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Article
Engineering, Multidisciplinary
Tomasz Nowakowski et al.
Summary: The article presents the genesis of a method to diagnose a tram transmission based on acoustic signals from the track position. The influence of a damaged gearbox on acoustic phenomena near the tram line, specifically changes in psychoacoustic indicators, was demonstrated. Nonstationary acoustic signals were analyzed using empirical mode decomposition. The developed quantitative measure served as a classifier in decision trees. An effective tree was selected based on calculated diagnostic indicators, and an algorithm to diagnose the tram transmission without mounting equipment on the vehicle was developed.
Article
Automation & Control Systems
Jiayang Liu et al.
Summary: Condition monitoring of wind turbines is crucial for their long-term stable operation. This paper proposes a new monitoring network, called spatio-temporal graph neural network, to overcome the limitations of existing deep learning methods. By applying missing value supplement and selecting variables with maximal information coefficient, constructing graphs using top-k nearest neighbors, and using graph convolution networks and gated recurrent unit to establish spatio-temporal blocks, the proposed method efficiently detects early abnormal operation and improves the utilization of renewable energy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Siwei Lou et al.
Summary: This study proposes a fault information-aided canonical variate analysis (FICVA) and a structured FICVA (SFICVA) monitoring strategy to improve fault detection rate. In FICVA, canonical variate analysis (CVA) is used to extract state and residual subspace based on normal data. Then, the subspaces are further decomposed into fault relevant and irrelevant subspaces to concentrate fault information.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Engineering, Mechanical
Renhe Yao et al.
Summary: This paper proposes an integrated framework for mechanical bearing health monitoring (DHM) and performance degradation assessment (PDA) using key-spectrum entropy and statistical properties. The framework includes key spectrum, key-spectrum entropy, joint statistical alarm and fault identification strategy, health phase segmentation strategy, and three-dimensional (3D) key spectrums. Evaluation on eighteen sets of bearing degradation vibration signals demonstrates the validity and practical application prospects of the proposed framework.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Amir M. Fathollahi-Fard et al.
Summary: The global shift towards sustainable and regenerative agriculture is driven by increasing awareness of social inequalities and climate change. This study introduces a new multi-objective optimization approach that employs fuzzy logic and multiple objectives to facilitate sustainable harvest planning in the face of various uncertainties.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Fengyuan Zhang et al.
Summary: This paper proposes a method using heterogeneous signal fusion and graph-driven approach to assess and predict the health condition of Francis turbine units in hydroelectric power generation. By establishing connections between similar signals, the multivariate data of health status are transformed into spatial-temporal graphs. A hybrid neural network-based model is designed to excavate the spatial-temporal dependence relationships in these graphs. The comprehensive health assessment index is obtained by calculating the distance between the predicted heterogeneous signals and measured signals. Verification experiments show that the proposed method has a higher sensitivity in assessing the deterioration degree of turbine units.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Josef Koutsoupakis et al.
Summary: Real-time monitoring of mechanical systems through vibration measurements enables fault detection and predictive maintenance. The use of Artificial Intelligence (AI) in damage detection provides automated means for Condition Monitoring (CM) and characterization of health states. This study proposes a novel CM framework using Convolutional Neural Networks (CNNs) for damage detection and identification, applied to an elevator door rail using simulated data. Rating: 8 out of 10.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Green & Sustainable Science & Technology
Syed Mithun Ali et al.
Summary: This paper proposes a comprehensive closed-loop supply chain (CLSC) network that optimizes environmental, economic, and social footprints through multi-objective optimization. It addresses challenges associated with parameter uncertainties by utilizing scenario generation and stochastic programming. The proposed model is transformed into a single-objective formulation using a weighted sum method and problem-specific heuristics. By leveraging Lagrangian relaxation theory and a neighborhood-based algorithm, an optimal lower bound and feasible upper bound are obtained. An iterative reoptimization process achieves an optimal solution. A case study in the light engineering industry in Bangladesh demonstrates the applicability of the proposed algorithm. The research provides valuable guidance for establishing efficient and sustainable waste management systems.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Engineering, Mechanical
Chunsheng Wang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Guangdong Tian et al.
Summary: Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies while avoiding vague task assignments to multiple maintenance teams/operators.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Review
Environmental Sciences
Guangdong Tian et al.
Summary: With the growing severity of environmental problems, low-carbon development has become an inevitable choice. The complexity of low-carbon green sustainable development, influenced by various factors, poses challenges for decision-makers. This paper provides a systematic review and analysis of multi-criteria decision-making (MCDM) techniques in the field of low-carbon transport and green logistics, filling the gap in existing literature. Future directions for MCDM techniques in these areas are also presented.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Automation & Control Systems
Zhonghao Xie et al.
Summary: This paper proposes a robust method for PLS based on the idea of least trimmed squares (LTS), which effectively deals with high-dimensional regressors. By formulating the LTS problem as a concave maximization problem, the complexity of solving LTS is simplified. The results from simulation and real data sets demonstrate the effectiveness and robustness of the proposed approach.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jie Yang et al.
