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Cheng-Geng Huang - IEEE Xplore Author Profile
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Cheng-Geng Huang

Publications
14
Citations
577
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (28)
Image of author Cheng-Geng Huang

Cheng-Geng Huang

Affiliation

School of Automation Engineering
University of Electronic Science and Technology of China
Chengdu, China

Publication Topics

Biography

Cheng-Geng Huang received the B.S. degree in information display and photoelectric technology and the Ph.D. degree in mechanical engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2013 and 2019, respectively.,Currently, he is a Lecturer with the School of Automation Engineering, University of Electronic Science and Technology of China. He is also an active researcher in the field of prognostics and health management, industrial AI, and resilient analysis. He has published over 15 prestigious journal articles, three of which are ESI highly cited papers. He has held several projects sponsored by the National Natural Science Foundation of China, the China Postdoctoral Science Foundation, and the F...
Publications
14
Citations
577
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (28)
Author's Published Works

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The Interpretable Reasoning and Intelligent Decision-Making Based on Event Knowledge Graph With LLMs in Fault Diagnosis Scenarios

ChangHao Men;Yu Han;Ping Wang;JianYuan Tao;Cheng-Geng Huang

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Volume: 74 | Journal Article |
With the emergence of large language models (LLMs), artificial intelligence (AI) has experienced revolutionary advancements. Fault diagnosis and maintenance, as crucial components of industrial production, can also undergo significant technological innovations. However, as black-box models, LLMs have three main drawbacks in fault diagnosis scenarios that require deep and responsible reasoning and ...Show More

The Interpretable Reasoning and Intelligent Decision-Making Based on Event Knowledge Graph With LLMs in Fault Diagnosis Scenarios

ChangHao Men;Yu Han;Ping Wang;JianYuan Tao;Cheng-Geng Huang

Year: 2025 | Volume: 74 | Journal Article |

Interference Suppression of Nonstationary Signals for Bearing Diagnosis Under Transient Noise Measurements

Peng Chen;Yuhao Wu;Chaojun Xu;Cheng-Geng Huang;Mian Zhang;Junlin Yuan

IEEE Transactions on Reliability
Year: 2025 | Early Access Article |
In real-world applications, the diagnostic efficiency of rolling bearings is commonly affected by operating conditions like fluctuating rotating speed and varying loads, especially, environmental disturbances like transient noises. These disturbances tend to mask the indicators of damage, presenting substantial obstacles for accurately pinpointing failures. Traditional diagnostic methods struggle ...Show More

Interference Suppression of Nonstationary Signals for Bearing Diagnosis Under Transient Noise Measurements

Peng Chen;Yuhao Wu;Chaojun Xu;Cheng-Geng Huang;Mian Zhang;Junlin Yuan

Year: 2025 | Early Access Article |

Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Cheng-Geng Huang;He Li;Weiwen Peng;Loon Ching Tang;Zhi-Sheng Ye

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 22 | Journal Article |
Cited by: Papers (1)
Health prognostics within the Internet of Things (IoT) paradigm face several challenges, including data privacy, client drift, and prediction accuracy. Federated learning (FL), as an emerging decentralized machine learning paradigm, has the potential to address these challenges by integrating multiple data silos in a distributed and privacy-preserved fashion. This article develops a novel personal...Show More

Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Cheng-Geng Huang;He Li;Weiwen Peng;Loon Ching Tang;Zhi-Sheng Ye

Year: 2024 | Volume: 11, Issue: 22 | Journal Article |

A New Incremental Learning for Bearing Fault Diagnosis Under Noisy Conditions Using Classification and Feature-Level Information

Jun Zhu;Yuanfan Wang;Cheng-Geng Huang;Changqing Shen;Bojian Chen

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (4)
Deep learning has been widely used for fault diagnosis of complex mechanical equipment in recent years. However, fault types keep increasing with the change in the working state of mechanical equipment in practical scenarios, which causes the performance of traditional machine-learning models to degrade rapidly. Incremental learning can continuously learn new knowledge from incremental information...Show More

A New Incremental Learning for Bearing Fault Diagnosis Under Noisy Conditions Using Classification and Feature-Level Information

