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学者姓名：姚立纲
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Abstract ：
Entropy theories play a significant role in rotating machinery fault detection. The key parameters of these methods are, however, often selected subjectively based on trial-and-error methods or engineering experience. Unsuitable parameters would result in an inconsistency between the extracted entropy results and the realistic case. In order to address this issue, a complexity measurement method called "swarm intelligence optimization entropy" (SIOE) is proposed, which adaptively estimates optimal parameters using skewness metrics, logistic chaos theory, and African vulture optimization (AVO). By considering the variability and dynamic changes of various signals, SIOE enables the extraction of robust and discriminative dynamic features. Additionally, a collaborative intelligent fault detection method for rotating machinery fault detection is developed, based on SIOE and extreme gradient boosting (XGBoost). This method aims to accurately identify single faults, compound faults, and varying fault degrees within the rotating machinery. Simulation and fault detection experiments on rotating machines demonstrate that SIOE improves recognition accuracy by up to 21.25% compared to existing entropy methods. The proposed intelligent fault detection method improves recognition accuracy by up to 15.71% compared to advanced fault detection methods. These results highlight the advantages of SIOE in complexity measurement and feature extraction, as well as the effectiveness and accuracy of the proposed intelligent fault detection method, in identifying rotating machinery faults.
Keyword ：
Accuracy Accuracy Aerodynamics Aerodynamics Complexity theory Complexity theory Entropy Entropy Extreme gradient boosting (XGBoost) Extreme gradient boosting (XGBoost) fault detection fault detection Fault detection Fault detection feature extraction feature extraction Feature extraction Feature extraction Fluctuations Fluctuations Machinery Machinery Particle swarm optimization Particle swarm optimization rotating machinery rotating machinery swarm intelligence optimization entropy (SIOE) swarm intelligence optimization entropy (SIOE) Vibrations Vibrations
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| GB/T 7714 | Wang, Zhenya , Yao, Ligang , Li, Minglin et al. A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Wang, Zhenya et al. "A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Wang, Zhenya , Yao, Ligang , Li, Minglin , Chen, Meng , Zhao, Jingshan , Chu, Fulei et al. A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Aero-engines, pumps, and trains are widely used in transportation, maritime, aerospace, and other industries. However, these devices often operate in harsh and complex environments, making their internal components prone to failure. Thus, constructing a highly accurate fault diagnosis model is essential for ensuring the safe and reliable operation of machinery. However, most existing models require many labeled samples to build accurate training models, which is both expensive and difficult to achieve. Moreover, some models lack adaptability and often require adjustments to their structure or hyperparameters to suit new diagnostic tasks. To address these challenges, this paper proposes a few-shot fault diagnosis model based on multi-scale perception multi-level feature fusion image quadrant entropy (MPMFFIQE). The MPMFFIQE method uses the gramian angle summation field (GASF) to convert transient signals into images, preserving more detailed information about the mechanical state. The multi-scale perception multi-level feature strategy is then applied to sequentially enlarge and reconstruct feature maps at various levels, maximizing the extraction of fault-related information. Afterward, the fusion image quadrant entropy technique is proposed to combine nonlinear dynamic features from these feature maps, forming the mechanical MPMFFIQE feature set. Finally, this set is input into the harris hawks optimization support vector machine (HHOSVM) classifier to achieve fault identification. Results from three real-world case studies demonstrate that the proposed MPMFFIQE method improves accuracy by up to 12.90% in comparison with six feature extraction techniques. Furthermore, the proposed model achieves an accuracy rate exceeding 98.10% with just five training samples per state, representing up to a 27.48% improvement over six existing models. These findings confirm that the developed model can effectively and accurately diagnose mechanical faults in industrial applications using only a small number of training samples. Additionally, the model shows strong generalization across different mechanical equipment, highlighting its significant practical value. © 2024 Elsevier Ltd
