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Xiaohang Jin - IEEE Xplore Author Profile
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Xiaohang Jin

Publications
36
Citations
1,449
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (62)
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Xiaohang Jin

Affiliation

College of Mechanical Engineering
Zhejiang University of Technology
Hangzhou, China

Publication Topics

Biography

Xiaohang Jin (Senior Member, IEEE) received the Ph.D. degree in electronic engineering from the City University of Hong Kong, Hong Kong, China, in 2014. Since 2014, he has been with the Zhejiang University of Technology, Hangzhou, China, where he is currently an Associate Professor with the College of Mechanical Engineering. He is also a Visiting Scholar with the State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China. His research interests include intelligent manufacturing, condition monitoring, prognostics, and health management.(Based on document published on 10 June 2024).
Publications
36
Citations
1,449
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (62)
Author's Published Works

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Knowledge Graph Metric Learning Network for Few-Shot Health Status Assessment

Gang Xiao;Ying Cao;Jiacheng Huang;Xiaohang Jin;Yuanming Zhang

IEEE Sensors Journal
Year: 2025 | Volume: 25, Issue: 2 | Journal Article |
Health status assessment plays an important role in guaranteeing equipment operating safety and reducing maintenance cost. Traditional deep learning (DL) methods require large amounts of labeled samples for training, making them ineffective in most practical scenarios with few-shot labeled samples. To address this challenge, we propose a novel knowledge graph metric learning network (KGMLN) for fe...Show More

Knowledge Graph Metric Learning Network for Few-Shot Health Status Assessment

Gang Xiao;Ying Cao;Jiacheng Huang;Xiaohang Jin;Yuanming Zhang

Year: 2025 | Volume: 25, Issue: 2 | Journal Article |

Remaining Useful Life Prediction of Radioisotope Thermoelectric Generator Using a Component-Level Mechanism-Based Nonlinear Additive Wiener Process

Zixuan He;Ziqian Kong;Xiaohang Jin;Zijun Que;Zhengguo Xu

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Accurate prediction of the remaining useful life (RUL) of a general-purpose heat source radioisotope thermoelectric generator (GPHS-RTG) is critical for mission scheduling and power allocation in spacecraft. The key challenges in this task are long-term predictability and uncertainty quantification due to the limited data and variations in load conditions. However, little reported literature can b...Show More

Remaining Useful Life Prediction of Radioisotope Thermoelectric Generator Using a Component-Level Mechanism-Based Nonlinear Additive Wiener Process

Zixuan He;Ziqian Kong;Xiaohang Jin;Zijun Que;Zhengguo Xu

Year: 2024 | Volume: 73 | Journal Article |

Online Health Monitoring and Incipient Fault Detection for Large Wind Turbine Based on a Data-Driven Method

Xusheng Hu;Xiaohang Jin;Xiao Yang;Xiaodong Xing;Xiaoxia Zhang;Zihao Kang

2024 Prognostics and System Health Management Conference (PHM)
Year: 2024 | Conference Paper |
Faults can cause a reduction or complete interruption in the wind power generation of wind turbines, making it crucial to detect faults at its early stage to allow for timely maintenance and replacement decisions, and to prevent significant economic losses. However, many fault detection methods that rely on analyzing Supervisory Control and Data Acquisition (SCADA) data are not sensitive enough to...Show More

Online Health Monitoring and Incipient Fault Detection for Large Wind Turbine Based on a Data-Driven Method

Xusheng Hu;Xiaohang Jin;Xiao Yang;Xiaodong Xing;Xiaoxia Zhang;Zihao Kang

Year: 2024 | Conference Paper |

Incremental Learning Method for Wind Turbine Fault Detection Models Considering False Negatives

Ziqi Wang;Xiaohang Jin;Zhengguo Xu

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (1)
Fault detection (FD) algorithms based on supervisory control and data acquisition (SCADA) data have been widely used in the operation and maintenance (O&M) of wind turbines (WTs). However, the performance of FD models will degrade due to the time-varying operating conditions (TVOC). Incremental learning (IL) methods can be employed to update the models online to adapt to TVOC, but the error accumu...Show More

