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A Novel Active Learning Kriging-Based reliability Analysis Method for Aero-Engine Gear

ASCE-ASME Journal of Risk and Uncertainty in engineering systems, Part B: Mechanical engineering 2025年第3期11卷 031208页

作者： Qian, Hua-Ming Huang, Haoliang Li, Yan-Feng Zeng, Ying Huang, Hong-Zhong School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Center for System Reliability and Safety University of Electronic Science and Technology of China Sichuan Chengdu611731 China

This paper proposes the active learning Kriging (ALK)-based reliability method for high-cycle fatigue reliability analysis of aero-engine gears. Uncertainties to affect the reliability of aero-engine gears are quantified with random variables, and the finite element simulation model of gears is refined to align with experimental data. Based on the Basquin equation, the S-N curve of the gear is fitted to the stress-life data obtained from experiments. The stress under given loads is obtained through simulation, and the corresponding life is derived from the S-N curve. Using the given permissible lifespan, the limit state function for gear fatigue reliability analysis is established. This function is then approximated using an active learning surrogate model, and the probability of failure is subsequently estimated. Furthermore, to enhance computational efficiency and accuracy, this paper reviews the origin of active learning strategy and defines an improvement function aimed at structural reliability analysis by drawing an analogy to the derivation process of the expected improvement (EI) learning function in the efficient global optimization (EGO) algorithm. Consequently, a novel learning function for active learning Kriging-based reliability analysis is derived. The application of this method to aero-engine gears made of 17CrNiMo6 steel verifies that it effectively enhances the efficiency of fatigue reliability analysis under ensuring a certain accuracy. Copyright © 2025 by ASME.

关键词： Active learning

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Domain-Specific Dual Network With Unsupervised Domain Adaptation for Transfer Fault Prognosis Across Machines Using Multiple Source Domains

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Khanal, Smaran Huang, Hong-Zhong Huang, Cheng-Geng Dahal, Amrit Huang, Tudi Niazi, Sajawal Gul University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Center for System Reliability and Safety Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Center for System Reliability and Safety Chengdu611731 China

Deep learning (DL)-based fault prognostic methods often require extensive training data, which can be challenging to obtain due to time, cost, or safety constraints. However, labeled data from related but distinct machines, such as from accelerated degradation experiments, can offer valuable insights. Most transfer learning (TL) methods focus on single-source domain adaptation (DA) within the same machine, overlooking variability within the source domain and missing the opportunity to leverage multisource historical data effectively for application on different machines lacking run-to-failure data. To address these limitations, a domain-specific dual network (DSDN) is proposed for transfer fault prognosis across heterogeneous machines using multisource domain data. The DSDN consists of two feature extraction modules: domain-specific degradation feature extraction (DSDFE) and domain-specific high-level feature extraction (DSHLFE). These modules incorporate a deep convolutional neural network (DCNN) enhanced with multiple convolutional block attention modules (CBAMs) and a multilayer perceptron (MLP). The outputs from modules are concatenated to generate domain-invariant representations, effectively capturing both time-series and time-frequency spectrum features. By incorporating the DA technique, this approach mitigates discrepancies between the source and target domains, enabling accurate remaining useful life (RUL). The proposed method enhances the extraction of robust domain-invariant features for each source-target domain pair separately, improving prognostic accuracy by mitigating negative transfer. The effectiveness of the DSDN is validated through extensive case studies on single-source and multisource bidirectional transfer fault prognosis tasks across machines, utilizing bearing datasets from the IEEE prognostic and health management (PHM) Challenge 2012 and XJTU-SY. A comparative analysis further highlights its superiority over existing methods. © 1963-2012

关键词： Convolutional neural networks

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Corrigendum to "Assessing Human Situation Awareness reliability Considering Fatigue and Mood Using EEG Data: A Bayesian Neural Network-Bayesian Network Approach" [reliability engineering & system Safety volume 260, (2025) 110962] (reliability engineering and system Safety (2025) 260, (S0951832025001656), (10.1016/***.2025.110962))

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reliability engineering and system Safety 2025年 260卷

作者： Ding, Song Hu, Lunhu Pan, Xing Zuo, Dujun Sun, Liuwang School of Reliability & Systems Engineering Beihang University Beijing China School of Mechanical Engineering Inner Mongolia University of Technology Mongolia Institute of System Engineering China Academy of Engineering Physics Mianyang China

The authors regret that the CRediT authorship contribution statement in the original article omitted the equal contribution note for the first two authors. The corrected statement should be: "Song Ding: Writing – original draft, Validation, Data curation. Lunhu Hu: Validation, Conceptualization. Xing Pan: Project administration, Funding acquisition, Conceptualization. Dujun Zuo: Writing – review & editing, Methodology. Liuwang Sun: Visualization, Software, Methodology. The first two authors, Song Ding and Lunhu Hu, contributed equally to this work." The authors would like to apologise for any inconvenience caused. © 2025