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| GB/T 7714 | Wang, Zhenya , Liang, Pan , Bai, Rengui et al. Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy [J]. | Advanced Engineering Informatics , 2025 , 63 . |
| MLA | Wang, Zhenya et al. "Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy" . | Advanced Engineering Informatics 63 (2025) . |
| APA | Wang, Zhenya , Liang, Pan , Bai, Rengui , Liu, Yaming , Zhao, Jingshan , Yao, Ligang et al. Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy . | Advanced Engineering Informatics , 2025 , 63 . |
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To enhance the generalization capability of rotating machinery fault diagnosis, a novel generalized fault diagnosis framework is proposed. Phase entropy is introduced as a new method for measuring mechanical signal complexity. Furthermore, it is extended to refined time-shift multi-scale phase entropy. The extended method effectively captures dynamic characteristic information across multiple scales, providing a comprehensive reflection of the equipment's state. Based on signal amplitude, multiple time-shift multi-scale decomposition subsignals are constructed, and a scatter diagram is generated for each sub-signal. Subsequently, the diagram is partitioned into several regions, and the distribution probability of each region is calculated, enabling the extraction of stable and easily distinguishable features through the refined operation. Next, the one-versus-onebased twin support vector machine classifier is employed to achieve high-accuracy fault identification. Case analyses of a wind turbine, an aero-engine, a train transmission system, and an aero-bearing demonstrate that the accuracy, precision, recall, and F1 score of the proposed framework are over 99.51 %, 99.52 %, 99.51 %, and 99.51 %, respectively, using only five training samples per state. The proposed framework achieves higher accuracy compared to nine existing models via deep learning or machine learning. The aforementioned analysis results validate the accuracy and generalizability of the proposed framework.
Keyword ：
Fault diagnosis Fault diagnosis Phase entropy Phase entropy Rotating machinery Rotating machinery Twin support vector machine Twin support vector machine
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| GB/T 7714 | Wang, Zhenya , Zhang, Meng , Chen, Hui et al. A generalized fault diagnosis framework for rotating machinery based on phase entropy [J]. | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2025 , 256 . |
| MLA | Wang, Zhenya et al. "A generalized fault diagnosis framework for rotating machinery based on phase entropy" . | RELIABILITY ENGINEERING & SYSTEM SAFETY 256 (2025) . |
| APA | Wang, Zhenya , Zhang, Meng , Chen, Hui , Li, Jinghu , Li, Gaosong , Zhao, Jingshan et al. A generalized fault diagnosis framework for rotating machinery based on phase entropy . | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2025 , 256 . |
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Accurate estimation of battery state of health (SOH) parameters is crucial for enhancing the reliability of battery management systems. It depends on the quality of health features (HFs) extraction and the precision of the estimation algorithms. Typically, the process of extracting battery HFs is cumbersome and heavily relies on expert knowledge, with few studies focused on improving estimation methods. To solve this problem, a novel high-accuracy intelligent estimation method for battery SOH has been proposed in this work. This method includes the fractional-order refined time-shift multiscale fuzzy entropy feature extraction (FRTSMFE) algorithm, and the least square support vector machine (LSSVM) estimation algorithm, improved by the energy valley optimization (EVO) algorithm. By combining the fractional-order derivatives, the refined time-shift multiscale method, and fuzzy entropy theory, the FRTSMFE algorithm was derived to extract HFs from voltage and current data during charging. The EVO algorithm was used to optimize the hyperparameters of the LSSVM algorithm, enhancing its estimation accuracy. The EVO-LSSVM algorithm was then utilized to establish the correlation between the HFs and SOH of each battery. Finally, comparative and ablation experiments were conducted on the NASA, CALCE and FB datasets to validate the effectiveness and accuracy of the proposed high-accuracy intelligent estimation method for battery SOH. Additionally, the robustness of the proposed algorithm was validated using the MIT, XJTU, and TJU datasets.
