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Lubricant Transport Mechanism and Dynamics Model for Nepenthes-shaped Biomimetic Microtexture

Chinese Journal of Mechanical engineering 2025年第2期38卷 235-252页

作者： Xiaoming Wang Min Yang Teng Gao Lan Dong Yusuf Suleiman Dambatta Xin Liu Yuying Yang Qinglong An Yanbin Zhang Changhe Li Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of EducationQingdao University of Technology School of Mechanical and Electrical Engineering Qingdao Binhai University Mechanical Engineering Department Ahmadu Bello University School of Mechanical Engineering Dalian University of Technology School of Mechanical Engineering Qilu University of Technology (Shandong Academy of Sciences) School of Mechanical Engineering Shanghai Jiao Tong University Qingdao Jimo Qingli Intelligent Manufac turing Industry Research Institute

During the metal cutting process, especially in continuous contact conditions like turning, the challenge of lubricants failing to effectively reach the cutting point remains unresolved. Micro-textured cutting tools offer a potential solution for tool-chip contact challenges. Inspired by the evolutionary achievements of the biosphere, micro-textures are expected to overcome lubrication limitations in cutting zones. Drawing on the anti-gravity water transport seen at the mouth edge of the Nepenthes plant, an innovative microchannel with Nepenthes-shaped contours was designed on the rake face to enable controlled lubricant transport. However, the dynamics of lubricant delivery on textured surfaces are not fully understood. This study first analyzed the microstructure and water transport mechanism of Nepenthes to reconstruct a micro-textured surface for controlled lubricant transport. A dynamic model was then developed to describe lubricant transport within open microchannels, with mathematical simulations predicting transport speed and flow distance. To validate this model, diffusion experiments of alumina soybean oil nanolubricant on polycrystalline diamond(PCD) cutting tool surfaces were conducted, showing an average prediction deviation of 5.01%. Compared with the classical Lucas-Washburn model, the new model improved prediction accuracy by 4.72%. Additionally, comparisons were made to examine droplet spreading and non-uniform diffusion on textured surfaces, revealing that the T2 surface exhibited the strongest unidirectional diffusion characteristics. The contact angle ratio, droplet unidirectional spreading ratio, and droplet spreading aspect ratio were 0.48, 1.75, and 3.99, respectively. Finally, the anti-wear, friction-reducing, and efficiency-enhancing mechanisms of micro-textured surfaces in minimum quantity lubrication turning were analyzed. This approach may support continuous cutting of difficult-tomachine materials.

关键词： Biomimetic microtexture Capillary flow Nanolubricant Nepenthes

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control Method for Desorption Tower Liquid Level in Fluidized Catalytic Cracking Based on LESO-DMC

Control Method for Desorption Tower Liquid Level in Fluidize...

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2023 China Automation Congress, CAC 2023

作者： Xiao, Jinkun Zhu, Haibo Zhou, Fan Sun, Kaibiao Zhao, Jun Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment Ministry of Education China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350303759

Fluidized catalytic cracking (FCC) units are crucial equipment in the petroleum refining process, and the level of control directly impacts product quality and yield. To improve the control level of the desorption tower in FCC, this paper proposes a dynamic matrix control (DMC) approach based on the active disturbance rejection strategy. Based on DMC, a linear extended state observer (LESO) is constructed to compensate for model plant mismatch (MPM) and unknown disturbances. The observer bandwidth parameters are determined to optimize robustness, enabling the implementation of active disturbance rejection model predictive control in FCC units. A comparative experimental study is conducted, comparing the proposed method with the traditional DMC approach that does not account for uncertainties and disturbances. The impact of different disturbances on the control strategy is discussed. Experimental results demonstrate that the proposed method exhibits superior control performance and stronger disturbance rejection capability for the liquid level in the desorption tower of the FCC unit. © 2023 IEEE.

关键词： Fluidization

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On the optimality of Kalman filter for fault detection 4

On the optimality of Kalman filter for fault detection

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Zhou, Jinming Zhu, Yucai Yang, Chao Zhu, Yun Yin, Feng Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Zhejiang University State Key Lab of Clean Energy Utilization State Environmental Protection Center for Coal-Fired Air Pollution Control Hangzhou310027 China Zhejiang Baima Lake Laboratory Co. Ltd Hangzhou310051 China

ISBN: (纸本)9798350349252

Kalman filter is widely used for residual generation in fault detection. It leads to optimality in fault detection using some performance indices and also leads to statistically sound residual evaluation and threshold setting. This paper shows that these nice features do not necessarily imply an optimal fault detection performance. Based on a performance index related to fault detection rate and false alarm rate, several occasions where Kalman filter should not be used are pointed out;further the residual evaluation and threshold setting are discussed, in which it is pointed out that in stochastic setting an optimal statistical test of Kamlan filter is not related to optimality of commonly used detection performance indicators. The theoretical analysis is verified through Monte Carlo simulations and Tennessee Eastman process (TEP) dataset. © 2024 IEEE.

关键词： Kalman filters

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Efficient Identification and Accurate Evaluation for Shutdown Operation Flow of Cryogenic Air Separation Units

Efficient Identification and Accurate Evaluation for Shutdow...

