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Gearbox pump failure prognostics in offshore wind turbine by an integrated data-driven model

APPLIED ENERGY 2025年 380卷

作者： Zhang, Wanwan Vatn, Jorn Rasheed, Adil Norwegian Univ Sci & Technol Hogskoleringen 1 N-7034 Trondheim Norway

Offshore wind turbines face substantial challenges in operation and maintenance due to the harsh marine environment and remote locations. Predictive maintenance, encompassing fault diagnostics and failure prognostics, is a promising maintenance strategy to address these challenges. To contribute to this strategy, an integrated data-driven model is developed for probabilistic failure prognostics at the component level. The remaining useful life of a gearbox pump in an offshore wind turbine is predicted accurately based on supervisory control and data acquisition data. In this approach, light gradient boosting machines are tuned to model normal temperatures. The gated recurrent unit outperforms other neural networks and is selected to process temperature residuals with a Bayesian neural network. Results show that the prediction at the 50% percentile precedes the true failure time by 3.83 h. Moreover, there is 97.5% confidence that the true failure time falls within around +/- 5.3 h of the predicted time. Furthermore, the earliest alarm is issued at the 2.5% percentile, precisely 9.17 h prior to the true failure time. This study demonstrates the effectiveness of supervised learning and normal behavior modeling for probabilistic failure prognostics of offshore wind turbine components.

关键词： Remaining useful life Offshore wind turbine Gearbox pump failure Machine learning Temperature analysis Normal behavior modeling

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Double-Layer Woven Heating Fabrics: Design and Structure Optimization, Thermal modeling, and Implementation of an Intelligent Temperature control System

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FIBERS AND POLYMERS 2025年第5期26卷 2203-2220页

作者： Khachegini, Amirreza Alizade Moghaddam Ezazshahabi, Nazanin Tehran, Mohammad Amani Amirkabir Univ Technol Dept Text Engn Tehran Iran

The aim of this research is to present the optimal pattern structure of double-layer woven heating fabrics, to deliberately increase the heating efficiency of the fabric on one side and reduction of heating energy loss on the other side of the fabric. Besides, analysis was carried out to provide a thermal model for a more detailed examination of the performance of these textiles in terms of thermal characteristics. Moreover, implementation of an intelligent temperature control system was performed to steadily regulate the temperature, at a predetermined temperature, in various external environmental temperature conditions. To achieve this purpose, nine samples of double-layer woven fabrics made of polyester yarns, consisting of nickel-chromium element as a proportion of weft yarns, were designed and produced. The mentioned fabrics were different in terms of weave pattern, connecting stitch type, and element-thread ratio. Finally, the effect of various structural factors of these fabrics in terms of thermal insulation properties, the heat production, and also the transfer process, has been investigated and a thermal model is introduced and described. Also, an intelligent control system based on Arduino microcontroller was designed and optimized based on PID control logic, and the ability of the control system to adjust the temperature was examined. Finally, according to the analysis of the results, it was determined that the double-layer tubular fabric is the most appropriate sample to achieve the goals of this research. The finding is due to the complete separation of the two layers of fabric from each other, and as a result, significant air is trapped between the two layers of the fabric creating an insulation layer in between. Also, the presented thermal model had acceptable results compared to the experimental data, and the control system was successful in controlling the textile temperature in different conditions.

关键词： Smart textiles Heating fabric Multi-layer fabrics control logic Heat transfer modeling

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A Hybrid Fault Detection Method of Independent Component analysis and Auto-Associative Kernel Regression for process Monitoring in Power Plant

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IEEE ACCESS 2025年 13卷 39135-39151页

作者： Jung, Seunghwan Kim, Jonggeun Kim, Sungshin Pusan Natl Univ Dept Elect & Elect Engn Busan 46241 South Korea Korea Electrotechnol Res Inst Artificial Intelligence Res Ctr Chang Won 51543 South Korea

In complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. However, DCSs not only have the advantages of collecting large amounts of operational history data, but they also have the shortcoming of limited monitoring capabilities, such as early detection, due to their reliance on generating fault alarms based on simple threshold values. To improve the stability and reliability of industrial processes, it is essential to operate DCS in conjunction with data-driven process monitoring technologies. In this paper, we propose a novel hybrid model combining independent component analysis (ICA) and auto-associative kernel regression (AAKR) to address the limitations of both models. The proposed model (ICA+AAKR) introduces a new method, cumulative percentage distance (CPD), which can determine the appropriate number of independent components (ICs) for dimensionality reduction in ICA. By inputting the dimension-reduced IC matrix into AAKR, the issue of excessive computation time caused by lazy learning in AAKR is effectively mitigated. We applied the proposed fault detection method to two well-known benchmarks (multivariate dynamic process and Tennessee Eastman process) and a real-world application (actual tube leakage in power plant) to verify its monitoring performance. The experimental results validated superior detection performance compared to existing methods for the two benchmark problems. In addition, the method demonstrated the potential to enhance process stability and reliability by enabling remarkable early detection of tube leakage in a circulating fluidized bed boiler at the power plant.

关键词： Vectors Boilers Principal component analysis Kernel Covariance matrices Training Power generation Integrated circuit modeling Fault detection Benchmark testing Tube leakage circulating fluidized-bed boiler fault detection process monitoring independent component analysis auto-associative kernel regression

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UAV Autopilot Design for Entire Flight Profile with Cubature Kalman Filter

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年第3期26卷 1103-1130页

作者： Salahudden, Salahudden Roy, Anirban Chhetti, Shyam TATA Adv Syst Ltd Dept Nav Guidance & Flight Control Bengaluru 560064 Karnataka India

The autopilot design for the full flight envelope, starting from take-off to landing, is presented in this paper. The navigation, guidance, and flight control law models that make up the autopilot have been designed for use in unmanned aircraft. Through processor-in-loop simulation, the designed autopilot is put into action. Medium-altitude long-endurance unmanned aircraft (MALE) is being examined to demonstrate the proposed autopilot. The controller is put through a test against the wind. The flight control law in which the systematic and generalized autopilot design procedure is established with a hardware-lumped model. The design process comprises the assessment of the aircraft's linear and nonlinear responses, open-loop characteristics, transfer function design, root locus analysis with delay, control bandwidth analysis, time-domain analysis, control law implementation, and testing for the nonlinear model. The said procedure is repeated for other trim flight conditions. A novel Cubature Kalman filter (CKF)-based navigation algorithm has been proposed to process attitude heading reference systems (AHRS) solution information, primarily rates, attitudes, heading, geodetic position, and velocity data. The first filter, named complementary CKF is used to re-estimate aircraft attitudes and the second filter, named integrated CKF (I-CKF) is used to estimate aircraft position and velocities. Thus, the complete stages of a practical flight control system, starting from the aircraft model, total loop delay characterization, sensor and actuator modeling, controller design using successive loop closure method for navigation and landing flights, guidance, and navigation filters, are clearly explained in this paper.

关键词： UAV Dynamics PID Kalman filter Autopilot Flight envelope

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Near-Infrared Spectrum Calibration by Deep Dictionary Learning Regression With data Augmentation for In-Situ Moisture Measurement of Fluidized Bed Drying process

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Qin Liu, Tao Chen, Junghui Liu, Jingxiang Zhang, Qi Zhang, Hongbin Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Inst Adv Control Technol Dalian 116024 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China China Acad Engn Phys Inst Chem Mat Mianyang 621900 Sichuan Peoples R China Tech Univ Darmstadt Inst Mat Sci D-64287 Darmstadt Germany