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Self-Triggered Stochastic MPC With Adaptive Prediction Horizon for Cloud-Based Connected Vehicles Subject to Chance Constraints

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Jicheng Zhang, Hui Beihang University School of Reliability and System Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Beihang University School of Transportation Science and Engineering Beijing100191 China

This work presents a novel self-triggered stochastic model predictive control (SMPC) scheme for cloud-based vehicle path following control system subject to chance constraints. First, we model the cloud-based vehicle path following control system from a networked stochastic control system perspective. Unlike the conventional periodical sampling approach, a self-triggered mechanism (STM) with adaptive prediction horizon is developed to determine the next sampling time instant and inter-sampling control inputs at each sampling time instant. This mechanism can efficiently reduce the data transmission frequency in the vehicle-cloud communication network, leading to a lower communication load and thus improving the reliability of the system. The STM comprises a set of optimization problems with an adaptive prediction horizon. The optimization problems and threshold design explicitly take the vehicle-cloud communication load into account. Furthermore, a stochastic model predictive control problem with modified constraint tightening and terminal constraint is defined by considering the influence of STM. We develop sufficient conditions to guarantee the closed-loop chance constraints satisfaction in the presence of both adaptive STM and additive disturbances. Then, the recursive feasibility of the optimal control problem and closed-loop stability of the system are investigated. Finally, we illustrate the benefits and effectiveness of the proposed method through numerical examples in vehicle path following control problem. © 1967-2012 IEEE.

关键词： Stochastic control systems

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Micro-convex structure-assisted laser-induced backside dry etching for high-efficiency fabrication of micro-groove array on fused silica toward manipulation of wetting characteristics

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Journal of Manufacturing Processes 2025年 145卷 236-251页

作者： Huang, Hu An, Hong Qian, Yongfeng Zhang, Zhiyu Jiang, Minqiang Yan, Jiwang Key Laboratory of CNC Equipment Reliability Ministry of Education School of Mechanical and Aerospace Engineering Electron Microscopy Center Jilin University Changchun130022 China Key Laboratory of Optical System Advanced Manufacturing Technology Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China State Key Laboratory of Nonlinear Mechanics Institute of Mechanics Chinese Academy of Sciences Beijing100190 China School of Engineering Science University of Chinese Academy of Sciences Beijing101408 China Department of Mechanical Engineering Faculty of Science and Technology Keio University Yokohama223-8522 Japan

Fused silica has garnered significant attention in various industrial applications. The creation of surface micro/nanostructures on fused silica can impart specialized functional properties, such as extreme wettability, ultra-high transmittance, and excellent antimicrobial characteristics. Nonetheless, existing technologies for achieving surface micro/nanopatterning of fused silica, including wire electrical discharge machining, chemical etching, and electrochemical deposition, generally suffer from long processing cycles and high energy consumption, which are inconsistent with the development concept of the low-carbon economy. In this study, a novel approach termed micro-convex structure-assisted laser-induced backside dry etching (MCSALIBDE) is proposed for high-efficiency fabrication of micro-groove array on fused silica surfaces. The synergistic effect of enhanced heat conduction and plasma explosion in a confined space facilitates material ablation. The impact of processing parameters including scanning pitch and peak laser power intensity on the morphological and topographical features of the MCSALIBDE-processed fused silica surfaces is studied. Notably, the micro-groove structures with a depth-to-width ratio of 1.19 is fabricated. Furthermore, the wetting behaviors of the MCSALIBDE-processed fused silica surfaces before and after annealing treatment are studied. Experimental findings reveal that the MCSALIBDE-processed fused silica surfaces exhibit enhanced hydrophilicity compared to the untreated one. After annealing treatment, a transformation from hydrophilicity to superhydrophobicity is observed. The superhydrophobic surfaces produced under different laser parameters exhibit distinct adhesion and droplet bouncing behaviors. This work offers an in-depth understanding of the high-efficiency fabrication of micro/nanostructures on fused silica surfaces and as well the modulation of their wetting characteristics, heralding innovations in surface engineering fo

关键词： Wetting

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A comprehensive belief reliability analysis method for electronic systems considering the effect of failure mechanisms

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reliability engineering and system Safety 2025年 261卷

作者： Wang, Yanfang Chen, Ying Li, Yingyi Kang, Rui School of Reliability and System Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beihang University Beijing100191 China Hangzhou International Innovation Institute Beihang University 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou311115 China

