Article
Automation & Control Systems
Qichao Yang, Baoping Tang, Qikang Li, Xiaoli Liu, Lei Bao
Summary: This paper proposes a novel prediction framework for forecasting the remaining useful life (RUL) of aircraft engines. The framework utilizes a dual-frequency enhanced attention network architecture, separable convolutional neural networks, frequency-enhanced modules, and an efficient channel attention block to improve the prediction performance and robustness of the model.
Article
Engineering, Industrial
Lu Liu, Xiao Song, Zhetao Zhou
Summary: This study proposes a double attention-based data-driven framework for aircraft engine RUL prediction. The framework utilizes channel attention-based CNN to weigh important features and uses Transformer to focus attention at critical time steps. Experimental results indicate that the proposed framework outperforms existing SOTA algorithms, effectively predicting RUL and reducing equipment failure risk.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Artificial Intelligence
Yujie Cheng, Jiyan Zeng, Zili Wang, Dengwei Song
Summary: This study proposes a health-state-related ensemble deep learning method for predicting the remaining useful life (RUL) of aircraft engines. The method divides the lifetime degradation into multiple health states and trains three deep learning methods on different health states to learn different degradation laws. By calculating the self-adaptive ensemble weight sets, the prediction results of each algorithm model in different health states can be comprehensively utilized. The experimental results demonstrate that the proposed method can significantly improve prediction performance.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Kui Hu, Yiwei Cheng, Jun Wu, Haiping Zhu, Xinyu Shao
Summary: This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the remaining useful life (RUL) prediction of aircraft engines. The method achieves high accuracy in RUL prediction by extracting hidden features from sensory data and iteratively training regression decision tree (RDT) models.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Hao Li, Zhuojian Wang, Zhe Li
Summary: In this paper, an improved CNN-LSTM model based on CBAM is proposed for aircraft engine RUL prediction. Experimental results demonstrate the feasibility and improved accuracy and performance of our model compared to other methods.
PEERJ COMPUTER SCIENCE
(2022)
Article
Engineering, Mechanical
Unnati Thakkar, Hicham Chaoui
Summary: This research utilizes machine learning to provide a prediction framework for the remaining useful life of an aircraft's turbofan engine. The proposed Deep Layer Recurrent Neural Network (DL-RNN) model demonstrates higher predictive accuracy compared to other machine learning algorithms.
Article
Computer Science, Information Systems
Owais Asif, Sajjad Ali Haider, Syed Rameez Naqvi, John F. W. Zaki, Kyung-Sup Kwak, S. M. Riazul Islam
Summary: This paper highlights the importance of accurately predicting the remaining useful life (RUL) of industrial machinery and proposes the use of deep learning-based LSTM networks to improve prediction accuracy. Additionally, an improved piecewise linear degradation model and pre-processing techniques are introduced to enhance predictive performance.
Article
Engineering, Aerospace
Yiming Peng, Yin Yin, Pengpeng Xie, Xiaohui Wei, Hong Nie
Summary: This paper presents some studies on the complex and nonlinear carrier-based aircraft landing and arrest process, including the coupling effect between the aircraft and arresting system. A dynamic model of the landing and arrest cable was established and verified using laboratory test results. The key parameters affecting the collision rebound performance of the arresting hook were analyzed, and a surrogate model of the reliability of the arresting hook was established using the Support Vector Machine method. The reliability analysis was carried out using the Monte Carlo method, and meaningful conclusions were obtained.
CHINESE JOURNAL OF AERONAUTICS
(2023)
Article
Chemistry, Multidisciplinary
Hairui Wang, Dongwen Li, Dongjun Li, Cuiqin Liu, Xiuqi Yang, Guifu Zhu
Summary: This study proposed a new method for predicting the remaining useful life (RUL) of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP). The method selected key features that significantly impact the engine's lifetime operation cycle and established an MLP-based RUL prediction model using a neural network. Experimental results showed that compared to other methods, the proposed method achieved a 6.1% reduction in RMSE, demonstrating its effectiveness in improving the accuracy of RUL prediction for aircraft engines.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics
Pengli Mao, Yan Lin, Song Xue, Baochang Zhang
Summary: This study proposes a neural architecture search method based on gradient descent to predict the remaining useful life (RUL) of engines and prevent potential serious accidents. Experimental results show that the proposed method achieves superior performance in estimation accuracy.
Article
Engineering, Electrical & Electronic
Likun Ren, Haiqin Qin, Zhenbo Xie, Bianjiang Li, Kejun Xu
Summary: This article proposes a novel multi-head structure and time loss function to improve the accuracy of data-driven aero-engine remaining useful life (RUL) estimation. The experiments demonstrate the effectiveness of the proposed method in evaluating aero-engine degradation.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Aerospace
Xiaofeng Liu, Liuqi Xiong, Yiming Zhang, Chenshuang Luo, Sujin Bureerat
Summary: This paper proposes a residual life prediction model based on Autoencoder and TCN, which reduces the data dimension and extracts features to predict the remaining useful life of the engine. The experimental results show that the proposed model performs the best in evaluation, and it has important implications for engine health.
