Article
Computer Science, Hardware & Architecture
Shaojie Ai, Jia Song, Guobiao Cai
Summary: This article investigates a fault diagnosis scheme for hypersonic vehicles based on a data-driven approach. It uses wavelet packet translation and an improved distance evaluation technique to extract sensitive features, and utilizes support vector regression for fault pattern recognition and localization.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Chemistry, Multidisciplinary
Katleho Moloi, Nomihla Wandile Ndlela, Innocent E. Davidson
Summary: This paper proposes a fault protection diagnostic scheme for power distribution systems. It combines wavelet packet decomposition (WPD) for signal processing and analysis with support vector machine (SVM) for fault classification and location. The scheme is tested on a real system and shows rapid and efficient fault classification and location.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Bing Sun, Xiaofeng Liu
Summary: Wheelset bearing is a critical component in high-speed trains for safe and efficient operation. However, the Support Vector Machine (SVM) method for bearing health monitoring can lead to overfitting when outliers are present in the training dataset. In order to address this issue, an improved Significance SVM (SSVM) is proposed that assigns significant coefficients to samples in the model training process, giving less attention to outlier samples. The experiments on HST bearing vibration dataset demonstrate the effectiveness and stability of the proposed method under different noise levels.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Yuhan Wu, Xianbo Sun, Yi Zhang, Xianjing Zhong, Lei Cheng
Summary: This paper proposes a power transformer fault diagnosis method based on KPCA and TISOA-SVM, which optimizes the parameters of SVM to build the optimal diagnosis model, achieving higher diagnostic accuracy and efficiency.
Article
Engineering, Electrical & Electronic
Yongkui Sun, Yuan Cao, Peng Li
Summary: The paper proposes a sound-based fault diagnosis method for railway point machines (RPMs) using fractional calculus and coarse-grain process. A novel feature called multi-scale fractional WPDE (FWPDE) is developed to improve the fault diagnosis accuracy. The paper also presents a synchronous optimization strategy based on binary particle swarm optimization (BPSO) to select optimal feature set and optimize the hyperparameters of support vector machine (SVM), further improving the diagnosis accuracy. The proposed method is verified to be superior and effective compared to existing fault diagnosis methods.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Zhiwei Qiu, Rui Min, Daozhi Wang, Siwen Fan
Summary: This study proposes an intelligent fault diagnosis method based on energy features fusion to detect seal wear and internal leakage in hydraulic cylinders. By analyzing the flow field using computational fluid dynamics (CFD) technology, the energy features of the pressure signal are found to be related to internal leakage. Wavelet packet transform is applied to extract the energy features, which are then decomposed into statistics using multivariate statistics theory. Experimental investigations confirm the method's robustness and accuracy, outperforming several classical fault diagnosis methods.
Article
Acoustics
Rakesh Kumar Jha, Preety D. Swami
Summary: This paper presents a fault classification method for ball bearings based on multiclass support vector machines, which converts vibration signals to texture images, extracts features, and trains classifiers to achieve fault localization and severity diagnosis.
Article
Computer Science, Artificial Intelligence
Sinan Li, Tianfu Li, Chuang Sun, Xuefeng Chen, Ruqiang Yan
Summary: Proposed an interpretable wavelet packet kernel-constrained convolutional network (WPConvNet) for noise-robust fault diagnosis, which combines the feature extraction ability of wavelet bases and the learning ability of convolutional kernels. The proposed architecture outperforms other diagnosis models in terms of interpretability and noise robustness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Kenichi Yatsugi, Shrinathan Esakimuthu Pandarakone, Yukio Mizuno, Hisahide Nakamura
Summary: Induction motors are crucial components in many industries, requiring proper maintenance and fault detection to prevent serious damage and industry shutdown. Bearing faults, broken rotor bar faults, and short-circuit insulation faults are common in induction motors, and their early detection and classification have received significant attention. However, there are limited studies on the detection and classification of these faults in the initial stage using common diagnosis methods.
Article
Engineering, Electrical & Electronic
Mingliang Cui, Youqing Wang, Xinshuang Lin, Maiying Zhong
Summary: The study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis, which has stronger feature extraction ability and faster network convergence speed compared to existing methods.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Kumari Sarita, Sachin Kumar, R. K. Saket
Summary: This paper introduces a fast fault detection algorithm based on the two samples technique and a fault localization algorithm using the Entropy of Wavelet Packets as a feature, which can effectively classify and localize Open Circuit faults in Multilevel Converters.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu
Summary: This paper proposes a unified framework to address the asymmetric distribution of information between training and testing phases in regression tasks. By integrating continuous, ordinal, and binary privileged information into the learning process of support vector regression, the proposed method outperforms the classic learning paradigm in solving practical problems.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Engineering, Electrical & Electronic
Andrei S. Maliuk, Zahoor Ahmad, Jong-Myon Kim
Summary: This paper proposes a framework aimed at improving the accuracy of bearing-fault diagnosis. The framework utilizes a hybrid feature-selection method based on Wrapper-WPT. It decomposes the vibration signal using Wavelet Packet Transform and extracts time and frequency domain features. The features are then selected using the Boruta algorithm, and a Subspace k-NN is used for bearing fault diagnosis. The proposed method shows higher classification performance compared to other state-of-the-art methods.
Article
Multidisciplinary Sciences
Chun-Yao Lee, Guang-Lin Zhuo
Summary: This article proposes an effective rotor fault diagnosis model of an induction motor based on multilayer signal analysis and hybrid genetic binary chicken swarm optimization. The proposed model reduces the dimension of raw data, enhances robustness, and achieves better global exploration ability compared to other evolutionary algorithms.
Article
Computer Science, Information Systems
Wentao Zhang, Ting Zhang, Guohua Cui, Ying Pan
Summary: With the development of large-scale industrial systems, accurate fault diagnosis methods are necessary for the security and reliability of mechanical equipment. Conventional machine learning methods are time-consuming and have poor generalization performance, while deep learning methods have wider application prospects. However, deep learning models face challenges such as a large number of parameters, hyperparameter tuning, and initialization instability. In this study, we proposed a novel deep learning framework using convolutional neural networks and transfer learning to address these challenges.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)