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A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

APPLIED INTELLIGENCE 2023年第19期53卷 22682-22699页

作者： Wang, Youming Wang, Yue Xian Univ Posts & Telecommun Sch Automation Xian Key Lab Adv Control & Intelligent Proc Xian 710121 Peoples R China

Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction of maintenance costs for turbofan engine systems. However, most of the existing methods capture temporal or spatial features to predict RUL, which leads to the neglect of deep spatio-temporal correlation in the prediction data. In addition, there are usually noise interference and few labels in feature extraction, which causes difficulty in designing robust RUL prediction models. To address these problems, a denoised semi-supervised model based on fully convolutional denoising autoencoder, convolutional neural network, and long short-term memory network (FCDAE-CNN-LSTM) is proposed to predict RUL, where FCDAE is constructed to denoise the original data by parameter fine-tuning and CNN-LSTM is established based on parallel connection for RUL prognosis. The denoising and generalization capabilities of the RUL prediction model are enhanced by the combination of unsupervised denoising and supervised feature extraction in the case of few labeled training data. The superior features of the method lie in that FCDAE effectively captures globalized and localized features and CNN-LSTM captures multi-layer fused spatial-temporal correlations. The Root Mean Square Error (RMSE) and Score of the proposed method on C-MAPSS dataset are 12.01, 16.62, 11.84, 18.21, and 209.44, 1466.03, 205.07, 2338.93, respectively, which have demonstrated that our method achieves the state-of-the-art performance and outperforms other models.

关键词： Convolutional neural network Denoising autoencoder Long short-term memory network Remaining useful life Semi-supervised deep learning

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Consensus of discrete-time heterogeneous multi-agent systems in noisy environments without velocity transmission

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ASIAN JOURNAL OF control 2025年第0期

作者： Chen, Jiali Zong, Xiaofeng Hou, Xuping Wang, Zi-Xuan China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automation Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol GeoExplorat Wuhan Peoples R China

This paper investigates consensus control of discrete-time heterogeneous multi-agent systems with multiplicative noise. As the frequent absence of velocity sensors in real industrial environments, a control protocol is developed using absolute velocity and relative position measurement information. Then, the consensus control issue is reformulated as the stability issue of stochastic difference equations. By choosing an appropriate Lyapunov function, the above stability issue is studied, and the sufficient conditions for leader-free and leader-following discrete-time heterogeneous multi-agent systems to achieve both mean-square and almost-sure consensus are derived, respectively. Finally, numerical simulations are provided to validate the effectiveness of the main results.

关键词： consensus discrete-time heterogeneous multi-agent systems leader-following leader-free multiplicative noise

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A matrix-separation-based integral inequality for aperiodic sampled-data synchronization of delayed neural networks considering communication delay

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APPLIED MATHEMATICS AND COMPUTATION 2025年 486卷

作者： Wang, H. -Z Shangguan, X. -C Xiong, D. An, Y. -H Jin, L. China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automation Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

This paper achieves the synchronization of delayed neural networks (DNNs) considering aperiodic sampled-data control and communication delay. First of all, based on the master- slave DNNs with aperiodic sampling synchronization controller, a synchronization error system is constructed. Then, an augmented functional containing both the error state and its derivative is constructed. Compared with the existing researches, the augmented functional introduces more cross information of error states to the criterion. Next, an integral inequality based on the separation of internal integral variable and matrix is developed. Compared to the inequalities that treat the internal variable and the matrix as unified ones, the developed inequality provides a tighter estimate of the derivative of the augmented functional. On this basis, a criterion with less conservative is developed for the aperiodic sampled-data synchronization of DNNs considering communication delay. Finally, to indicate the superiority of the developed method on improving the acceptable sampling upper bound of synchronization, three numerical examples are provided.

关键词： Delayed neural networks Master-slave synchronization Aperiodic sampled-data control Matrix-separation-based integral inequality

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Unit load prediction method based on weighted just-in-time learning with spatio-temporal characteristics for gas boiler power generation process

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control ENGINEERING PRACTICE 2025年 160卷

作者： Xu, Yan Wu, Min Hu, Jie Du, Sheng Zhang, Wen Peng, Fusheng Li, Huihang Song, Wenshuo China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automation Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Load management of gas boiler generator units in metallurgical captive power plants heavily relies on operators' coordination and scheduling. The operating load fluctuates frequently due to varying electricity demands across production processes and its sensitivity to multiple operational parameters. To accurately predict unit load, promptly reflect load changes, and ensure stable operation, we propose a unit load prediction model utilizing a weighted just-in-time learning algorithm with consideration of key parameter spatio-temporal characteristics (WJITL-ST) and a long short-term memory network with temporal pattern attention (TPA-LSTM) mechanism. First, we thoroughly analyze the process mechanism and use the maximum information coefficient to identify and select the variables most relevant to unit load as model inputs. Next, the WJITL-ST method, based on data segment retrieval, selects historical data most similar to the retrieved segments for online local modeling. The TPA-LSTM algorithm is then used to model and predict unit load. Finally, experiments using actual production data from a 150MW gas boiler generator unit in a metallurgical captive power plant show that the proposed method achieves higher prediction accuracy, and demonstrates superior performance under fluctuating operating conditions.