For in-situ measurement of moisture content (MC) during fluidized bed drying (FBD) processes via the near-infrared (NIR) spectroscopy, a novel spectrum calibration method is proposed by developing a deep dictionary learning regression (DDLR) modeling approach based on sampled data augmentation (DA), to address the troublesome issues of very limited and uneven distributed samples along with spectral absorption nonlinearity in practice. The robust principal component analysis (RPCA) is combined with the synthetic minority oversampling technique (SMOTE) for DA, which could balance the distribution of all samples, eliminate the influence of random noise associated with SMOTE when introducing the augmented data, and sort out outliers in the synthesized virtual spectra and MC labels. The dictionary learning model is subtly extended to a deep dictionary learning (DDL) form in order to describe the latent features of measured spectra for model calibration. Experimental results on predicting the MC of silica gel granules during an FBD process demonstrate that the root-mean-square error (RMSE) could be significantly reduced by the proposed NIR calibration model, over 45% compared to traditional calibration modeling methods.

关键词： Calibration data models Moisture Spectroscopy Moisture measurement Predictive models data augmentation Training Temperature measurement Silicon dioxide data augmentation (DA) deep dictionary learning regression (DDLR) fluidized bed drying (FBD) in-situ measurement moisture content (MC) spectrum calibration

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Causality-Informed data-Driven Predictive control

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2025年

作者： Sader, Malika Wang, Yibo Huang, Dexian Shang, Chao Huang, Biao Beijing Informat Sci & Technol Univ Sch Automat Beijing 100084 Peoples R China Beijing Informat Sci & Technol Univ Beijing Key Lab High Dynam Nav Technol Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control (DDPC) is subject to bias-variance tradeoff and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control (SPC), we gain insight into the reason being the lack of causality as a main cause for their high variance of implicit prediction. In this brief, we derive a new causality-informed formulation of DDPC and its regularized form that balances between control cost minimization and implicit identification of a causal multistep predictor. Since the proposed causality-informed formulations only call for block-triangularization of a submatrix in the generic noncausal DDPC based on LQ factorization, our causality-informed formulation of DDPC enjoys computational efficiency. Its efficacy is investigated through numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.

关键词： Cause effect analysis Predictive control Uncertainty Predictive models Computational modeling control design Trajectory Stochastic processes Linear systems Faces Causality data-driven predictive control (DDPC) LQ factorization regularization subspace predictive control (SPC)

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Unified Low-Dimensional Subspace analysis of Continuous and Binary Variables for Industrial process Monitoring

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1135-1146页

作者： Chen, Junhao Zhao, Chunhui Song, Pengyu Xie, Min Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China City Univ Hong Kong Dept Syst Engn Hong Kong Peoples R China

Industrial data often consist of continuous variables (CVs) and binary variables (BVs), both of which provide crucial information about process operating conditions. Due to the coupling between industrial systems or equipment, these hybrid variables are usually high-dimensional and highly correlated. However, existing methods generally model hybrid variables directly in the observation space and assume independence between the variables to overcome the curse of dimensionality. Thus, they are ineffective at capturing dependencies among hybrid variables, and the effectiveness of process monitoring will be compromised. To overcome the limitations, this study proposes to seek a unified subspace for hybrid variables using the probabilistic latent variable (LV) model. By introducing a low-dimensional continuous LV, the proposed method can avoid the curse of dimensionality while capturing the dependencies between hybrid variables. Nevertheless, the inference of LV is analytically intractable and thus time-consuming due to the heterogeneity of CVs and BVs. To accelerate offline learning and online inference procedures, this study originally derives an analytical Gaussian distribution to approximate the true posterior distribution of the LV, based on which an efficient expectation-maximization algorithm is developed for parameter estimation. The Gaussian approximation is simultaneously optimized with the latest parameters to achieve a high approximation accuracy. The LV is then estimated by the posterior mean of the Gaussian approximation. By mapping the heterogeneous variables into a unified subspace, the proposed method defines three monitoring statistics, which are physically interpretable and thoroughly evaluate the probability of hybrid variables being normal. The effectiveness of the proposed method in detecting anomalies in CVs and BVs is shown through a numerically simulated case and a real industrial case.