Keyword ：
Feature extraction Feature extraction Lithium-ion battery Lithium-ion battery Machine learning Machine learning State estimation State estimation State of health State of health
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| GB/T 7714 | Liu, Yaming , Ding, Jiaxin , Yao, Ligang et al. A novel high-accuracy intelligent estimation method for battery state of health [J]. | MEASUREMENT , 2025 , 245 . |
| MLA | Liu, Yaming et al. "A novel high-accuracy intelligent estimation method for battery state of health" . | MEASUREMENT 245 (2025) . |
| APA | Liu, Yaming , Ding, Jiaxin , Yao, Ligang , Su, Haocheng , Chen, Yangxin , Wang, Zhenya . A novel high-accuracy intelligent estimation method for battery state of health . | MEASUREMENT , 2025 , 245 . |
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Accurately estimating the battery's state of health (SOH) is critical for battery efficiency and stability. Despite significant progress in data-driven methods, the accuracy of these models is limited by feature extraction strategies and the scarcity of dataset samples. To address this issue, this study develops a battery SOH estimation model tailored to the limited sample conditions. A refined composite multiscale discrete sine entropy (RCMDSE) algorithm is proposed, which combines composite multiscale approaches, Shannon entropy theory, and the discrete sine transform. This algorithm is designed to extract high-quality battery entropy domain health features (HFs) from current and voltage signals at various scales and levels. Subsequently, we introduce semi-supervised learning concepts to enhance the estimation performance of the nu-support vector regression (NuSVR) algorithm in limited sample conditions. The golden jackal optimization algorithm (GJO) is used to improve the estimation accuracy of the NuSVR algorithm in a semi-supervised framework. Comparative and ablation experiments on four datasets validate that the battery SOH estimation model maintains RMSE and MAPE values of <1 %, even when trained with only 10 % of the data. Furthermore, the proposed RCMDSE algorithm outperforms and is more robust in HF extraction than the widely used incremental capacity (IC) curve feature extraction method.
Keyword ：
Battery Battery Entropy feature Entropy feature Semi-supervised learning Semi-supervised learning State estimation State estimation State of health State of health
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| GB/T 7714 | Liu, Yaming , Ding, Jiaxin , Cai, Yingjie et al. A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions [J]. | JOURNAL OF ENERGY STORAGE , 2025 , 106 . |
| MLA | Liu, Yaming et al. "A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions" . | JOURNAL OF ENERGY STORAGE 106 (2025) . |
| APA | Liu, Yaming , Ding, Jiaxin , Cai, Yingjie , Luo, Biaolin , Yao, Ligang , Wang, Zhenya . A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions . | JOURNAL OF ENERGY STORAGE , 2025 , 106 . |
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The wheeled bipedal robots have great application potential in environments with a mixture of structured and unstructured terrain. However, wheeled bipedal robots have problems such as poor balance ability and low movement level on rough roads. In this paper, a novel and low-cost wheeled bipedal robot with an asymmetrical five-link mechanism is proposed, and the kinematics of the legs and the dynamics of the Wheeled Inverted Pendulum (WIP) are modeled. The primary balance controller of the wheeled bipedal robot is built based on the Linear Quadratic Regulator (LQR) and the compensation method of the virtual pitch angle adjusting the Center of Mass (CoM) position, then the whole-body hybrid torque-position control is established by combining attitude and leg controllers. The stability of the robot's attitude control and motion is verified with simulations and prototype experiments, which confirm the robot's ability to pass through complex terrain and resist external interference. The feasibility and reliability of the proposed control model are verified.