关键词： Power generation process Unit load prediction Weighted just-in-time learning Attention mechanism

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A Dynamic Tuning Decision-Making Model Using Multi-Feature Fusion

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 601-605页

作者： Guo, Linwei Cao, Weihua Bi, Leyu Hu, Wenkai Yuan, Yan Wu, Min China Univ Geosci Sch Automation Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automation Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Dielectric Filters (DFs) are tuned to meet the requirements of 5G communication systems. The tuning methods based on Tuning Decision-Making Models (TDMMs) are efficient. However, how to build TDMMs accurately and fast is rather difficult due to the complex performance representation and the poor consistency of DFs. In this brief, a dynamic tuning decision-making model using multi-feature fusion is proposed. The contributions of this brief are as follows: 1) For the problem of incomplete feature representation of DFs performance, the multiple features are fused to improve the accuracy of TDMMs;2) to deal with the problem that high-quality samples are challenging to obtain, a guided sampling method is designed to determine the sampling range, driven by the characteristics of DFs;3) to solve the problem of inefficient modeling for inconsistent DFs, TDMMs are built dynamically using transfer learning. Finally, the excellent performance of the proposed method is demonstrated through comparative simulations.

关键词： Tuning Feature extraction Dielectrics Decision making Training Permittivity Computational modeling Dielectric filters decision-making models sampling multi-feature fusion dynamic modeling

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Efficient Mobile Deployment of Lightweight CNN-Transformer Model for Crop Disease Diagnosis in Smart Agriculture 4

Efficient Mobile Deployment of Lightweight CNN-Transformer M...

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4th Asia Conference on Information Engineering, ACIE 2024

作者： Lv, Zirong China University of Geosciences Hubei Key Lab. of Adv. Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350371925

Crop disease diagnosis is of great significance for crop yield and agricultural production. Deep learning methods have become a major research direction for solving crop disease diagnosis. However, recent studies have shown that deploying complex neural network models on mobile devices poses a significant challenge to mobile hardware performance. To address this issue, this paper proposes a lightweight network model based on the Transformer and deploys it on mobile devices. Compared to other lightweight network models, our approach significantly reduces computational complexity using Transformer modules and exhibits a particular advantage in recognition accuracy. It achieves a recognition rate of 97.76% for ten categories of tomato diseases, while the model size is only 3.86 MB. Furthermore, we deploy the model on embedded devices and apply it to tomato crop recognition, enabling farmers to monitor crop conditions in real-time and realizing smart agriculture. © 2024 IEEE.

关键词： Deep learning

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An Improved Dynamic Optimization control System for the Drilling Rate of Penetration (ROP) and Its Industrial Application

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第6期70卷 6201-6208页

作者： Gan, Chao Cao, Wei-Hua Wang, Lu-Zhao Liu, Kang-Zhi Wu, Min China Univ Geosci Sch Automation Wuhan 430074 Peoples R China Key Lab Adv Control & Intelligent Automation Compl Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China No 3 Explorat Inst Geol & Mineral Resources Shando Yantai 264000 Peoples R China Chiba Univ Dept Elect & Elec tron Engn Chiba 2638522 Japan

Optimization control of the rate of penetration (ROP) is crucial in the drilling process, due to its vital role in improving the drilling efficiency and safety. In this article, an improved dynamic optimization control system for ROP is proposed and successfully applied to a drilling site. First, a three layers (i.e., intelligent optimization layer, basic automation layer, and process monitoring layer) system framework is proposed according to the drilling characteristics. After that, "if-then " strategy is used as the method to identify the rotary drilling condition from four different working conditions. A moving window strategy is selected to establish the dynamic ROP model that can better adapt to environmental changes. Moreover, a Jaya algorithm, a new meta-heuristic optimization method, is introduced to solve the dynamic ROP optimization issue. In the simulations, the optimization performance of the proposed system is better than the moving window-extreme learning machine-hybrid bat algorithm with more stable (fewer switch times) drilling operational parameters. Finally, in an industrial application to a drilling process in Dandong area, Northeast China, the ROP is improved by 20.79% compared with manual operation. Both simulation and industrial application results indicate that the proposed system has significantly improved the drilling efficiency and safety.