Jun Zhu;Yuanfan Wang;Cheng-Geng Huang;Changqing Shen;Bojian Chen

Year: 2024 | Volume: 73 | Journal Article |

A Hybrid Health Prognostics Method for Proton Exchange Membrane Fuel Cells With Internal Health Recovery

Weiwen Peng;Zongyi Wei;Cheng-Geng Huang;Guodong Feng;Jun Li

IEEE Transactions on Transportation Electrification
Year: 2023 | Volume: 9, Issue: 3 | Journal Article |
Cited by: Papers (9)
Existing health prognostics methods often omit the internal health recovery of proton exchange membrane fuel cells (PEMFCs), although this phenomenon commonly exists, especially in the long-term usage of PEMFCs for hydrogen fuel cell vehicles. To this end, a novel hybrid method for PEMFCs is proposed, and internal recovery effects and external health data are collaboratively leveraged to achieve h...Show More

A Hybrid Health Prognostics Method for Proton Exchange Membrane Fuel Cells With Internal Health Recovery

Weiwen Peng;Zongyi Wei;Cheng-Geng Huang;Guodong Feng;Jun Li

Year: 2023 | Volume: 9, Issue: 3 | Journal Article |

Intelligent Health Monitoring of Machine Tools Using a Bayesian Multibranch Neural Network

Rong Zhu;Weiwen Peng;Yu Han;Cheng-Geng Huang

IEEE Sensors Journal
Year: 2022 | Volume: 22, Issue: 12 | Journal Article |
Cited by: Papers (4)
Online health monitoring of machine tools is an essential technique for tool life extension, manufacturing productivity improvement and product quality improvement. In the era of industrial big data, numerous deep learning (DL)-based methods have been proposed to achieve these goals. However, in complex and dynamic manufacturing processes, practical concerns such as uncertainty quantification and ...Show More

Intelligent Health Monitoring of Machine Tools Using a Bayesian Multibranch Neural Network

Rong Zhu;Weiwen Peng;Yu Han;Cheng-Geng Huang

Year: 2022 | Volume: 22, Issue: 12 | Journal Article |

Electrochemical Impedance Spectroscopy Based State-of-Health Estimation for Lithium-Ion Battery Considering Temperature and State-of-Charge Effect

Qunming Zhang;Cheng-Geng Huang;He Li;Guodong Feng;Weiwen Peng

IEEE Transactions on Transportation Electrification
Year: 2022 | Volume: 8, Issue: 4 | Journal Article |
Cited by: Papers (79)
State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS)-based technique has been proven to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (S...Show More

Electrochemical Impedance Spectroscopy Based State-of-Health Estimation for Lithium-Ion Battery Considering Temperature and State-of-Charge Effect

Qunming Zhang;Cheng-Geng Huang;He Li;Guodong Feng;Weiwen Peng

Year: 2022 | Volume: 8, Issue: 4 | Journal Article |

Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers

Jun Zhu;Cheng-Geng Huang;Changqing Shen;Yongjun Shen

IEEE Transactions on Industrial Informatics
Year: 2022 | Volume: 18, Issue: 11 | Journal Article |
Cited by: Papers (59)
Cross-domain fault diagnosis methods based on transfer learning attempt to leverage knowledge from a domain with sufficient labeled samples to a different but related domain with few or even nonlabeled samples. These methods have been widely investigated in the past years. Notwithstanding the efficacy, most existing approaches assume that the label spaces of training and testing data are the same....Show More

Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers

Jun Zhu;Cheng-Geng Huang;Changqing Shen;Yongjun Shen

Year: 2022 | Volume: 18, Issue: 11 | Journal Article |

A Novel Bayesian Deep Dual Network With Unsupervised Domain Adaptation for Transfer Fault Prognosis Across Different Machines

Cheng-Geng Huang;Jun Zhu;Yu Han;Weiwen Peng

IEEE Sensors Journal
Year: 2022 | Volume: 22, Issue: 8 | Journal Article |
Cited by: Papers (28)
The existing deep learning-based fault prognostic methods require massive labeled condition monitoring (CM) data to train a well-generalized model. However, acquiring massive labeled CM data for real-case machines is infeasible due to time, economic costs, and safety concerns. Fortunately, we can handily obtain labeled CM data from relevant but different machines such as from accelerated degradati...Show More