Article
Computer Science, Interdisciplinary Applications
Alex Falcon, Giovanni D'Agostino, Oswald Lanz, Giorgio Brajnik, Carlo Tasso, Giuseppe Serra
Summary: This paper explores the usage of Neural Turing Machine (NTM) in solving the problem of estimating the Remaining Useful Life. It is found that using a single NTM as the key feature extraction component can yield more accurate solutions compared to commonly used Long Short-Term Memory-based solutions. The proposed approach is validated using sensor data from aircraft turbofan engines and particle filtration systems, achieving competitive results against state-of-the-art techniques.
COMPUTERS IN INDUSTRY
(2022)
Article
Engineering, Industrial
Juseong Lee, Mihaela Mitici
Summary: This study proposes a framework that integrates data-driven probabilistic Remaining-Useful-Life (RUL) prognostics with predictive maintenance planning, using aircraft turbofan engines as an example. By employing this framework, the total maintenance cost can be reduced, unscheduled maintenance can be prevented, and the wasted life of engines can be limited.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Chemistry, Analytical
Nachuan Liu, Xiaofeng Zhang, Jiansheng Guo, Songyi Chen
Summary: A bi-discrepancy network is proposed to address the issues in unsupervised domain adaptation methods. By detecting target samples with significant domain shifts and locally aligning fine-grained features, the accuracy of cross-domain prediction tasks is improved.
Article
Environmental Sciences
Paulino Jose Garcia-Nieto, Esperanza Garcia-Gonzalo, Jose Pablo Paredes-Sanchez, Antonio Bernardo Sanchez
Summary: This paper evaluates the performance of fossil-fuel power plants using a new hybrid algorithm based on MARS and DE to predict net annual electricity generation and carbon dioxide emissions with high accuracy. The model also determines the importance of economic and energy parameters in characterizing the behavior of thermal power stations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Mathematics, Applied
Esteban Jove, Jose M. Gonzalez-Cava, Jose-Luis Casteleiro-Roca, Hector Quintian, Juan Albino Mendez Perez, Rafael Vega Vega, Francisco Zayas-Gato, Francisco Javier de Cos Juez, Ana Leon, Maria Martin, Jose A. Reboso, Michal Wozniak, Jose Luis Calvo-Rolle
Summary: This study focuses on predicting the evolution of analgesic variables in patients undergoing surgery using hybrid intelligent modeling methods. The proposed model can make a one-step-ahead prediction of the remifentanil dose corresponding to the current state of the patient, showing potential in calculating drug doses for specific analgesic states.
LOGIC JOURNAL OF THE IGPL
(2021)
Article
Computer Science, Artificial Intelligence
Paulino Jose Garcia-Nieto, Esperanza Garcia-Gonzalo, Jose Pablo Paredes-Sanchez, Antonio Bernardo Sanchez
Summary: The research used the Gaussian process regression method to establish a predictive model for early detection of thermal power efficiency in buildings, based on data collected from different dwellings. The model successfully predicted the thermal power efficiency and demonstrated the effectiveness of the innovative approach.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Paulino Jose Garcia-Nieto, Esperanza Garcia-Gonzalo, Jose Ramon Alonso Fernandez, Cristina Diaz Muniz
Summary: The article introduces a nonparametric machine learning algorithm that combines the GBRT model and L-SHADE algorithm to better predict and control algal atypical proliferation in water systems, successfully estimating Chlorophyll-a and Total Phosphorus concentrations.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Astronomy & Astrophysics
L. Bonavera, S. L. Suarez Gomez, J. Gonzalez-Nuevo, M. M. Cueli, J. D. Santos, M. L. Sanchez, R. Muniz, F. J. de Cos
Summary: In this study, a method based on fully convolutional networks is developed to detect point sources in realistic simulations, and its performance is compared with the Mexican hat wavelet 2 method commonly used in this context, with results showing that the neural network performs better in handling spurious sources.
ASTRONOMY & ASTROPHYSICS
(2021)
Article
Astronomy & Astrophysics
A. Castro-Gonzalez, E. Diez Alonso, J. Menendez Blanco, J. Livingston, J. P. de Leon, J. Lillo-Box, J. Korth, S. Fernandez Menendez, J. M. Recio, F. Izquierdo-Ruiz, A. Coya Lozano, F. Garcia de la Cuesta, N. Gomez Hernandez, J. R. Vidal Blanco, R. Hevia Diaz, R. Pardo Silva, S. Perez Acevedo, J. Polancos Ruiz, P. Padilla Tijerin, D. Vazquez Garcia, S. L. Suarez Gomez, F. Garcia Riesgo, C. Gonzalez Gutierrez, L. Bonavera, J. Gonzalez-Nuevo, C. Rodriguez Pereira, F. Sanchez Lasheras, M. L. Sanchez Rodriguez, R. Muniz, J. D. Santos Rodriguez, F. J. de Cos Juez
Summary: The K2-OjOS project, a collaboration between professional and amateur astronomers, identified four new planets and 14 planet candidates, improving the precision of transit ephemeris using a combination of archival and new data. Some systems exhibited features related to period commensurabilities, while new single transits and uncertain signal origins were also detected.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2022)
Article
Energy & Fuels
Paulino Jose Garcia Nieto, Esperanza Garcia-Gonzalo, Beatriz M. Paredes-Sanchez, Jose P. Paredes-Sanchez
Summary: This study developed an artificial smart model based on support vector machines and grid search optimizer for predicting and characterizing the Higher Heating Value (HHV) of raw biomass. The results showed that the model was accurate in predicting the HHV of biomass and highlighted the importance of physico-chemical parameters in determining the HHV.