Summary: Process monitoring is critical for ensuring process safety and maintaining quality stabilization in large-scale industrial processes. The traditional monitoring methods may overlook the correlation between process and quality variables. A new dynamic concurrent partial least squares (DCPLS) monitoring scheme based on variable importance in the projection (VIP) is proposed in this paper, which proves to be more efficient in monitoring abnormal events.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Khandaker Noman et al.
Summary: This research achieves sparsity index guided bearing health monitoring by oscillation based decomposition of vibration signals, aiming at the limitations of conventional bearing health monitoring indices under varying speed operating condition. The low oscillatory component of a vibration signal is separated with the help of tunable Q factor wavelet transform (TQWT), and the health of rolling element bearing is monitored by quantifying the extracted signal component using four prominent sparsity indices.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Hadi Gholizadeh et al.
Summary: This study investigates the impact of electrical discharge machines (EDM) on production management and develops a mathematical modelling approach to analyze its effect on volumetric flow rate, electrode corrosion percentage, and surface roughness. The proposed method predicts and optimizes EDM parameters using a fuzzy possibility regression integrated model and an adaptive-network-based fuzzy inference system. The results confirm the accuracy and reliability of the method and encourage further testing.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Chemical
Lin Xuan You et al.
Summary: To achieve desired product qualities, monitoring the operational status is necessary. Traditional multivariate statistical process control techniques are not suitable for monitoring unevenly distributed process data. This study proposes the use of a multilocal partial least-squares (ML-PLS) model to monitor a wide operation process.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Computer Science, Theory & Methods
Tianyu Guan et al.
Summary: The partial least squares approach has achieved great success in spectrometric prediction in chemometrics and can be used for handling spectral data. This paper proposes a sparse version of the functional partial least squares regression, aiming to achieve locally sparse estimates for the functional partial least squares bases and the slope function. It applies a functional regularization technique and a computational method to identify nonzero sub-regions for the slope function estimation. The proposed method is illustrated through simulation studies and two applications on actual datasets.
STATISTICS AND COMPUTING
(2022)
Article
Energy & Fuels
Pawel Sokolski et al.
Summary: This paper discusses the problem of cooperation between multiple model predictive control systems and proposes a cooperative control solution to improve power generation performance through information exchange.
Review
Engineering, Mechanical
Chunwei Zhang et al.
Summary: Structural health monitoring (SHM) is an important and hot topic in various fields, aiming to estimate the health condition of structures and understand their unique characteristics through assessing measured physical parameters in real-time. Signal processing plays a crucial role in vibration-based SHM research, with the goal of identifying changes or damages from vibration signals and detecting, locating, and quantifying any existing damages. This paper comprehensively reviews recent progress in using signal processing techniques for vibration-based SHM approaches, with a focus on feature extraction in the time and frequency domains.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Industrial
Di Maio Francesco et al.
Summary: This paper adopts a time-dependent reliability approach to explicitly model aging and degradation of the nuclear power plant's reactor building, quantifying the risk of failure over time. By coupling a Finite Element Model with a degradation model, the study provides risk measures based on condition monitoring data, exemplified by a case study of an internal overpressure due to a hydrogen explosion.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Chemical
Wahiba Bounoua et al.
Summary: This study introduces a novel Dynamic Kernel PCA method for process monitoring in nonlinear dynamical systems, using the powerful theory of the nonlinear Fractal Dimension. The Fractal-based DKPCA integrates the two strategies to overcome the shortcomings of traditional methods and showed superior performance in fault detection and diagnosis compared to contemporary approaches.
CHEMICAL ENGINEERING SCIENCE
(2021)
Article
Engineering, Industrial
Jue Li et al.
Summary: Construction equipment-related accidents are common, with hazard perception error being a key contributing factor. While the importance of hazard perception to safety is widely recognized, there is still a lack of comprehensive analysis of its cognitive processes and underlying causes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Automation & Control Systems
Jie Yang et al.
Summary: An enhanced intelligent diagnosis method, J-PDBN, is proposed based on a joint pairwise graph embedded sparse deep belief network and partial least square fine-tuning. The combination of unsupervised learning and PLS fine-tuning results in better fault classification capabilities. Experimental results validate the high accuracy and superiority of the proposed method in gearbox and bearing fault diagnosis.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Automation & Control Systems
Yabin Si et al.
Summary: The article proposed an improved KPLS method that considers KPI-related information for KPI-related process monitoring. The method includes performing GSVD on calculable loadings based on the kernel matrix, and dividing the kernel matrix into KPI-related and KPI-unrelated subspaces. Additionally, the article presents the design of statistics for process monitoring and a detailed algorithm performance analysis for kernel methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Wenderson N. Lopes et al.
