Article
Computer Science, Artificial Intelligence
Xiang Li, Wei Zhang, Hui Ma, Zhong Luo, Xu Li
Summary: This paper proposes a deep learning-based RUL prediction method, which aligns the data of different entities in similar degradation levels through a cycle-consistent learning scheme to improve prediction performance. Experimental results suggest that the method offers a novel perspective on RUL estimations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Xiuli Wang, Bin Jiang, Shaomin Wu, Ningyun Lu, Steven X. Ding
Summary: This article proposes a degradation path-based RUL prediction framework using a dynamic multivariate relevance vector regression model to handle degradation modeling and remaining useful life (RUL) prediction. The approach considers the multivariate environment and introduces a matrix Gaussian distribution-based RVR method and Nesterov's accelerated gradient method to estimate hyperparameters and avoid exhausting re-estimation. The RUL is predicted based on the forecasted degradation path using the first hitting time method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Xuerui Cao, Kaixiang Peng
Summary: This article proposes a novel framework for degradation model and remaining useful life (RUL) prediction, taking into account both the aleatory uncertainty and epistemic uncertainty. The probability theory and uncertainty theory are used to establish a stochastic uncertain degradation model. A new stochastic uncertain maximum likelihood estimation (SUMLE) method is proposed to identify model parameters. Bayesian inference is used to update the model parameters. The proposed method is demonstrated to outperform methods based solely on stochastic process or uncertain process for RUL prediction through experimental studies on gallium arsenide (GaAs) laser and gyroscope degradation data.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Mechanical
Feng Yang, Mohamed Salahuddin Habibullah, Yan Shen
Summary: This paper proposed a generic prognostics framework with HI dynamic smoothing and multi-model ensemble realization, which enables the incorporation of different types of HI degradations. Experimental studies on real data from 8 induction motors showed that the proposed prognostic method using nonlinearly degrading HI resulted in clear performance improvements compared to linear HI degradation prediction.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Physical
Yu Hui Lui, Meng Li, Austin Downey, Sheng Shen, Venkat Pavan Nemani, Hui Ye, Collette VanElzen, Gaurav Jain, Shan Hu, Simon Laflamme, Chao Hu
Summary: The proposed physics-based approach for predicting the remaining useful life of implantable-grade lithium-ion batteries considers multiple degradation mechanisms and provides a more accurate prediction compared to traditional capacity-based approaches.
JOURNAL OF POWER SOURCES
(2021)
Article
Engineering, Industrial
Pengfei Wen, Shuai Zhao, Shaowei Chen, Yong Li
Summary: This paper proposes a generalized RUL prediction method for complex systems with multiple CM signals, along with two desirable properties of HI and a fusion method based on Genetic Programming to construct a superior composite HI. This approach enhances the prediction capability of multiple CM signals.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Chemistry, Analytical
Haifeng Guo, Aidong Xu, Kai Wang, Yue Sun, Xiaojia Han, Seung Ho Hong, Mengmeng Yu
Summary: This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils, proposing a health indicator and prediction method based on insulation degradation characteristics. Experimental validation of the predictive performance was carried out, offering new insights for predictive maintenance of systems with coils.
Article
Automation & Control Systems
Milad Rezamand, Mojtaba Kordestani, Marcos E. Orchard, Rupp Carriveau, David S-K Ting, Mehrdad Saif
Summary: A hybrid prognostic method using SCADA and vibration signals is introduced to predict the remaining useful life of wind turbine bearings. Experimental data validation shows higher RUL accuracy compared to the Bayesian algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Maximilian Benker, Artem Bliznyuk, Michael F. Zaeh
Summary: The quality of RUL estimation is crucial for predictive maintenance strategies, with deep learning methods often relying on large amounts of failure data. This paper introduces a data-efficient approach that can estimate RUL without the need for complete failure sequence data.