A Novel Bayesian Deep Dual Network With Unsupervised Domain Adaptation for Transfer Fault Prognosis Across Different Machines

Cheng-Geng Huang;Jun Zhu;Yu Han;Weiwen Peng

Year: 2022 | Volume: 22, Issue: 8 | Journal Article |

Fault prognosis using deep convolutional neural network and bootstrap-based method

Cheng-Geng Huang;Hong-Zhong Huang;Yan-Feng Li;Weiwen Peng

2020 IEEE 18th International Conference on Industrial Informatics (INDIN)
Year: 2020 | Conference Paper |
This article develops a generalized deep convolutional neural network (DCNN)-Bootstrap-based prognostic approach for remaining useful life (RUL) prediction of rolling bearing. The proposed architecture includes two main parts: first, a hybrid DCNN model is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and predict RUL of bea...Show More

Fault prognosis using deep convolutional neural network and bootstrap-based method

Cheng-Geng Huang;Hong-Zhong Huang;Yan-Feng Li;Weiwen Peng

Year: 2020 | Volume: 1 | Conference Paper |

An Enhanced Deep Learning-Based Fusion Prognostic Method for RUL Prediction

Cheng-Geng Huang;Xianhui Yin;Hong-Zhong Huang;Yan-Feng Li

IEEE Transactions on Reliability
Year: 2020 | Volume: 69, Issue: 3 | Journal Article |
Cited by: Papers (73)
This article proposes a novel deep learning based fusion prognostic method for remaining useful life (RUL) prediction of engineering systems. The proposed framework strategically combines the advantages of bidirectional long short-term memory (BLSTM) networks and particle filter (PF) method and meanwhile mitigates their limitations. In the proposed framework, BLSTM networks are applied for further...Show More

An Enhanced Deep Learning-Based Fusion Prognostic Method for RUL Prediction

Cheng-Geng Huang;Xianhui Yin;Hong-Zhong Huang;Yan-Feng Li

Year: 2020 | Volume: 69, Issue: 3 | Journal Article |

Fault prognosis of Engineered Systems: A Deep Learning Perspective

Cheng-Geng Huang;Xiang-Yu Li;Hong-Zhong Huang;Yan-Feng Li

2019 Annual Reliability and Maintainability Symposium (RAMS)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
In modern industry, engineered systems are generally required to work under complex operational conditions to complete specific missions. But most of existing data-driven prognostic methods still lack an effective model that can utilize operational conditions data to predict remaining useful life (RUL) of engineered systems. To fill these practical gaps, this paper develops a novel prognostic meth...Show More

Fault prognosis of Engineered Systems: A Deep Learning Perspective

Cheng-Geng Huang;Xiang-Yu Li;Hong-Zhong Huang;Yan-Feng Li

Year: 2019 | Conference Paper |

Reliability Evaluation for the C4ISR Communication System via Propagation Model

Xiang Li;Hong-Zhong Huang;Xiang-Yu Li;Cheng-Geng Huang

2019 Annual Reliability and Maintainability Symposium (RAMS)
Year: 2019 | Conference Paper |
The effect of path loss on the reliability of wireless communication in Military C4ISR (Command, Control, Communication, computer, Intelligence, Surveillance and Reconnaissance) Architectures has drawn much attention. Previous work were focused on the reliability assessment of infrastructure, but its link reliability has not been fully considered. This paper puts forward a new method for the relia...Show More

Reliability Evaluation for the C4ISR Communication System via Propagation Model

Xiang Li;Hong-Zhong Huang;Xiang-Yu Li;Cheng-Geng Huang

Year: 2019 | Conference Paper |

A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Cheng-Geng Huang;Hong-Zhong Huang;Yan-Feng Li

IEEE Transactions on Industrial Electronics
Year: 2019 | Volume: 66, Issue: 11 | Journal Article |
Cited by: Papers (319)
Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develops a novel prognostic method based on bidirectional long short-term memory (BLSTM) networks. The meth...Show More

A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

Cheng-Geng Huang;Hong-Zhong Huang;Yan-Feng Li

Year: 2019 | Volume: 66, Issue: 11 | Journal Article |

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