Article
Automation & Control Systems
Xianzhi Wang, Shubin Si, Yongbo Li
Summary: A fault diagnosis scheme based on multiscale diversity entropy (MDE) and extreme learning machine (ELM) is proposed in this article, which quantifies dynamical complexity and provides a comprehensive feature description for pattern identification of rotating machinery. The effectiveness of the proposed MDE method is verified through simulated signals and experimental signals from bearing test and dual-rotator of aeroengine test, showing superior classification accuracy compared with existing approaches.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Zhe Chen, Shiqing Tian, Xiaotao Shi, Huimin Lu
Summary: This article proposes a novel multiscale shared-learning network architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. Experimental results demonstrate the superiority of this method in fault diagnosis for bearings and gearboxes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Mechanical
Fei Chen, Liyao Zhang, Wenshen Liu, Tingting Zhang, Zhigao Zhao, Weiyu Wang, Diyi Chen, Bin Wang
Summary: In this study, a fault diagnosis method for rotating machinery based on improved multiscale attention entropy and random forests is proposed. The experimental results show that the proposed method achieves the optimal diagnostic performance on two different fault datasets and exhibits strong adaptability in practical applications.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Multidisciplinary
Li Zou, Heung Fai Lam, Jun Hu
Summary: This study proposes a novel fault diagnosis method utilizing adaptive resize-residual deep neural networks, which converts vibration signals into time-frequency images using continuous wavelet transform, enhances image contrast with histogram equalization algorithm, and achieves superior recognition accuracy.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Haopeng Liang, Jie Cao, Xiaoqiang Zhao
Summary: This study proposes a small sample rotating machinery fault diagnosis method based on a multibranch and multiscale dynamic convolutional network (MBSDCN), which can accurately diagnose faults under small sample and noisy conditions through the feature splitting strategy, channel reconstruction attention mechanism, and novel multiscale feature extraction model.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Varun Khemani, Michael H. Azarian, Michael G. Pecht
Summary: This paper introduces a novel technique called learnable wavelet scattering networks for fault diagnosis of circuits and rotating machinery. By optimizing the operators of this network, higher fault diagnosis accuracy can be achieved, and it also has good generalization and transfer learning performance in fault diagnosis in different domains.
Article
Computer Science, Artificial Intelligence
Changyuan Yang, Sai Ma, Qinkai Han
Summary: Fault diagnosis is an important technology in intelligent manufacturing to maintain high quality and low failure rate. This research proposes a novel feature selection method named unified discriminant manifold learning (UDML) for accurately diagnosing faults in rotating machinery. UDML unifies local linear relationship, distance between adjacent points, and intra-class and inter-class variance, effectively preserving the local structure, global information, and label information of high-dimensional features. Through experimental verifications and comparisons with classical feature selection algorithms, the proposed method achieves more accurate fault diagnosis in rotating machinery.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Industrial
Yadong Xu, Xiaoan Yan, Ke Feng, Xin Sheng, Beibei Sun, Zheng Liu
Summary: This study proposes an attention-based multiscale denoising residual convolutional neural network (AM-DRCN) for fault diagnosis in noisy industrial scenes. By introducing a multiscale denoising module, a feature enhancement module, and a joint attention module, the model effectively extracts key features and filters out noise.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Computer Science, Interdisciplinary Applications
Masoud Jalayer, Carlotta Orsenigo, Carlo Vercellis
Summary: The paper proposes a new feature engineering model and develops a novel Convolutional Long Short-Term Memory (CLSTM) model to improve the accuracy of fault detection and diagnosis of rotating machinery. The study demonstrates the superior performance of the model in fault diagnosis on different datasets.
COMPUTERS IN INDUSTRY
(2021)
Article
Automation & Control Systems
Changyuan Yang, Sai Ma, Qinkai Han
Summary: This paper proposes a robust discriminant latent variable manifold learning (RDLVML) algorithm for fault diagnosis of rotating machinery. By selecting features of high-dimensional fault data and extracting low-dimensional fault features with better discrimination, the accuracy of fault diagnosis is improved. A novel weighted neighborhood graph is proposed by constructing the q-Rényi and Prime kernel function to suppress the interference of outliers and noise, making the RDLVML algorithm more robust. Furthermore, a fault diagnosis method for rotating machinery based on RDLVML is presented, which achieves more accurate results compared to classical feature selection algorithms through experimental verifications.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Aosheng Tian, Ye Zhang, Chao Ma, Huiling Chen, Weidong Sheng, Shilin Zhou
Summary: In this paper, a wavelet-based self-attention network called Wavelet-SANet is proposed for machinery fault diagnosis. The network combines frequency-oriented fusion modules and Transformer modules to suppress noise in both frequency and time domains, leading to improved diagnostic performance for machinery faults.