Article
Engineering, Multidisciplinary
Elio Chiodo, Pasquale De Falco, Luigi Pio Di Noia
Summary: In this article, a hybrid methodology is developed to characterize the remaining useful life (RUL) of lithium-ion batteries even with limited data. The methodology considers several probability distributions and uses expectation-maximization algorithms for parameter estimation. The proposed approach is tested on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model. Numerical experiments are reported to evaluate the effectiveness of the proposal.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Review
Automation & Control Systems
Bin Xue, Huangyang Xu, Xing Huang, Ke Zhu, Zhongbin Xu, Hao Pei
Summary: This paper reviews the entire procedure of Similarity-based prediction (SBP) methods, including the industrial scenarios with limited failure data and sufficient failure data, the construction of degradation indicators (DIs), the utilization of similarity calculation and matching rule, the acquisition of point estimation and uncertainty management, and the discussion of the effectiveness, limitations, and future challenges of SBP methods.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Jaeyeon Jang, Chang Ouk Kim
Summary: In this article, a novel health representation learning method based on a Siamese network is proposed to prevent overfitting and enable robust Remaining Useful Life (RUL) prediction.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Leonardo Ramos Rodrigues, Takashi Yoneyama
Summary: This paper proposed a novel repair priority rule based on a Prognostics and Health Monitoring system, and numerical experiments showed that it consistently reduces inventory system cost.
Article
Computer Science, Hardware & Architecture
Shuyi Zhang, Qingqing Zhai, Xin Shi, Xuejuan Liu
Summary: This article proposes a nonlinear Wiener process model with a random time-varying covariate to capture the degradation patterns and predict the remaining useful life (RUL) of industrial products. The model considers the dynamic environmental impacts and estimates the parameters using maximum likelihood estimation. A simulation-based procedure is also proposed to obtain the RUL. The model is verified using Monte Carlo simulations and outperforms existing models in fitting real data and predicting RUL.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Multidisciplinary
Xieyi Chen, Yi Wang, Haoran Sun, Hulin Ruan, Yi Qin, Baoping Tang
Summary: Gear is crucial for mechanical equipment, and its health directly influences the overall operation of the equipment. Therefore, accurately predicting the remaining useful life (RUL) of gearboxes is of great significance. However, current deep learning-based RUL prediction methods often overlook trend characteristics and focus on the fluctuation patterns of degradation data. To address this issue, a generalized degradation tendency tracking strategy (GDTTS) is proposed to improve the prediction performance by capturing both trend and fluctuation characteristics. Experimental results on real gearbox datasets demonstrate the effectiveness of the proposed strategy.
Article
Thermodynamics
Quan Xia, Yi Ren, Zili Wang, Dezhen Yang, Peiyu Yan, Zeyu Wu, Bo Sun, Qiang Feng, Cheng Qian
Summary: The thermal safety of lithium-ion batteries is crucial for their further development and application. Existing deterministic analyses struggle to accurately evaluate the risk probability due to the coupling effect of various factors. To address this, a safety risk assessment method and an improved bisection-method-based analysis algorithm for the thermal safety boundary are proposed. By considering an equivalent circuit, thermal abuse, and fluid dynamics, a multiphysics model is developed, along with stochastic models for battery parameters and loading to account for randomness. The integration of temperature and power stress-strength interference models enables the evaluation of thermal safety risk for battery packs. Results from case studies show that internal resistance dispersion has a dominant effect and the risk probability increases with degradation.
Article
Automation & Control Systems
Bo Sun, Zeyu Wu, Qiang Feng, Zili Wang, Yi Ren, Dezhen Yang, Quan Xia
Summary: This article proposes a novel autoaugmentation network to address the problem of limited time-series data. The network generates synthetic data that carry realistic patterns and expands a small sample without prior knowledge for reliability evaluation. Experimental results in lithium battery cells demonstrate the breakthrough achieved by this method in online reliability assessment.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Chemistry, Physical
Bo Sun, Junlin Pan, Zeyu Wu, Quan Xia, Zili Wang, Yi Ren, Dezhen Yang, Xing Guo, Qiang Feng
Summary: This paper proposes an adaptive evolution enhanced physics-informed neural network-based time-variant health prognosis framework for lithium-ion batteries. It uses a long short-term memory neural network model with a dynamic sliding window, informed by physical information derived from simulation, to predict the health status and remaining useful life of the batteries. The proposed method provides high prognosis accuracy under different conditions and improves accuracy through adaptive model evolution during long-term operations.