Incremental Learning Method for Wind Turbine Fault Detection Models Considering False Negatives

Ziqi Wang;Xiaohang Jin;Zhengguo Xu

Year: 2024 | Volume: 73 | Journal Article |

A Guided Multiobjective Optimization Method for Attenuation Calibration in Distributed Fiber Sensing

Jiamu Ling;Chenli Shi;Feng Wang;Wei Ye;Xiaohang Jin;Zhuangwei Xu;Zhengguo Xu

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Optical fibers serve as both signal transmission channels and sensing units in distributed fiber sensing systems (DFSs). The sensor’s detection signal may be adversely affected by multiple forms of attenuation due to unavoidable bending and splices during fiber installation. Ensuring signal reliability requires precise calibration of signal attenuation. The attenuation calibration of multiple meas...Show More

A Guided Multiobjective Optimization Method for Attenuation Calibration in Distributed Fiber Sensing

Jiamu Ling;Chenli Shi;Feng Wang;Wei Ye;Xiaohang Jin;Zhuangwei Xu;Zhengguo Xu

Year: 2024 | Volume: 73 | Journal Article |

Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine

Xiaohang Jin;Shengye Lv;Ziqian Kong;Hongchun Yang;Yuanming Zhang;Yuanjing Guo;Zhengguo Xu

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 4 | Journal Article |
Condition monitoring of wind turbines (WTs) is essential for advancing wind energy. Existing data-driven methods heavily rely on deep learning and big data, leading to challenges in distinguishing true faults from false alarms, impacting operational decisions negatively. Thus, this paper proposes a spatio-temporal graph neural network framework that incorporates prior knowledge. Prior WT knowledge...Show More

Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine

Xiaohang Jin;Shengye Lv;Ziqian Kong;Hongchun Yang;Yuanming Zhang;Yuanjing Guo;Zhengguo Xu

Year: 2024 | Volume: 15, Issue: 4 | Journal Article |

Spatio-Temporal Propagation: An Extended Message-Passing Graph Neural Network for Remaining Useful Life Prediction

Ziqian Kong;Xiaohang Jin;Feng Wang;Zhengguo Xu

IEEE Sensors Journal
Year: 2024 | Volume: 24, Issue: 20 | Journal Article |
The deep learning (DL)-based method for predicting remaining useful life (RUL) has gained lots of attention in the industrial equipment sector. Due to the complexity of modern industrial equipment and the necessity of monitoring multivariate time-series data to obtain comprehensive health information, DL models with spatio-temporal feature extraction have been developed to achieve accurate RUL pre...Show More

Spatio-Temporal Propagation: An Extended Message-Passing Graph Neural Network for Remaining Useful Life Prediction

Ziqian Kong;Xiaohang Jin;Feng Wang;Zhengguo Xu

Year: 2024 | Volume: 24, Issue: 20 | Journal Article |

Improved Autoencoder Model With Memory Module for Anomaly Detection

Wei Huang;Zhen Liu;Xiaohang Jin;Jinshan Xu;Xinwei Yao

IEEE Sensors Journal
Year: 2024 | Volume: 24, Issue: 8 | Journal Article |
Cited by: Papers (1)
As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In addition, it is well known that the autoencoder model has over-prominent reconstruction ability for anomalous data, leading to high false-negative rate. On the other hand, the deep support vector data descripti...Show More

Improved Autoencoder Model With Memory Module for Anomaly Detection

Wei Huang;Zhen Liu;Xiaohang Jin;Jinshan Xu;Xinwei Yao

Year: 2024 | Volume: 24, Issue: 8 | Journal Article |

Current-Aided Vibration Fusion Network for Fault Diagnosis in Electromechanical Drive System

Ruchun Zhao;Guoqian Jiang;Qun He;Xiaohang Jin;Ping Xie

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (4)
Traditional fault diagnosis methods mainly rely on a single sensor signal, such as vibration or generator current signals, thus it often leads to limited diagnosis accuracy, primarily when multiple faults exist at the same time. Considering the electromechanical coupling characteristics of the electromechanical drive system, different sensors usually contain correlated and complementary informatio...Show More

