the traditional approaches using hand-crafted features, the proposed strategy can automatically learn representative features on the time-series data collected from industrial control loops. The strategy is composed of two complementary data conversion methods, a mixed feature learning stage and a fusion decision stage, and it has the following merits: 1) the interaction of different pairs of time series can be effectively captured; and 2) the whole process of feature learning is automatic, and no manual feature extraction is needed. The effectiveness of the proposed strategy is evaluated through the comprehensive data, including the International Stiction Data Base, and the real data collected from the real hardware experimental system and the industrial environment. Compared with four traditional methods and three deep-learning-based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we give the implementation procedure of practical application of the proposed strategy and provide the detailed analysis from the perspective of the data conversion methods and the number of timescales.}
  }

• Kexin Zhang and Yong Liu. Unsupervised Feature Learning with Data Augmentation for Control Valve Stiction Detection. In 2021 IEEE 10th data Driven Control And Learning Systems Conference, pages 1385-1390, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper proposes an unsupervised feature learning approach on industrial time series data for detection of valve stiction. Considering the commonly existed characteristics of industrial time series signals and the condition that sometimes massive reliable labeled-data are not available, a new time series data transformation and augmentation method is developed. The transformation stage converts the raw time series signals to 2-D matrices and the augmentation stage increases the diversity of the matrices by performing transformation on different timescales. Then a convolutional autoencoder is used to extract the representative features on the augmented data, these new features are taken as the inputs of the traditional clustering algorithms. Unlike the traditional approaches using hand-crafted features or requiring labeled-data, the proposed strategy can automatically learn features on the time series data collected from industrial control loops without supervision. The effectiveness of the pro-posed approach is evaluated through the International Stiction Data Base (ISDB). Compared with the traditional machine learning methods and deep learning based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we provide a visualization process of feature learning via principal component analy-sis.

  @inproceedings{zhang2021unsupervisedfl,
  title = {Unsupervised Feature Learning with Data Augmentation for Control Valve Stiction Detection},
  author = {Kexin Zhang and Yong Liu},
  year = 2021,
  booktitle = {2021 IEEE 10th data Driven Control And Learning Systems Conference},
  pages = {1385-1390},
  doi = {https://doi.org/10.1109/DDCLS52934.2021.9455535},
  abstract = {This paper proposes an unsupervised feature learning approach on industrial time series data for detection of valve stiction. Considering the commonly existed characteristics of industrial time series signals and the condition that sometimes massive reliable labeled-data are not available, a new time series data transformation and augmentation method is developed. The transformation stage converts the raw time series signals to 2-D matrices and the augmentation stage increases the diversity of the matrices by performing transformation on different timescales. Then a convolutional autoencoder is used to extract the representative features on the augmented data, these new features are taken as the inputs of the traditional clustering algorithms. Unlike the traditional approaches using hand-crafted features or requiring labeled-data, the proposed strategy can automatically learn features on the time series data collected from industrial control loops without supervision. The effectiveness of the pro-posed approach is evaluated through the International Stiction Data Base (ISDB). Compared with the traditional machine learning methods and deep learning based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we provide a visualization process of feature learning via principal component analy-sis.}
  }