Article
Chemistry, Analytical
Yuanqing Luo, Wenxia Lu, Shuang Kang, Xueyong Tian, Xiaoqi Kang, Feng Sun
Summary: In this study, an enhanced feature extraction network (EFEN) based on acoustic signal feature learning is proposed for fault diagnosis of rolling bearings. Experimental results show that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals.
Article
Automation & Control Systems
Zhuang Ye, Jianbo Yu
Summary: A novel deep neural network called MWMNet is proposed in this article for extracting impulses from vibration signals and performing fault diagnosis. MWMNet utilizes a smoothly embedded morphological layer to filter out noise and employs multiple branches with different scales and adaptive weighted fusion to extract impulse signals. Experimental results demonstrate that MWMNet can learn fault-related features and filter out noise, outperforming other DNN models in fault diagnosis performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Multidisciplinary
Zong Meng, Hanbing Huo, Zuozhou Pan, Lixiao Cao, Jimeng Li, Fengjie Fan
Summary: This paper proposes a gear fault diagnosis method based on an innovative accommodative random weighting theory and a balanced binary one dimension ternary pattern (BB-1D-TP) model. The method accurately diagnoses gear failures under the circumstances of multiple channels and strong background noise, effectively improving the accuracy and efficiency of gear fault identification.
Article
Chemistry, Analytical
Kaitai Dong, Ashkan Lotfipoor
Summary: This paper proposes a novel and efficient fusion method called MD-1d-DCNN, which combines statistical features from multiple domains and adaptive features obtained using a one-dimensional dilated convolutional neural network. Signal processing techniques are used to uncover statistical features and achieve general fault information. To offset the negative influence of noise, 1d-DCNN is adopted to extract more dispersed and intrinsic fault-associated features, while preventing overfitting.
Article
Engineering, Electrical & Electronic
Mingfeng Shi, Xianzeng Liu, Wenbo Wei, Yongbin Liu, Fang Liu, Guoli Li
Summary: This paper proposes an Improved Linear Discriminant Analysis algorithm for diagnosing train bearing faults. The experimental results show that the fusion features extracted by the proposed method have good clustering performance and high identification rate.
IEEE SENSORS JOURNAL
(2022)
Article
Acoustics
Jiachi Yao, Chao Liu, Keyu Song, Chenlong Feng, Dongxiang Jiang
Summary: A fault diagnosis method based on acoustic signals for planetary gearbox is proposed, using FDM decomposition and RF classification algorithm to improve fault diagnosis accuracy.
Review
Engineering, Mechanical
Yonghao Miao, Boyao Zhang, Jing Lin, Ming Zhao, Hanyang Liu, Zongyang Liu, Hao Li
Summary: Fault diagnosis is crucial for the safe operation of machinery equipment. Signal processing techniques, especially blind deconvolution methods, play a significant role in feature extraction, signal denoising, and fault identification. The use of blind deconvolution methods in machinery fault diagnosis has been extensively studied and applied, with a focus on historical background, principles, merits, limitations, performance analysis, research, and applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Linshan Jia, Tommy W. S. Chow, Yu Wang, Yixuan Yuan
Summary: A novel fault diagnosis framework called MRA-CNN is proposed in this article to learn discriminative multiscale features from vibrational signals and reduce noises. Experimental results show that the proposed method achieves higher accuracy in highly noisy environments compared to state-of-the-art methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Mathematical & Computational Biology
Jinyi Tai, Chang Liu, Xing Wu, Jianwei Yang
Summary: This paper explores the application of compressed sensing processing framework and wavelet sparse convolutional network in bearing fault diagnosis. The method effectively extracts fault characteristics of bearing acoustic emission signals, improves analysis efficiency, and accurately classifies bearing faults.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Yongchao Zhang, Ke Feng, Hui Ma, Kun Yu, Zhaohui Ren, Zheng Liu
Summary: This article proposes a method for cross-domain fault diagnosis using multisensor data, achieving comprehensive feature capturing and improving the accuracy and practicality of fault diagnosis.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Energy & Fuels
Yongheng Pang, Dongsheng Yang, Ran Teng, Bowen Zhou, Chi Xu
Summary: This paper proposes a novel fault diagnosis method called multi-dimensional aggregation and decoupling network (MADN) to address the challenges in power system fault diagnosis. By incorporating a multi-dimensional image building stage, a feature decoupling mapping stage, and a system fault state classification stage, the proposed MADN can automatically integrate the significant information from multiple signals and decouple the implicit features in a more advanced space. Experimental results confirm the effectiveness and superiority of the proposed method.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Engineering, Electrical & Electronic
Yixiang Lu, Chen Liang, De Zhu, Qingwei Gao, Dong Sun
Summary: In this article, a novel fault diagnosis algorithm based on convolutional sparse representation (CSR) is proposed to accurately detect the fault frequency of bearings. The algorithm utilizes the nonlocal similarity of vibration signals to collect denoised similar subblocks and reconstruct the original signal more closely. A denoising algorithm based on an adaptive threshold is employed to remove noise and a measure function is designed to select the optimal signal subband that contains the main fault information. Experimental results demonstrate that the proposed algorithm outperforms other fault diagnosis methods in fault frequency detection.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Dong Zhang, Taotao Zhou
Summary: This paper proposes a new deep convolutional neural network (DCNN) and transfer learning (TL) combined approach for fault diagnosis in industrial systems to handle different fault types. By converting one-dimensional time-series signals into grayscale images using a signal processing method and training an optimal DCNN with ImageNet datasets, this method shows improved generalization performance through TL.