Current-Aided Vibration Fusion Network for Fault Diagnosis in Electromechanical Drive System

Ruchun Zhao;Guoqian Jiang;Qun He;Xiaohang Jin;Ping Xie

Year: 2024 | Volume: 73 | Journal Article |

Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines

Ruxu Yue;Guoqian Jiang;Xiaohang Jin;Qun He;Ping Xie

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (11)
Supervisory control and data acquisition (SCADA) data-based wind turbine blade icing detection has been widely studied due to its low cost and easy access. However, SCADA data often present severe class imbalance and thus challenge accurate icing detection. Moreover, since data distribution discrepancy exists in both spatio-temporal features of SCADA data from different wind turbines, the well-tra...Show More

Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines

Ruxu Yue;Guoqian Jiang;Xiaohang Jin;Qun He;Ping Xie

Year: 2024 | Volume: 73 | Journal Article |

Remaining Useful Life Prediction Based on Incremental Learning

Zijun Que;Xiaohang Jin;Zhengguo Xu;Chang Hu

IEEE Transactions on Reliability
Year: 2024 | Volume: 73, Issue: 2 | Journal Article |
Cited by: Papers (3)
Remaining useful life (RUL) prediction based on machine learning assumes that there are enough representative data for training models. However, it is impossible to have so many representative data considering security, economy factors, and so on. Thus, an incremental learning based RUL prediction approach is proposed to address this problem. First, a novel sequence input vector is constructed fro...Show More

Remaining Useful Life Prediction Based on Incremental Learning

Zijun Que;Xiaohang Jin;Zhengguo Xu;Chang Hu

Year: 2024 | Volume: 73, Issue: 2 | Journal Article |

Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction

Yuanming Zhang;Weiyue Zhou;Jiacheng Huang;Xiaohang Jin;Gang Xiao

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (3)
Remaining useful life (RUL) prediction is of great significance to ensure the safety and reliability of equipment. Graph neural network (GNN)-based methods show great potential to improve RUL prediction performance by extracting spatiotemporal (ST) features from sensor monitoring data; however, current methods construct sensor-based homogeneous graphs without considering equipment component struct...Show More

Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction

Yuanming Zhang;Weiyue Zhou;Jiacheng Huang;Xiaohang Jin;Gang Xiao

Year: 2023 | Volume: 72 | Journal Article |

An Adaptive Condition Monitoring Method of Wind Turbines Based on Multivariate State Estimation Technique and Continual Learning

Ziqi Wang;Xiaohang Jin;Zhengguo Xu

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (10)
With the development of wind energy, the condition monitoring (CM) methods of wind turbines (WTs) based on supervisory control and data acquisition (SCADA) data have attracted much attention to detect potential faults. With the impact of complicated internal and external factors, the operation conditions of WTs are time-varying. Thus, it is necessary to adaptively update CM models in long-term ope...Show More

An Adaptive Condition Monitoring Method of Wind Turbines Based on Multivariate State Estimation Technique and Continual Learning

Ziqi Wang;Xiaohang Jin;Zhengguo Xu

Year: 2023 | Volume: 72 | Journal Article |

Condition Monitoring of Wind Turbine Generators Based on SCADA Data and Feature Transfer Learning

Xiaohang Jin;Hao Wang;Ziqian Kong;Zhengguo Xu;Wei Qiao

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (5)
In order to build an effective condition monitoring (CM) model for the target wind turbines (WTs) with few operational data, an approach based on the feature transfer learning and a modified generative adversarial network is proposed. First, a large amount of labelled data from WTs are analyzed to construct a CM model with the aid of an autoencoder. This forms the knowledge of CM for WTs in the so...Show More

Condition Monitoring of Wind Turbine Generators Based on SCADA Data and Feature Transfer Learning

Xiaohang Jin;Hao Wang;Ziqian Kong;Zhengguo Xu;Wei Qiao

Year: 2023 | Volume: 11 | Journal Article |

A Physics-Based and Data-Driven Feature Extraction Model for Blades Icing Detection of Wind Turbines