关键词： Drilling Optimization Optimization methods Heuristic algorithms control systems automation Employee welfare Drilling efficiency drilling process dynamic optimization optimization control rate of penetration (ROP)

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Improved Exponential Stability for Delayed Neural Networks With Large Delay Based on Relaxed Piecewise Lyapunov-Krasovskii Functional

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第7期70卷 2510-2514页

作者： Fan, Yu-Long Xu, Jin-Meng Zhang, Chuan-Ke Liu, Yunfan He, Yong China Univ Geosci Sch Automat Hubei Key Lab Adv Control & Intelligent Automation Wuhan 430074 Peoples R China China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China

In this brief, the stability of neural networks with switching between small and large time delays is studied by developing an improved exponential stability criterion. Firstly, the delayed neural network (DNN) with small delay (SD) and large delay (LD) is modeled as a switched DNN. Then, based on an augmented piecewise Lyapunov-Krasovskii functional with LD-based terms considering relaxed switching constraints, and Wiritinger-based inequality, a stability criterion with less conservatism is developed. Finally, a numerical example is provided to demonstrate the superiority and effectiveness of the proposed method.

关键词： Exponential stability delayed neural networks switching large delay

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Asynchronous sampled-data control for connected vehicles with heterogeneous delays

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ISA TRANSACTIONS 2024年 146卷 127-141页

作者： Wen, Shixi Guo, Ge Dalian Univ Sch Informat Engn Dalian 116622 Peoples R China Dalian Univ Key Lab Adv Design & Intelligent Comp Minist Educ Dalian 116622 Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China

This study investigates an asynchronous sampled -data control problem of vehicular platoons, with heterogeneous sampling, subjected to actuator delays. Without a synchronized clock, a completely asynchronous sampled -data controller is designed for each follower, where the state of the ith follower itself and its neighboring vehicles are sampled at their own sampling time instants. The caused closed -loop tracking error dynamics for the entire platoon considering the effect of the nonuniform sampling time intervals, heterogeneous vehicle dynamics, inter -vehicle topology and heterogeneous time delays. To simplify the stability analysis and controller design, the tracking -error dynamics of the entire platoon are decomposed into individual subsystems with reduced -order dynamics. Based on Lyapunov stability theory, the optimal conditions are explored to design an asynchronous sampled -data controller to guarantee the desired stability performance. Moreover, the exact values for the maximum allowable sampling interval and time delay are calculated for each follower using the designed feedback controller gain. The proposed asynchronous sampled -data control method is extended to a vehicular platoon using an event -based sampling scheme. Numerical examples are used to verify the effectiveness of the proposed sampled -data control method.

关键词： Asynchronous sampled-data control Connected vehicles Heterogeneous time delays Undirected inter-vehicle communication topology String stability

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Implementation of adaptive fault-tolerant tracking control for robot manipulators with integral sliding mode

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第10期33卷 5337-5364页

作者： Liu, Linzhi Zhang, Liyin Hou, Yinlong Tang, Dafeng Liu, Hui Xian Univ Posts & Telecommun Sch Automation Xian Key Lab Adv Control & Intelligent Proc Xian Shaanxi Peoples R China Xian Dong Yi Sci Technol & Ind Grp Co Ltd China State Shipbuilding Corp Ltd Xian Shaanxi Peoples R China

This article presents an adaptive fault-tolerant tracking control for uncertain robot manipulators. By fully considering the effects of uncertainties and actuator effectiveness faults, an integral sliding manifold with a novel auxiliary function is first proposed for tracking control of robot manipulators. Then, a novel fault-tolerant tracking control based on the proposed integral sliding manifold is developed to acquire robustness in both reaching and sliding phase. In order to further reduce the effects of uncertainties on tracking performance, an adaptive law is performed to estimate unknown parameters of the upper bounded uncertainties, which further reduces the control torque input in case of the similar tracking performance. The stability of the proposed approaches is accomplished by Lyapunov stable theory and homogeneous technique. The key contributions of the proposed approach are as follows: (i) the reaching phase of sliding mode control is removed completely in the control design and then the tracking system enters the sliding mode from the initial states;(ii) the nominal control term is eliminated in the design of integral sliding surface and then the algebraic loop problem is also avoided in the proposed approach for robot manipulators;(iii) the simple control structure with an adaptive law is obtained better tracking performance by utilizing lower control torque input and then the effects of time-delay and computational burden are also restrained from the proposed approach. Simulation and experimental comparisons have been accomplished for verifying the effectiveness of the proposed approach.

关键词： actuator effectiveness faults adaptive control fault-tolerant control homogeneous technique integral sliding mode control robot manipulators

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