Article
Engineering, Marine
Chenxiang Lu, Xiangyang Zeng, Qiang Wang, Lu Wang, Anqi Jin
Summary: In this paper, a target spectrum reconstruction method is proposed under a sparse Bayesian learning framework with joint sparsity priors, which achieves high-resolution target separation in the angular domain and constant beamwidth over a frequency range. Experimental results demonstrate that the proposed method effectively suppresses interference and preserves more detailed spectral structures compared to conventional beamformers. Furthermore, a frequency shift-invariant feature extraction method is proposed to overcome the problem of frequency drift caused by target motion and underwater channel effects, and the classification experiments show that the proposed methods outperform traditional beamformers and Mel-frequency features, improving underwater recognition performance.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Yang Yang, Hui Liu, Lijin Han, Pu Gao
Summary: This article proposes a new rolling bearing status feature extraction method based on variational mode decomposition (VMD) and improved envelope spectrum entropy (IESE). The feasibility of the proposed method is verified by three experimental cases. Compared with other methods, the performance of this proposed method is better.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Md Junayed Hasan, M. M. Manjurul Islam, Jong-Myon Kim
Summary: This paper proposes a crack diagnosis framework that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process, significantly outperforming classical methods with average performance improvements of 2.36-20.26%.
Review
Energy & Fuels
Shiza Mushtaq, M. M. Manjurul Islam, Muhammad Sohaib
Summary: This paper comprehensively reviews the developments in rotating bearing fault diagnosis over the past decade, including data-driven fault diagnosis frameworks, signal processing techniques, machine learning methods, and deep learning algorithms. The paper also discusses public datasets widely used in bearing fault diagnosis experiments, as well as a comparison of various machine learning techniques and deep learning algorithms utilized for the diagnosis of rotary machines bearing fault.
Article
Chemistry, Analytical
Md Junayed Hasan, M. M. Manjurul Islam, Jong-Myon Kim
Summary: This study proposes an autonomous diagnostic system that combines signal-to-image transformation techniques and convolutional neural network (CNN)-aided multitask learning (MTL) to extract statistical features from bearing fault signals. By fusing information from multi-domains, a 2-D composite image is created, which is highly effective in identifying faults even with variable speeds and loads.
Article
Environmental Sciences
Muhammad Sohaib, Ayesha Ghaffar, Jungpil Shin, Md Junayed Hasan, Muhammad Taseer Suleman
Summary: An automated sleep stage categorization using empirical mode decomposition and stacked autoencoders showed improved results for EEG signal classification. The empirical mode decomposition effectively denoised the non-stationary signals, and the stacked autoencoders further improved the classification performance.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Energy & Fuels
Muhammad Sohaib, Shahid Munir, M. M. Manjurul Islam, Jungpil Shin, Faisal Tariq, S. M. Mamun Ar Rashid, Jong-Myon Kim
Summary: This paper presents a data-driven gearbox fault diagnosis system that addresses the issue of variable working conditions. It proposes an improved feature extraction process and multi-task learning to enhance the overall performance of the fault diagnosis model.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Chemistry, Analytical
Prajoy Podder, Sanchita Rani Das, M. Rubaiyat Hossain Mondal, Subrato Bharati, Azra Maliha, Md Junayed Hasan, Farzin Piltan
Summary: This paper proposes a new deep learning framework called LDDNet for analyzing lung diseases, such as COVID-19 and pneumonia, from chest CT scans and X-ray images. LDDNet enhances the base DenseNet201 model with additional layers and optimized hyper-parameters. Different datasets, including CT scan and X-ray images, were used to evaluate the performance of LDDNet with various optimizers. The results show that LDDNet achieves high accuracy, recall, precision, and F1 score in detecting COVID-19-affected patients.