Complex electronic systems have been extensively employed in various engineering scenarios, necessitating the use of reasonable and effective methods for analyzing their reliability from the perspective of their performance to ensure smooth and secure operation. While numerous Physics of Failure (PoF)-based methods have identified and modeled failure causes for different system performances, there is still a lack of discussion on the direct correlation between damage caused by failure mechanisms and corresponding performance, as well as consideration of comprehensive performance requirements when analyzing system reliability. To address these issues, this paper proposes a comprehensive belief reliability analysis method for electronic systems to establish a connection between the failure mechanism and performance parameters. An S-O-P (Structure-overload-performance) belief reliability framework has been provided to analyze the reliability of complex electronic systems based on the proposed definitions of structure-related reliability, overload-related reliability and performance reliability. To implement the framework, an Improved Hybrid Bond Graph (IHBG) method is studied, and the interactions and uncertainties of the failure process are quantified. Furthermore, a delay-trigger electronic controller is utilized as an example to demonstrate the effectiveness and rationality of the proposed reliability analysis method. © 2025

关键词： Belief reliability Electronic system Failure mechanism Performance Physics of failure

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An Asymmetric Arc-Shaped Coupler–Based WPT Design for High Misalignment Tolerance Charging of AUVs

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International Journal of Circuit Theory and Applications 2025年

作者： Deng, Shuming Cai, Changsong Wang, Junhua Jiang, Yufeng Zhang, Xian Wan, Leke Wu, Xi Du, Yinfeng Zhou, Yingguo Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan University Hubei Wuhan China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China School of Materials Science and Engineering Jiangsu University of Science and Technology Zhenjiang China

In contact with a wide area of seawater, the use of a typical planar coil structure generates the problem of large fluctuations of the overall efficiency and power when the system encounters environmental disturbances. In order to improve the misalignment tolerance of the system, this article proposes a high misalignment tolerance WPT system with constant current output based on the asymmetric arc-shaped coupler and LCC-P compensation topology. This design effectively increases the horizontal and rotation misalignment tolerance, so that the fluctuation of the overall efficiency and power of the system greatly declines when coming across disturbances. The article firstly establishes the equivalent circuit model and deduces the equations about parameter configuration and the output characteristics of the system. Furthermore, the asymmetric arc-shaped coupler is designed, and the mutual inductance fluctuation of the coupler is verified to be within 8.7% through simulation. Finally, a 500-W-level experimental prototype based on the WPT design is built and tested to verify the performance and effectiveness. The prototype achieves stable performance with an output power about 580 W and an 85% overall efficiency when the horizontal misalignment distance varies from 0 to 40 mm and the misalignment angle varies from 0 to 35°. © 2025 John Wiley & Sons Ltd.

关键词： Autonomous underwater vehicles

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Two-Stage Stochastic Resilience Optimization of Converter Stations Under Uncertain Mainshock-Aftershock Sequences

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IEEE Transactions on Power systems 2025年

作者： Wang, Kai Xue, Zhihang Cao, Di Liu, Yu Fang, Yi-Ping University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Center for System Reliability and Safety Chengdu611731 China Université Paris-Saclay Laboratoire Génie Industriel CentraleSupélec Gif-sur-Yvette91190 France

Reducing vulnerability and enhancing resilience of power systems, particularly converter stations, against earthquakes are crucial for maintaining their safe operation. Nevertheless, earthquakes are often accompanied by aftershocks and encompass plenty of uncertainties, posing significant challenges for the development of a joint pre-earthquake preparation and post-earthquake restoration strategy. In this article, a novel two-stage stochastic programming model is put forth to bolster the resilience of converter stations under uncertainties associated with mainshock-aftershock sequences. The model is bifurcated into two pillars: the first stage focuses on designing equipment hardening strategy (EHS) and spare parts strategy (SPS) for converter stations before earthquakes. The second stage is dedicated to the optimization of recovery scheduling (RS) in the wake of mainshock-aftershock sequences. To mitigate the endogenous uncertainties of the model, a tolerated random number generation method is employed to generate deterministic scenarios in the second stage. The resulting optimization model is, then, efficiently solved using a combination of the sample average approximation and progressive hedging algorithm. To evaluate the performance of the proposed resilience optimization method, comparative studies are carried out on a real ±800 kV converter station located in the city of Yibin, Sichuan Province, P.R. China. Results from this application demonstrate that the EHS pre-dominantly focuses on hardening the equipment of the same circuit to guarantee the low transmission capacity under diverse earthquake scenarios, while the SPS is proven to be more effective than the EHS in scenarios with high values of peak ground acceleration (PGA) of mainshock and occurrence intensity of aftershocks. © 1969-2012 IEEE.

关键词： Converter station endogenous uncertainty mainshock-aftershock sequences resilience stochastic programming

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Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis

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Journal of Central South University 2012年第2期19卷 459-464页

作者：高成黄姣英孙悦刁胜龙 School of Reliability and System Engineering Beihang University

A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.

关键词： non-linear circuits fault diagnosis relevance vector machine particle swarm optimization kurtosis entropy

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Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

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Journal of Central South University 2012年第5期19卷 1302-1310页

作者：黄姣英高成崔嵬梅亮 School of Reliability and System Engineering Beihang University

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

关键词： accelerated degradation test capacitor multiple degradation measure particle swarm optimization grey model

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