Keyword ：
Wheeled Robots Legged Robots Motion Control Mechanism Design Wheeled Robots Legged Robots Motion Control Mechanism Design
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| GB/T 7714 | Xiong, Yi , Liu, Haojie , Chen, Bingxing et al. Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot [J]. | JOURNAL OF BIONIC ENGINEERING , 2025 , 22 (2) : 626-641 . |
| MLA | Xiong, Yi et al. "Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot" . | JOURNAL OF BIONIC ENGINEERING 22 . 2 (2025) : 626-641 . |
| APA | Xiong, Yi , Liu, Haojie , Chen, Bingxing , Chen, Yanjie , Yao, Ligang , Lu, Zongxing . Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot . | JOURNAL OF BIONIC ENGINEERING , 2025 , 22 (2) , 626-641 . |
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The field of bioelectronics has witnessed significant advancements, offering practical solutions for personalized healthcare through the acquisition and analysis of skin-based physical, chemical, and electrophysiological signals. Despite these advancements, current bioelectronics face several challenges, including complex preparation procedures, poor skin adherence, susceptibility to motion artifacts, and limited personalization and reconfigurability capabilities. In this study, we introduce an innovative method for fabricating erasable bioelectronics on a flexible substrate coating adhered to the skin using a ballpoint pen without any postprocessing. Our approach yields devices that are thin, erasable, reconfigurable, dry-friction resistant, self-healing, and highly customizable. We demonstrate the multifunctionality of these on-skin bioelectronics through their application as strain sensors for motion monitoring, temperature and humidity sensors for breath monitoring, and heating elements for target point hyperthermia. The potential of our bioelectronics in personalized medicine is substantial, particularly in health monitoring. We provide a novel solution for achieving efficient and convenient personalized medical services, addressing the limitations of existing technologies and paving the way for next-generation wearable health devices.
Keyword ：
bioelectronics bioelectronics direct writing direct writing erasable erasable multifunctional multifunctional on-skin on-skin
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| GB/T 7714 | Zhu, Xintao , Liu, Wei , Luo, Qinzhou et al. Erasable and Multifunctional On-Skin Bioelectronics Prepared by Direct Writing [J]. | ACS SENSORS , 2025 , 10 (4) : 2850-2860 . |
| MLA | Zhu, Xintao et al. "Erasable and Multifunctional On-Skin Bioelectronics Prepared by Direct Writing" . | ACS SENSORS 10 . 4 (2025) : 2850-2860 . |
| APA | Zhu, Xintao , Liu, Wei , Luo, Qinzhou , Lv, Zhen , Yao, Ligang , Wei, Fanan . Erasable and Multifunctional On-Skin Bioelectronics Prepared by Direct Writing . | ACS SENSORS , 2025 , 10 (4) , 2850-2860 . |
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Toroidal drives combine a planetary gear drive with a worm gear drive. Furthermore, in toroidal drives, the planetary gear teeth are movable. The motion of spherical movable teeth for planetary gears affects the friction, wear and transmission efficiency of the meshing surface, but this effect has not been thoroughly studied to date. This study involved a kinematic analysis of planetary gear teeth. Frenet-fram was first introduced into the toroidal drive to describe the motion of spherical movable teeth. A contact analysis of the spherical movable teeth was carried out, and a mathematical model of rolling-sliding motion between the spherical movable teeth and the central worm spiral groove was established via the conversion mechanism method. The instantaneous velocity formula and relative sliding velocity formula at the meshing points of movable teeth were derived. The influence of the system parameters on the sliding velocity was analysed, and several useful conclusions were drawn. These results can provide a theoretical foundation for subsequent research on the friction loss and transmission efficiency of toroidal drives.