Review
Chemistry, Analytical
Md. Khaliluzzaman, Ashraf Uddin, Kaushik Deb, Md Junayed Hasan
Summary: This paper provides a comprehensive overview of the advancements, challenges, and limitations in gait recognition using deep learning methods. It examines gait datasets, analyzes the performance of state-of-the-art techniques, presents a taxonomy of deep learning methods, and suggests future research directions.
Article
Computer Science, Interdisciplinary Applications
Prajoy Podder, Fatema Binte Alam, M. Rubaiyat Hossain Mondal, Md Junayed Hasan, Ali Rohan, Subrato Bharati
Summary: Due to its high transmissibility, COVID-19 has significantly burdened healthcare systems worldwide. X-ray imaging of the chest has proven to be an effective and cost-efficient method for detecting and diagnosing COVID-19 patients. In this study, a deep learning model using modified DenseNet architecture and optimized hyperparameters achieved impressive accuracy and recall rates for detecting COVID-19 patients, as well as for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on and its low false-positive and false-negative rates suggest its potential as a reliable diagnostic tool for various lung diseases.
Review
Computer Science, Interdisciplinary Applications
Rezaul Haque, Saddam Hossain Laskar, Katura Gania Khushbu, Md Junayed Hasan, Jia Uddin
Summary: With the growth of the internet, social networking sites have become an important platform for user-generated content related to medications and healthcare. Leveraging machine learning approaches such as sentiment analysis can provide valuable insights from the vast amount of comments. However, the large volume of comments makes manual review impractical, and drug assessments can serve as a valuable source of medical information for healthcare professionals and the public. Previous studies have used machine learning and deep learning algorithms to categorize comments, with deep learning classifiers performing better. This study aimed to improve previous research by applying sentiment analysis to medication reviews and training machine learning and deep learning algorithms. The findings showed that the random forest trained on the count vectorizer achieved the highest accuracy and F1 score among the machine learning algorithms, while the bidirectional LSTM model trained on GloVe embedding performed even better. Therefore, using appropriate natural language processing and machine learning algorithms can lead to superior results compared to earlier studies.
Proceedings Paper
Computer Science, Artificial Intelligence
M. M. Manjurul Islam, Cormac McAteer, Girijesh Prasad
Summary: This paper introduces an efficient wafer defects pattern recognition methodology based on deep convolutional neural networks (DCNN) for automated classification. The proposed DCNN, a revamped LeNet-5 model, consists of seven layers including three convolutional and two pooling layers. The experiments on publicly available semiconductor wafers datasets demonstrate the high efficiency of the proposed model with an accuracy of 99.22% in testing.
2023 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Zahoor Ahmad, Md Junayed Hasan, Jong-Myon Kim
Summary: This paper proposes a new fault diagnosis framework for Centrifugal Pump (CP) fault diagnosis. The framework utilizes wavelet packet transform (WPT) for preprocessing the vibration signal (VS) and extracting features. It also employs a discriminative-factor-based feature selection method and the K-nearest neighbor (KNN) algorithm for classification, achieving better results compared to existing methods.
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zahoor Ahmad, Md Junayed Hasan, Jong-Myon Kim
Summary: This paper proposes a vibration imaging-based diagnosis approach for bearing under variable speed conditions. It preprocesses and transforms the signals into vibration images to capture the health patterns, and uses a convolutional neural network for classification. The experimental results show excellent classification accuracy.
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021
(2022)
Article
Multidisciplinary Sciences
Niloy Sikder, Abu Shamim Mohammad Arif, M. M. Manjurul Islam, Abdullah-Al Nahid
Summary: Electric motors play a crucial role in industrial applications, with bearing faults being a common issue. Research has proposed various methods to detect faults in motors, but there is still room for improvement in this field.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
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.