Xiaohang Jin;Xiaoying Zhang;Xu Cheng;Guoqian Jiang;Lesedi Masisi;Wei Huang

IEEE Sensors Journal
Year: 2023 | Volume: 23, Issue: 4 | Journal Article |
Cited by: Papers (7)
Blades icing will seriously affect the performance of wind turbines with respect to power loss and dynamic load increase. Blades icing detection technique becomes necessary to advance de-icing maintenance. Extracting effective features from supervisory control and data acquisition (SCADA) data has become a challenging task during operating conditions under icing. Current research work lacks for th...Show More

A Physics-Based and Data-Driven Feature Extraction Model for Blades Icing Detection of Wind Turbines

Xiaohang Jin;Xiaoying Zhang;Xu Cheng;Guoqian Jiang;Lesedi Masisi;Wei Huang

Year: 2023 | Volume: 23, Issue: 4 | Journal Article |

Condition Monitoring of Wind Turbine Generator Based on Transfer Learning and One-Class Classifier

Xiaohang Jin;Hengtuo Pan;Chengzuo Ying;Ziqian Kong;Zhengguo Xu;Bin Zhang

IEEE Sensors Journal
Year: 2022 | Volume: 22, Issue: 24 | Journal Article |
Cited by: Papers (12)
The healthy operation conditions of wind turbines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target...Show More

Condition Monitoring of Wind Turbine Generator Based on Transfer Learning and One-Class Classifier

Xiaohang Jin;Hengtuo Pan;Chengzuo Ying;Ziqian Kong;Zhengguo Xu;Bin Zhang

Year: 2022 | Volume: 22, Issue: 24 | Journal Article |

Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network

Ziqian Kong;Xiaohang Jin;Zhengguo Xu;Bin Zhang

IEEE Transactions on Instrumentation and Measurement
Year: 2022 | Volume: 71 | Journal Article |
Cited by: Papers (40)
Prognostics and health management applications rely heavily on predicting industrial equipment’s remaining useful life (RUL). The traditional RUL prediction approaches mainly consider the nonlinear mapping relationship of time series data but rarely consider the structural information of the equipment, resulting in low prediction accuracy. In order to improve the effectiveness of RUL prediction, t...Show More

Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network

Ziqian Kong;Xiaohang Jin;Zhengguo Xu;Bin Zhang

Year: 2022 | Volume: 71 | Journal Article |

Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit

Zijun Que;Xiaohang Jin;Zhengguo Xu

IEEE Transactions on Instrumentation and Measurement
Year: 2021 | Volume: 70 | Journal Article |
Cited by: Papers (78)
Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predic...Show More

Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit

Zijun Que;Xiaohang Jin;Zhengguo Xu

Year: 2021 | Volume: 70 | Journal Article |

Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis

Xiaohang Jin;Zhuangwei Xu;Wei Qiao

IEEE Transactions on Sustainable Energy
Year: 2021 | Volume: 12, Issue: 1 | Journal Article |
Cited by: Papers (123)
Utility-scale wind turbines are equipped with a supervisory control and data acquisition (SCADA) system for remote supervision and control. The SCADA system accumulates a large amount of data that contains the health conditions of the wind turbines. Thus, it is interesting to mine the health status-related information from SCADA data for wind turbine condition monitoring. In this article, an ensem...Show More

Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis

Xiaohang Jin;Zhuangwei Xu;Wei Qiao

Year: 2021 | Volume: 12, Issue: 1 | Journal Article |

Development of Vibration-Based Health Indexes for Bearing Remaining Useful Life Prediction

Xiaohang Jin;Zijun Que;Yi Sun

2019 Prognostics and System Health Management Conference (PHM-Qingdao)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Bearing failure can cause their host system shutdown, and even some catastrophic accidents. These will lead to a high maintenance cost and a huge economic loss. Thus, health monitoring and fault prognosis for bearings becomes increasingly important. Developing an effective health index (HI) will do help in these works. Hence, three different HIs are developed by using root mean square, Kolmogorov-...Show More

Development of Vibration-Based Health Indexes for Bearing Remaining Useful Life Prediction