Keyword ：
Contact analysis Contact analysis Mathematical modelling Mathematical modelling Rolling-sliding Rolling-sliding Sliding velocity Sliding velocity Toroidal drive Toroidal drive
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| GB/T 7714 | Zeng, Xuelian , Yao, Ligang , Lou, Meiyan . Mathematical modelling and sliding characteristics analysis on the spherical movable teeth for the toroidal drive [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Zeng, Xuelian et al. "Mathematical modelling and sliding characteristics analysis on the spherical movable teeth for the toroidal drive" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Zeng, Xuelian , Yao, Ligang , Lou, Meiyan . Mathematical modelling and sliding characteristics analysis on the spherical movable teeth for the toroidal drive . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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In recent years, continuum soft robots have emerged as a promising avenue for the advancement of in vivo therapeutic interventions. However, the current continuum robots are often limited to singular functionalities and exhibit a deficiency in diagnostic capabilities for vascular lesions. For example, vasculitis often leads to temperature abnormalities in local blood vessels, and the existing continuum robots are unable to accurately detect the lesion area based on this characteristic. To address this issue, this paper presents the design of a multifunctional integrated thermally drawn polymer multichannel continuum robot. First, the magnetic deformation of the continuum robot was theoretically analyzed, and the robot's locomotion within a flow field was experimentally verified. Moreover, different channels of the multichannel continuum robot were independently designed for specific functions, enabling multithreaded operations. It can perform real-time sensing and monitoring of external environmental temperatures with high resolution and carry out targeted drug delivery as well as neural electrical stimulation. We successfully conducted in vitro experiments on isolated frog sciatic nerves, confirming the effectiveness of the multichannel continuum robot for biological treatment. The multichannel continuum robot shows great potential in the diagnosis and treatment of vasculitis in situ and nerve system disorder.
Keyword ：
continuum soft robot continuum soft robot electrical nerve stimulation electrical nerve stimulation magneticresponse magneticresponse multifunction multifunction temperaturemonitoring temperaturemonitoring
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| GB/T 7714 | Liu, Wei , Luo, Qinzhou , Zhu, Xintao et al. A Multichannel Continuum Robot for In Situ Diagnosis and Treatment of Vascular Lesions [J]. | ACS BIOMATERIALS SCIENCE & ENGINEERING , 2025 , 11 (5) : 3071-3081 . |
| MLA | Liu, Wei et al. "A Multichannel Continuum Robot for In Situ Diagnosis and Treatment of Vascular Lesions" . | ACS BIOMATERIALS SCIENCE & ENGINEERING 11 . 5 (2025) : 3071-3081 . |
| APA | Liu, Wei , Luo, Qinzhou , Zhu, Xintao , Liu, Ming , Yao, Ligang , Wei, Fanan . A Multichannel Continuum Robot for In Situ Diagnosis and Treatment of Vascular Lesions . | ACS BIOMATERIALS SCIENCE & ENGINEERING , 2025 , 11 (5) , 3071-3081 . |
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借助前期研制的张拉仿生机器鱼,通过实验初步探索鱼体的身体刚度分布与鱼体波参数之间的关系.使用鱼体波重构方法,获取张拉机器鱼在频率为 1.87 Hz时不同刚度分布下的鱼体波参数.实验结果表明,摆幅、相位、波速和曲率与刚度分布之间存在关系.通过调整机器鱼的刚度分布,波速最大可提高约 21.5%,并且可以实现与真实鱼类相似的摆幅和改变最大曲率发生的位置.非均匀刚度分布在改变摆幅等方面存在优势.机器鱼第 4 关节的刚度对波速具有较大影响,但对曲率影响较小.刚度分布与鱼体波参数的相关性有助于机器鱼通过控制身体刚度优化鱼体波参数,提高游动性能.
Keyword ：
仿生机器鱼 仿生机器鱼 刚度分布 刚度分布 张拉整体结构 张拉整体结构 鱼体波参数 鱼体波参数
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| GB/T 7714 | 陈文祥 , 章杰 , 姜洪洲 et al. 张拉仿生机器鱼身体刚度分布对鱼体波参数的影响 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (2) : 159-167 . |
| MLA | 陈文祥 et al. "张拉仿生机器鱼身体刚度分布对鱼体波参数的影响" . | 福州大学学报（自然科学版） 53 . 2 (2025) : 159-167 . |
| APA | 陈文祥 , 章杰 , 姜洪洲 , 姚立纲 , 陈炳兴 . 张拉仿生机器鱼身体刚度分布对鱼体波参数的影响 . | 福州大学学报（自然科学版） , 2025 , 53 (2) , 159-167 . |
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