JOURNAL OF POWER SOURCES
(2023)
Article
Engineering, Industrial
Yue Zhang, Qiang Feng, Dongming Fan, Yi Ren, Bo Sun, Dezhen Yang, Zili Wang
Summary: Construction and optimization of maritime support networks with relays have been widely studied for their impact on maritime economy and national interests. This study proposes a two-stage stochastic optimization framework for these networks to improve total revenues under uncertain risk scenarios. A novel matheuristics method, which combines metaheuristics and mathematical programming techniques, is proposed as a general algorithm framework. The proposed method is shown to be robust and efficient through benchmark tests compared to exact and heuristic algorithms.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Hardware & Architecture
Meng Liu, Qiang Feng, Dongming Fan, Hongyan Dui, Bo Sun, Yi Ren, Dezhen Yang, Zili Wang
Summary: This article proposes a new method that combines resilience importance measure and enhanced PIO to optimize system resilience. By optimizing the recovery sequence and task assignment, the method aims to improve system resilience. The study demonstrates the effectiveness of the proposed method through the recovery of a network system, showing a significant reduction in system resilience loss compared to stochastic and traditional PIO methods.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Industrial
Xiaoyan Shao, Baoping Cai, Yonghong Liu, Junyan Zhang, Zhongfei Sui, Qiang Feng
Summary: A novel hybrid model-data-driven RUL prediction method based on a fusion of Kalman filter and dynamic Bayesian network is proposed in this paper. The method improves accuracy by enhancing the performance of observation values through DBN and considering estimation error and observation error. The uncertainty distribution of degradation parameters and environmental parameters is integrated into the state estimation model. Numerical simulation of a subsea Christmas tree valves demonstrates the advantages of the proposed RUL prediction method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Baoping Cai, Chaoyang Sheng, Chuntan Gao, Yonghong Liu, Mingwei Shi, Zengkai Liu, Qiang Feng, Guijie Liu
Summary: This paper proposes an enhanced artificial intelligence reliability assessment method by combining Bayesian neural networks and differential evolution algorithms. By fusing small samples and prior information, a reliability assessment model is constructed and reconstructed using virtual samples. The experimental results demonstrate that this method can accurately evaluate the reliability life of a product and improve the accuracy of the assessment.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Civil
Xingshuo Hai, Huaxin Qiu, Changyun Wen, Qiang Feng
Summary: This paper presents a novel approach to address the problem of distributed situation awareness consensus for unmanned aerial vehicle swarm systems. Due to the complexity and antagonism of the mission environment, traditional centralized architectures fail to achieve distributed consensus. To tackle this issue, a systematic distributed SA consensus scheme is proposed, including a distributed optimization-based consensus reaching model, dual-loop decision-making framework, and novel coordination algorithm. Convergence analysis is conducted on the proposed algorithm, and comparative simulations confirm the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Zeyu Wu, Bo Sun, Qiang Feng, Zili Wang, Junlin Pan
Summary: Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Chemistry, Analytical
Qilong Wu, Zitao Geng, Yi Ren, Qiang Feng, Jilong Zhong
Summary: This paper presents a deep reinforcement learning-based distributed reconfiguration strategy for optimizing the redeployment of multi-UAVs, aiming to improve swarm performance. By developing a multi-agent deep reinforcement learning framework, a two-layer reconfiguration between the swarm and single groups is achieved. The effectiveness of the proposed method as a high-quality reconfiguration strategy for large-scale scenarios is demonstrated through Python simulations and a case study.
Article
Ergonomics
Lulu Jia, Dezhen Yang, Yi Ren, Cheng Qian, Qiang Feng, Bo Sun, Zili Wang
Summary: This paper proposes a dynamic test scenario generation method for comprehensive evaluation of autonomous vehicles. By simulating the interaction process between the autonomous vehicle and environmental vehicles, the method can test the autonomous vehicle's ability to cope with dynamic scenarios and reveal its weaknesses.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Proceedings Paper
Engineering, Electrical & Electronic
Ran Zhang, Xinyu Zhang, Lei Guo, Qiang Feng, Xinguang Zhang
Summary: This research analyzes the influence of the phase switched surface on frequency shift pull-off jamming and verifies its effectiveness through simulation and analysis. The results show that the phase switched surface can generate false target echoes, achieving the goal of pulling away and jamming the real target.