Xiaohang Jin;Zijun Que;Yi Sun

Year: 2019 | Conference Paper |

A Data-Driven Approach for Bearing Fault Prognostics

Xiaohang Jin;Zijun Que;Yi Sun;Yuanjing Guo;Wei Qiao

IEEE Transactions on Industry Applications
Year: 2019 | Volume: 55, Issue: 4 | Journal Article |
Cited by: Papers (61)
Bearing is a critical component widely used in rotary machines. Bearing failure can cause damages of other components and lead to a lengthy downtime of the machine and costly maintenance. To reduce the cost and downtime for maintenance of the machines, it is desirable to perform fault prognostics to enable predictive health management for bearings. This paper proposes a new data-driven approach fo...Show More

A Data-Driven Approach for Bearing Fault Prognostics

Xiaohang Jin;Zijun Que;Yi Sun;Yuanjing Guo;Wei Qiao

Year: 2019 | Volume: 55, Issue: 4 | Journal Article |

Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods

Xiaohang Jin;Jicong Fan;Tommy W. S. Chow

IEEE Transactions on Instrumentation and Measurement
Year: 2019 | Volume: 68, Issue: 9 | Journal Article |
Cited by: Papers (43)
This paper proposes a new bearing fault detection framework that is based on multivariate statistical process control methods. In this framework, historical offline normal data are used to train the models and calculate the control limits of the monitored metrics. Then, bearings' new online data are the input to the trained models to obtain their monitoring metrics, which are compared with the con...Show More

Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods

Xiaohang Jin;Jicong Fan;Tommy W. S. Chow

Year: 2019 | Volume: 68, Issue: 9 | Journal Article |

A Data-Driven Approach for Bearing Fault Prognostics

Xiaohang Jin;Zijun Que;Yi Sun;Yuanjing Guo;Wei Qiao

2018 IEEE Industry Applications Society Annual Meeting (IAS)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Bearings are one of the critical components widely used in rotary machines. Bearing failure can be catastrophic and may lead to a lengthy downtime of systems for maintenance. Bearing fault prognostics can help reduce the cost for maintenance and avoid catastrophic failures of the systems. This paper proposes a new data-driven approach for bearing fault prognostics, which is based on the Kolmogorov...Show More

A Data-Driven Approach for Bearing Fault Prognostics

Xiaohang Jin;Zijun Que;Yi Sun;Yuanjing Guo;Wei Qiao

Year: 2018 | Conference Paper |

Drivetrain Gearbox Fault Diagnosis: Vibration- and Current-Based Approaches

Xiaohang Jin;Fangzhou Cheng;Yayu Peng;Wei Qiao;Liyan Qu

IEEE Industry Applications Magazine
Year: 2018 | Volume: 24, Issue: 6 | Magazine Article |
Cited by: Papers (25)
Gearboxes are widely used in rotary machines, such as wind turbines, automobiles, and helicopters. Gearbox failures contribute to a significant proportion of the total failures and downtime in these machines. Gearbox fault diagnosis is an effective means to prevent catastrophic failures, improve reliability, and reduce downtime and maintenance costs. Vibration-based approaches have been employed i...Show More

Drivetrain Gearbox Fault Diagnosis: Vibration- and Current-Based Approaches

Xiaohang Jin;Fangzhou Cheng;Yayu Peng;Wei Qiao;Liyan Qu

Year: 2018 | Volume: 24, Issue: 6 | Magazine Article |

A comparative study on Vibration- and current-based approaches for drivetrain gearbox fault diagnosis

Xiaohang Jin;Fangzhou Cheng;Yayu Peng;Wei Qiao;Liyan Qu

2016 IEEE Industry Applications Society Annual Meeting
Year: 2016 | Conference Paper |
Cited by: Papers (7)
Gearboxes are widely used in rotary machines, such as wind turbines, automobiles, and helicopters. Failures of gearboxes contribute to a significant portion of the total failures and downtime in these machines. Gearbox fault diagnosis is an effective means to prevent catastrophic failures, improve the reliability, and reduce the downtime and maintenance cost of these machines. Vibration-based appr...Show More

A comparative study on Vibration- and current-based approaches for drivetrain gearbox fault diagnosis

Xiaohang Jin;Fangzhou Cheng;Yayu Peng;Wei Qiao;Liyan Qu

Year: 2016 | Conference Paper |

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