关键词： Probabilistic logic process monitoring Feature extraction data models Analytical models Gaussian approximation Principal component analysis Parameter estimation data mining Computational modeling EM algorithm hybrid variable probabilistic latent variable (LV) model process monitoring

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SimMF: A Simplified Multi-Factor modeling Framework for Fine-Grained Urban Flow Inference

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IEEE SIGNAL processING LETTERS 2025年 32卷 1026-1030页

作者： Fan, Bingqian Pan, Lin Ren, Qianqian Lv, Xingfeng Heilongjiang Univ Dept Comp Sci & Technol Harbin 150080 Peoples R China

Fine-grained urban flow inference is pivotal in alleviating traffic congestion and reducing detector deployment costs. It aims to infer fine-grained flow maps from coarse-grained traffic data. However, existing methods face challenges due to the highly complex nature of spatial modeling for urban flow patterns and the distinctive impact of external factors such as temperature and weather. To address these issues, this paper proposes a Simplified Multi-Factor spatial modeling framework (SimMF) to enhance the accuracy of fine-grained flow inference while optimizing inference complexity. SimMF incorporates a dual-path architecture for short-range modeling, combining multi-scale convolutions and frequency-domain processing via FFT to capture cross-scale spatial correlations and heterogeneity. For long-range dependencies, SimMF employs enhanced bottleneck attention with linear complexity, effectively modeling intricate spatial relationships. Additionally, SimMF adopts a view-aware learnable approach to represent external factors, enabling each factor to generate distinctive feature maps and capture its unique characteristics. Experimental results on two urban datasets demonstrate that SimMF outperforms existing methods, achieving superior inference accuracy while maintaining computational efficiency with significantly improved computational efficiency.

关键词： Training Frequency-domain analysis Meteorology Feature extraction Correlation Computational modeling Vectors Temperature Traffic control Temperature dependence Urban flow inference graph attention traffic flow analysis urban systems

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process safety modeling and analysis method based on finite state machine 3

Process safety modeling and analysis method based on finite ...

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2024 3rd International conference on Aerospace and control Engineering, ICoACE 2024

作者： Zhao, Nuo Wu, Fengfeng Zhang, Yi Wang, Weizhe China Institute of Nuclear Industry Strategy Beijing100048 China

The cause of the accident comes from the unsafe state of the system, and the development process of the accident reflects the dynamic change of the system state in the process. Aiming at the problem of complex system and process safety analysis under the human-machine-environment coupling effect, this paper proposes a process safety modeling and simulation analysis method based on a finite state machine, which is based on functional structure analysis, mechanism analysis, workflow analysis, and fault logic analysis. The process safety simulation analysis is completed in the Simulink/Stateflow environment, and the effective identification of the potential accident chain is realized by using the efficient traversal algorithm. Finally, a case study is given to verify the effectiveness of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Accidents

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modeling test strain data reveal shear damage evolution characteristic of CFRP laminates

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年第1期32卷 1-14页

作者： Zou, Xionghui Shen, Zijie Liu, Wei Zhang, Yu Gao, Weicheng Zhou, Guangchun Wu, Zhenyu Harbin Inst Technol Dept Astronaut Sci & Mech Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Smart Prevent & Mitigat Civil Engn Disaste Minist Ind & Informat Technol Harbin Peoples R China

CFRP laminates are commonly used in aircraft structures due to their high structural efficiency. The phenomenon-based failures of CFRP laminates have poor predictability for the complex failure process with buckling and matrix fractures. This work carried out seven shear tests of CFRP laminates. It also attempts to reveal its failure characteristic points by directly modeling the measured strain data based on the relative deformation distribution catastrophe. The failure characteristic points are revealed by constructing the stressing state modes/parameters and applying the clustering analysis criterion, whose accuracy can be verified by comparing the shear failure characteristic points of nondestructive/impacted CFRP laminates.

关键词： CFRP laminates shear load test strain stressing state evolution clustering analysis criterion characteristic point

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