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第35届中国过程控制会议

作者： Duowen Chen Guanghui Yang Hongfeng Lou Xiongfeng Feng Zuhua Xu Zhijiang Shao Department of Polytechnic Zhejiang University State Key Lab of Industrial Control Technology Department of Control Science and Engineering Zhejiang University

During the operation of air separation units(ASU), planned short-term shutdowns are often required to ensure a balance between supply and demand of downstream air separation products, and maintain equipment performance and ensure production safety. Making the device shut down smoothly and safely is a key measure in air separation production. However, this process highly relies on manual participation and carries risks and uncertainties. In response to this practical demand, we propose an ASU Shutdown operation flow assistance system(SOFAS). We firstly build a standard logical model based on the actual shutdown process and develop algorithms to assist in executing standardized and safe shutdown operations. Then, we propose an operation flow identification algorithm to identify the shutdown operation flow, and an evaluation algorithm to quantitatively analyze the efficiency and safety of the operation flow using two performance indicators. Finally, the effectiveness of the method was verified through experimental analysis using the actual operational data of ASU.

关键词： Air Separation Unit Shut down Identification algorithm Evaluation algorithm Operation flow

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CPL-SLAM: Centralized Collaborative Multirobot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Carleton University School of Information Technology Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Traditional visual-inertial Simultaneous Localization and Mapping (SLAM) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues, such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intrarobot and interrobot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Federated Ophthalmic Disease Diagnosis Based on Weighted Averaging and Local Optimization

Federated Ophthalmic Disease Diagnosis Based on Weighted Ave...

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2023 China Automation Congress, CAC 2023

作者： Gu, Aoguang Jiang, Xiaoyu Gao, Zhiyuan Ye, Juan Jin, Kai Zhang, Xinmin Zhejiang University The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China The Second Affiliated Hospital of Zhejiang University The Department of Ophthalmology Hangzhou310027 China

ISBN: (纸本)9798350303759

Deep learning and computer vision (CV) can easily recognize pictures that the human doctor would have to spend a lot of time and labor to process. Thus, they have a wide range of applications in the field of ophthalmology, which is highly dependent on fundus images. However, training a robust and highly accurate model requires a large number of labeled high-quality images, which is often difficult for a single institution to provide. A natural solution is to use data from multiple medical centers to train the model. But due to privacy protection, data heterogeneity, and other problems, these data cannot be shared directly. Based on a real case of ophthalmic disease diagnosis with multiple data sources, this paper proposes solutions to the heterogeneity of data amount and features between datasets, establishes a heterogeneous federated learning framework, and applies federated learning to the diagnosis of multiple types of ophthalmic diseases for the first time. The experimental results show that federated learning can greatly improve the diagnostic accuracy of clients with less data, and the accuracy of clients with more data can also be slightly improved. The ablation experiment has demonstrated the effectiveness of the proposed method for handling data heterogeneity. Our research shows that through heterogeneous federated learning, we can use multi-source, non-independent-identical data to build accurate disease diagnosis models, and make clients who cannot obtain effective models get a practical level disease diagnosis model. © 2023 IEEE.

关键词： Diagnosis

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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

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Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Fault identification of rotating machinery based on multiscale convolution and deep residual networks 9

Fault identification of rotating machinery based on multisca...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wang, Huihui Fan, Tianqi Qiu, Sen Liu, Long School of Intelligence and Electronic Engineering Dalian Neusoft University of Information Dalian116023 China Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781510672802

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly available datasets, and the results demonstrate the excellent performance of the proposed method. © 2024 SPIE.

关键词： Convolution Feature extraction Deep learning Machine learning Vibration Data modeling Lithium Convolutional neural networks Data centers Signal processing

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Consensus control of Multi-Agent Systems Using Fault-Estimation-in-the-Loop:Dynamic Event-Triggered Case

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1440-1451页

作者： Yamei Ju Derui Ding Xiao He Qing-Long Han Guoliang Wei Shanghai Key Lab of Modern Optical System the Department of Control Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Automation BNRistTsinghua UniversityBeijing 100084China College of Science University of Shanghai for Science and TechnologyShanghai 200093China

The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication ***,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed ***,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability ***,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,***,a simulation result is employed to verify the usefulness of the proposed design framework.

关键词： Consensus control dynamic event-triggered protocol(DETP) fault compensation(FC) fault estimation multi-agent systems(MASs)

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Deep learning-based digital twin for intelligent predictive maintenance of rapier loom

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9409-9430页

作者： Xiao, Yanjun Li, Rui Zhao, Yue Wang, Xiaoliang Liu, Weiling Peng, Kai Wan, Feng School of Mechanical Engineering Hebei University of Technology Tianjin China Career Leader Intelligent Control Automation Company Jiangsu Province Suqian China Tianjin Key Lab Power Transmiss & Safety Technol Department State Key Lab Reliabil & Intellectual Elect Tianjin China

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from "regular maintenance" and "post-event maintenance" to "predictive maintenance". In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of "combining the real with the virtual and controlling the real". Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. © 2024 – IOS Press.

关键词： Forecasting

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