Summary: This study proposes an algorithm based on the Kaiser window to adjust STFT parameters for an appropriate balance between time-frequency resolutions, and applies this algorithm to investigate characteristic frequencies in dressing operation of an aluminum oxide grinding wheel. The results show that the spectral content of AE signals during dressing follows a uniform behavior, but their amplitude changes depending on the characteristics of topography and sharpness of the grinding wheel cutting edges.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Industrial
L. Puppo et al.
Summary: This paper introduces a novel methodological framework based on Finite Mixture Models and Adaptive Kriging for addressing complex multimodal issues in safety analyses of passive systems for nuclear energy applications. The framework tackles non-smooth and multimodal output by reducing dimensionality and effectively characterizing the system's critical failure regions.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Industrial
David A. Quintanar-Gago et al.
Summary: The paper introduces a Bayesian network model to handle interactions among common damage mechanisms and failure modes in nuclear steam turbine rotating blades. This model helps predict which portions of the turbine will need repair and utilizes a unique quantification method combining expert judgement, Recursive Noisy OR, and damage mechanism susceptibility ranking. It is adaptable to different turbine designs and purposes, with detailed development, validation, and examples of application presented.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Thermodynamics
Fletcher Carlson et al.
Summary: Thermal energy storage for nuclear power can enhance flexibility of low carbon baseload power plants and facilitate greater use of renewable energy sources. Comparison of different integration approaches in a parametric study provides insights on the technical performance and cost efficiency, with configurations showing varying effectiveness in terms of energy production ratio and peaking power capabilities.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Automation & Control Systems
Zhe Li et al.
Summary: Kernel Partial Least Squares (KPLS) is an effective nonlinear modeling technique used in control engineering applications, capable of handling small sample sizes and noisy, highly correlated variable sets. By mapping input variables to a feature space, it produces an optimal prediction model for process output variables. However, the computational intensity of the procedure can be a challenge, especially when dealing with large data sets. The proposed Efficient Kernel Partial Least Squares (EKPLS) aims to reduce computational complexity significantly compared to the traditional approach.
CONTROL ENGINEERING PRACTICE
(2021)
Article
Engineering, Environmental
Bing Xiao et al.
Summary: The new RRVS-DPCA method addresses abnormal condition detection in large-scale industrial processes through variable selection based on relevance and redundancy, as well as a weighted contribution plot method to identify root causes of faults. The feasibility and effectiveness of the proposed monitoring scheme were demonstrated through comparisons with state-of-the-art process monitoring methods on numerical examples and the Tennessee Eastman benchmark process.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Engineering, Electrical & Electronic
Hai Yang et al.
Summary: This article introduces a method to improve the accuracy of U-tube CMF under pulsating flow, based on variable step-size LMS filter and Hilbert transform. Experimental results show that the stability and accuracy of the proposed algorithm are better than that of the traditional CMF phase difference calibration method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Issam Attoui et al.
Summary: This article proposes a novel automatic machinery condition monitoring method based on signal similarity measurement in the time-frequency domain, utilizing short-time Fourier Transform and wavelet packet decomposition techniques for robustness and accuracy. The method demonstrates high classification accuracy even with very limited labeled samples, showcasing its effectiveness in noisy environments and diverse operating conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Thermodynamics
Abhay Patil et al.
APPLIED THERMAL ENGINEERING
(2020)
Article
Energy & Fuels
Aiden Peakman et al.
Review
Energy & Fuels
Yubiao Sun
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2020)
Article
Automation & Control Systems
Jingying Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2020)
Article
Nuclear Science & Technology
Areai Nuerlan et al.
PROGRESS IN NUCLEAR ENERGY
(2020)
Article
Computer Science, Information Systems
Gaojun Liu et al.
Article
Computer Science, Interdisciplinary Applications
Karl Ezra S. Pilario et al.
COMPUTERS & CHEMICAL ENGINEERING
(2019)
Article
Engineering, Mechanical
Bing Guo et al.
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME
(2019)
Review
Automation & Control Systems
Marcos Quinones-Grueiro et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2019)
Review
Engineering, Mechanical
Tianyang Wang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Mechanics
Guojie Zhang et al.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2019)
Article
Engineering, Chemical
Yan Liu et al.
CHEMICAL ENGINEERING SCIENCE
(2019)
Article
Environmental Studies
Sarah M. Jordaan et al.
GLOBAL ENVIRONMENTAL POLITICS
(2019)
Article
Automation & Control Systems
Slaheddine Zgarni et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2018)
Article
Automation & Control Systems
Yining Dong et al.
JOURNAL OF PROCESS CONTROL
(2018)
Article
Engineering, Mechanical
Fatao Hou et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2018)
Article
Automation & Control Systems
Kai Zhang et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2017)
Article
Automation & Control Systems
Ning Sheng et al.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2016)
Article
Engineering, Electrical & Electronic
Zhe Li et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2015)
Proceedings Paper
Automation & Control Systems
Yining Dong et al.
Review
Automation & Control Systems
Shen Yin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2014)
Article
Computer Science, Information Systems
Jicong Fan et al.
INFORMATION SCIENCES
(2014)