Article
Mathematics, Applied
Esperanza Garcia-Gonzalo, Paulino Jose Garcia Nieto, Javier Gracia Rodriguez, Fernando Sanchez Lasheras, Gregorio Fidalgo Valverde
Summary: In this study, a machine learning method is used to forecast the spot prices of copper from the New York Commodity Exchange, and the performance of different model schemas is compared. The numerical results demonstrate that the hybrid direct-recursive method achieves the best results.
LOGIC JOURNAL OF THE IGPL
(2023)
Article
Engineering, Environmental
Paulino Jose Garcia-Nieto, E. Garcia-Gonzalo, Jose Ramon Alonso Fernandez, Cristina Diaz Muniz
Summary: This study utilized support vector regression (SVR) to predict the concentrations of chlorophyll-a (Chl-a) and total phosphorus (TP) in water bodies. By optimizing parameters and establishing models, successful predictions of the concentrations of these two substances in water bodies were achieved.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Surgery
Thanapoom Boonipat, Amjed Abu-Ghname, Jason Lin, Esperanza Garcia-Gonzalo, Uldis Bite, Mitchell A. Stotland
Summary: This study examines the perceptual response to periorbital aging before and after brow lift and upper blepharoplasty surgery. The surgical intervention increases attention to the eye and brow region while decreasing attention to the forehead and lower eyelid areas. It also improves the perception of character attributes.
PLASTIC AND RECONSTRUCTIVE SURGERY
(2022)
Article
Astronomy & Astrophysics
J. M. Casas, L. Bonavera, J. Gonzalez-Nuevo, M. M. Cueli, D. Crespo, E. Goitia, C. Gonzalez-Gutierrez, J. D. Santos, M. L. Sanchez, F. J. de Cos
Summary: This study develops and trains a machine learning model based on a convolutional neural network to estimate the polarisation flux density and angle of point sources embedded in cosmic microwave background images. The model shows reliable results in constraining the polarisation flux density of sources above 80 mJy, with relative errors below 30% for most flux density levels. It can also determine the polarisation angle of Q and U sources with a 1 sigma uncertainty of +/- 29 degrees and +/- 32 degrees, respectively.
ASTRONOMY & ASTROPHYSICS
(2023)
Article
Mathematics
Luis Alfonso Menendez-Garcia, Paulino Jose Garcia-Nieto, Esperanza Garcia-Gonzalo, Fernando Sanchez Lasheras, Laura Alvarez-de-Prado, Antonio Bernardo-Sanchez
Summary: This study aims to identify outliers in air quality observations near a seaport by comparing the effectiveness of functional data analysis (FDA) and vector methods. The FDA approach was found to be more powerful than the vector methods, with the functional bagplot method detecting more outliers than the HDR boxplot method.
Article
Mathematics, Applied
J. C. Alvarez Anton, P. J. Garcia-Nieto, E. Garcia-Gonzalo, M. Gonzalez Vega, C. Blanco Viejo
Summary: Due to efficiency and environmental reasons, electric vehicles (EVs) will dominate the automobile industry. Lithium-ion batteries are the leading energy supply for EVs and other electronic consumer devices. Predicting the state-of-charge (SOC) of the battery is crucial for EV users to avoid running out of power. This study utilizes machine learning techniques to predict the SOC of a storage cell, with the ABC/GBRT-based model showing the best performance.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Esperanza Garcia-Gonzalo, Paulino Jose Garcia Nieto, Gregorio Fidalgo Valverde, Pedro Riesgo Fernandez, Fernando Sanchez Lasheras
Summary: In this study, three different strategies for automatic lag selection in time series analysis are proposed. The two novel hybrid methods, NARX SVR and GPR, combined with DE optimizer, show better performance in gold spot price forecasting compared to other machine learning techniques.
16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Fernando Sanchez Lasheras, Paulino Jose Garcia Nieto, Esperanza Garcia-Gonzalo, Gregorio Fidalgo Valverde, Alicja Krzemien
Summary: This research presents a methodology for forecasting gold prices using historical values of metals as input information. The proposed method decomposes the time series into trend, seasonal, and random components, and uses trend information as independent variables in a regression model for predicting gold prices. The empirical results indicate that the method performs well in both short-term and medium-term forecasts.
16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021)
(2022)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)