2022 IEEE MTT-S INTERNATIONAL MICROWAVE WORKSHOP SERIES ON ADVANCED MATERIALS AND PROCESSES FOR RF AND THZ APPLICATIONS, IMWS-AMP
(2022)
Article
Engineering, Multidisciplinary
Sicheng Jiao, Shixiang Wang, Minge Gao, Min Xu
Summary: This paper presents a non-contact method of thickness measurement for thin-walled rotary shell parts based on a chromatic confocal sensor. The method involves using a flip method to obtain surface profiles from both sides of the workpiece, measuring the decentration and tilt errors of the workpiece using a centering system, establishing a unified reference coordinate system, reconstructing the external and internal surface profiles, and calculating the thickness. Experimental results show that the method can accurately measure the thickness of a sapphire spherical shell workpiece and is consistent with measurements of other materials.
Article
Engineering, Multidisciplinary
Rajeev Kumar, Sajal Agarwal, Sarika Pal, Alka Verma, Yogendra Kumar Prajapati
Summary: This study evaluated the performance of a CaF2-Ag-MXene-based surface plasmon resonance (SPR) sensor at different wavelengths. The results showed that the sensor achieved the maximum sensitivity at a wavelength of 532 nm, and higher sensitivities were obtained at shorter wavelengths at the expense of detection accuracy.
Article
Engineering, Multidisciplinary
Attilio Di Nisio, Gregorio Andria, Francesco Adamo, Daniel Lotano, Filippo Attivissimo
Summary: Capacitive sensing is a widely used technique for a variety of applications, including avionics. However, current industry standard Capacitive Level Sensors (CLSs) used in helicopters perform poorly in terms of sensitivity and dynamic characteristics. In this study, novel geometries were explored and three prototypes were built and tested. Experimental validation showed that the new design featuring a helicoidal slit along the external electrode of the cylindrical probe improved sensitivity, response time, and linearity.
Article
Engineering, Multidisciplinary
Kai Yang, Huiqin Wang, Ke Wang, Fengchen Chen
Summary: This paper proposes an effective measurement method for dynamic compaction construction based on time series model, which enables real-time monitoring and measurement of anomalies and important construction parameters through simulating motion state transformation and running time estimation.
Article
Engineering, Multidisciplinary
Hui Fu, Qinghua Song, Jixiang Gong, Liping Jiang, Zhanqiang Liu, Qiang Luan, Hongsheng Wang
Summary: An automatic detection and pixel-level quantification model based on joint Mask R-CNN and TransUNet is developed to accurately evaluate microcrack damage on the grinding surfaces of engineering ceramics. The model is effectively trained on actual micrograph image dataset using a joint training strategy. The proposed model achieves reliable automatic detection and fine segmentation of microcracks, and a skeleton-based quantification model is also proposed to provide comprehensive and precise measurements of microcrack size.
Review
Engineering, Multidisciplinary
Sang Yeob Kim, Da Yun Kwon, Arum Jang, Young K. Ju, Jong-Sub Lee, Seungkwan Hong
Summary: This paper reviews the categorization and applications of UAV sensors in forensic engineering, with a focus on geotechnical, structural, and water infrastructure fields. It discusses the advantages and disadvantages of sensors with different wavelengths and addresses the challenges of current UAV technology and recommendations for further research in forensic engineering.
Article
Engineering, Multidisciplinary
Anton Nunez-Seoane, Joaquin Martinez-Sanchez, Erik Rua, Pedro Arias
Summary: This article compares the use of Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS) for digitizing the road environment and detecting road slopes. The study found that ALS data and its corresponding algorithm achieved better detection and delimitation results compared to MLS. Measuring the road from a terrestrial perspective negatively impacted the detection process, while an aerial perspective allowed for scanning of the entire slope structure.
Article
Engineering, Multidisciplinary
Nur Luqman Saleh, Aduwati Sali, Raja Syamsul Azmir Raja Abdullah, Sharifah M. Syed Ahmad, Jiun Terng Liew, Fazirulhisyam Hashim, Fairuz Abdullah, Nur Emileen Abdul Rashid
Summary: This study introduces an enhanced signal processing scheme for detecting mouth-click signals used by blind individuals. By utilizing additional band-pass filtering and other steps, the detection accuracy is improved. Experimental results using artificial signal data showed a 100% success rate in detecting obstacles. The emerging concepts in this research are expected to benefit radar and sonar system applications.
Article
Engineering, Multidisciplinary
Jiqiang Tang, Shengjie Qiu, Lu Zhang, Jinji Sun, Xinxiu Zhou
Summary: This paper studies the magnetic noise level of a compact high-performance magnetically shielded room (MSR) under different operational conditions and establishes a quantitative model for magnetic noise calculation. Verification experiments show the effectiveness of the proposed method.
Review
Engineering, Multidisciplinary
Krzysztof Bartnik, Marcin Koba, Mateusz Smietana
Summary: The demand for miniaturized sensors in the biomedical industry is increasing, and optical fiber sensors (OFSs) are gaining popularity due to their small size, flexibility, and biocompatibility. This study reviews various OFS designs tested in vivo and identifies future perspectives and challenges for OFS technology development from a user perspective.
Article
Engineering, Multidisciplinary
Yue Wang, Lei Zhou, Zihao Li, Jun Wang, Xuangou Wu, Xiangjun Wang, Lei Hu
Summary: This paper presents a 3-D reconstruction method for dynamic stereo vision of metal surface based on line structured light, overcoming the limitation of the measurement range of static stereo vision. The proposed method uses joint calibration and global optimization to accurately reconstruct the 3-D coordinates of the line structured light fringe, improving the reconstruction accuracy.
Article
Engineering, Multidisciplinary
Jaafar Alsalaet
Summary: Order tracking analysis is an effective tool for machinery fault diagnosis and operational modal analysis. This study presents a new formulation for the data equation of the second-generation Vold-Kalman filter, using separated cosine and sine kernels to minimize error and provide smoother envelopes. The proposed method achieves high accuracy even with small weighting factors.
Article
Engineering, Multidisciplinary
Tonglei Cao, Kechen Song, Likun Xu, Hu Feng, Yunhui Yan, Jingbo Guo
Summary: This study constructs a high-resolution dataset for surface defects in ceramic tiles and addresses the scale and quantity differences in defect distribution. An improved approach is proposed by introducing a content-aware feature recombination method and a dynamic attention mechanism. Experimental results demonstrate the superior accuracy and efficiency of the proposed method.
Article
Engineering, Multidisciplinary
Qinghong Fu, Yunxi Lou, Jianghui Deng, Xin Qiu, Xianhua Chen
Summary: Measurement and quantitative characterization of aging-induced gradient properties is crucial for accurate analysis and design of asphalt pavement. This research proposes the composite specimen method to obtain asphalt binders at different depths within the mixture and uses dynamic shear rheometer tests to measure aging-induced gradient properties and reveal internal mechanisms. G* master curves are constructed to investigate gradient aging effects in a wide range. The study finds that the composite specimen method can effectively restore the boundary conditions and that it is feasible to study gradient aging characteristics within the asphalt mixture. The study also observes variations in G* and delta values and the depth range of gradient aging effects for different aging levels.
Article
Engineering, Multidisciplinary
Min Li, Kai Wei, Tianhe Xu, Yali Shi, Dixing Wang
Summary: Due to the limitations of ground monitoring stations in China for the BDS, the accuracy of BDS Medium Earth Orbit (MEO) satellite orbits can be influenced. To overcome this, low Earth orbit (LEO) satellites can be used as additional monitoring stations. In this study, data from two LEO satellites were collected to improve the precise orbit determination of the BDS. By comparing the results with GPS and BDS-2/3 solutions, it was found that including the LEO satellites significantly improved the accuracy of GPS and BDS-2/3 orbits.