Review
Acoustics
Shoucong Xiong, Shuai He, Jianping Xuan, Qi Xia, Tielin Shi
Summary: The article introduces an enhanced deep learning algorithm named the multilevel correlation stack-deep residual network to tackle the challenges of time-frequency analysis in vibration-based fault diagnosis. Experimental results demonstrate its superior classification performance in diagnosing rolling bearing faults, indicating significant potentials for practical applications.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Physics, Multidisciplinary
Qiyang Xiao, Sen Li, Lin Zhou, Wentao Shi
Summary: This paper proposes an intelligent diagnosis method for rotating machinery faults using improved variational mode decomposition and convolutional neural network to process non-stationary signals. The method automatically optimizes the number of modes and extracts time-domain features for fault diagnosis. The decomposed signal components are analyzed and correlated, and the high correlated components are selected to reconstruct the original signal. The method utilizes the continuous wavelet transform to extract two-dimensional time-frequency domain features, which are then applied to a convolutional neural network for fault feature identification.
Article
Computer Science, Artificial Intelligence
Wenqing Wan, Jinglong Chen, Zitong Zhou, Zhen Shi
Summary: Fault diagnosis is crucial for the security of rotating machinery operations. This article proposes a self-supervised simple Siamese framework based on the contrastive learning algorithm for bearing fault diagnosis. The framework learns invariant characteristics of fault samples by maximizing the similarity between two views of each inputted sample. After fine-tuning with a small subset of labeled data, the network achieves satisfactory performance in bearing fault diagnosis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Wenliao Du, Pengjie Hu, Hongchao Wang, Xiaoyun Gong, Shuangyuan Wang
Summary: In this article, a one-dimension in tandem with 2-D joint convolutional neural network (1D-2D JCNN) is proposed for rotating machinery fault diagnosis. It uses 1-D convolution to obtain multiscale feature vectors, constructs them into 2-D maps, and feeds them into a 2-D convolutional neural network. Experimental results show the excellent classification performance of 1D-2D JCNN in bearing and gear fault diagnosis.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Multidisciplinary
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes an iterative morphological difference product wavelet (MDPW) method for weak fault feature extraction and fault diagnosis of rolling bearing. The MDPW achieves noise suppression and fault feature enhancement through iterative computation and optimized parameters, and performs fault identification by analyzing the occurrence of fault defect frequencies in the spectrum.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Duo Shan, Changming Cheng, Lingjian Li, Zhike Peng, Qingbo He
Summary: Fault diagnosis of gearboxes is crucial for the safe operation of industrial systems. This article proposes a novel semisupervised method for gearboxes using weighted label propagation and virtual adversarial training to overcome the shortage of labeled data in practical industrial applications. The proposed method leverages unlabeled data for pseudo label inference and introduces sample weights to reduce the negative effect of noisy labels. Experimental results demonstrate the effectiveness of the proposed semisupervised approach in leveraging unlabeled data to solve the labeled data shortage.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes a novel method for rotating machinery fault detection, which achieves multi-sensor data fusion using improved cyclic spectral covariance matrix (ICSCM) and motor current signal analysis. The proposed method adaptively acquires multi-sensor mode components and constructs ICSCM using sample entropy to preserve the interaction relationship between different sensors. The ICSCM is then incorporated into an extreme learning machine classifier for fault type identification. The proposed method has achieved satisfactory results and more reliable diagnosis accuracy than other state-of-the-art algorithms in rotating machinery fault detection.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Junchao Guo, Qingbo He, Yang Yang, Dong Zhen, Fengshou Gu, Andrew D. Ball
Summary: This paper proposes a novel AM-FM demodulation method based on LMSB for extracting fault features from gearbox signals. The method can simultaneously demodulate multi-mesh frequency bands and multi-modulation components. The effectiveness of LMSB is demonstrated through numerical simulations and experimental analysis, showing its superiority over other demodulation techniques. This research provides a new perspective for gearbox fault detection.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Review
Computer Science, Information Systems
Siliang Lu, Jingfeng Lu, Kang An, Xiaoxian Wang, Qingbo He
Summary: Edge computing is a promising paradigm for IoT-based machine signal processing and fault diagnosis, as it offloads computations onto IoT edge devices, improving computation efficiency and reducing storage and computation workloads on cloud servers.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu
Summary: This article proposes a novel fault detection scheme based on cyclic morphological modulation spectrum (CMMS) and hierarchical Teager permutation entropy (HTPE) for rotating machinery. The scheme uses CMMS to analyze the measured signal and obtain CMMS slices with different frequency bands, and utilizes HTPE for improved feature selection. Experimental results show that the proposed scheme effectively obtains fault features and achieves accurate fault classification and recognition.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Mechanical
Xiaoluo Yu, Changming Cheng, Yang Yang, Minggang Du, Qingbo He, Zhike Peng
Summary: This paper proposes a Maximumly Weighted Iteration (MWI) approach to solve ill-conditioned inverse problems in dynamics. The ill-condition of the system coefficient matrix is controlled by iterative weighted decomposition and a weighted term, avoiding matrix inversion. The numerical results show that MWI outperforms Truncated Singular Value Decomposition and Tikhonov regularization in terms of accuracy and anti-noise property. Two application cases demonstrate the potential of MWI in engineering practice.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Qihang Wu, Xiaoxi Ding, Qiang Zhang, Rui Liu, Shanshan Wu, Qingbo He
Summary: This study proposes an Intelligent Edge Fault Diagnosis System (IEDS) based on a lightweight intelligent architecture called Multiplication-Convolution Sparse Network (MCSN). The system enables real-time data processing, fault identification, and fault data filtering with high accuracy and lightweight performance.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Jixu Zhang, Tianqi Li, Qingbo He, Zhike Peng
Summary: In this article, a novel method is proposed to deal with the performance degradation of DOA estimation caused by the interaction of instantaneous frequencies in low SNR. The method adopts the unified general parameterized TF transform with multiple sensors and considers the sparseness of the signal in the angle domain. It enhances the ability to extract valid TF points and improves the accuracy of DOA estimation.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Changming Cheng, Duo Shan, Yuxuan Teng, Baoxuan Zhao, Zhike Peng, Qingbo He
Summary: In order to ensure the reliability and security of gearboxes, accurate and efficient fault diagnosis is highly valued. However, most deep learning methods require sufficient labeled data, which is often lacking in practical industrial applications. Therefore, a semisupervised approach based on a hybrid classification network and weighted pseudo-labeling is proposed to address this problem.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Sha Wei, Yang Yang, Minggang Du, Qingbo He, Zhike Peng
Summary: The decomposition problem for multiple sinusoidal component signals has been developed in the past decades. However, many complex time series are made up of wave-shape components, which invalidates signal decomposition methods based on sinusoidal components. In this article, a varying wave-shape component decomposition (VWCD) method is proposed to extract time-varying and weak characteristics of wave-shape components from a multicomponent signal. The potential and effectiveness of the proposed VWCD method are verified by some simulated signals with different signal-to-noise ratios, a real-world electroencephalography seizure signal, and an experimental chest wall vibration signal from a microwave vital sign monitoring system.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Tianyu Liu, Bojian Chen, Weiguo Huang, Lisa Jackson, Lei Mao, Qingbo He, Qiang Wu
Summary: The reliability of manufacturing tooling is crucial for intelligent manufacturing processes. However, limited and unbalanced data pose challenges for accurate tool wear assessment. This study proposes a combined CGAN-HQOA model that generates tool data with higher similarity to real data, resulting in improved tool wear condition assessment using convolutional neural network. The effectiveness of the proposed method is verified using unbalanced data and different cutting tools, demonstrating the superiority of the generated data and the accuracy of tool wear assessment. These findings are valuable for practical applications with limited test data.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Multidisciplinary Sciences
Chong Li, Xinxin Liao, Zhi-Ke Peng, Guang Meng, Qingbo He
Summary: Bio-mechanoreceptors have inspired the design of micro-motion sensors, but achieving high sensitivity and broadband sensing remains a challenge. In this study, researchers developed a Metamaterial Mechanoreceptor (MMR) that mimics rat vibrissae. The MMR uses piezoelectric resonators with distributed zero effective masses, enabling highly sensitive and broadband micro-motion sensing. The MMR offers promising applications in spatio-temporal sensing, remote-vibration monitoring, and smart-driving assistance.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Qihang Wu, Xiaoxi Ding, Linhua Zhao, Rui Liu, Qingbo He, Yimin Shao
Summary: This study proposes a multiplication-convolution sparse network (MCSN) with interpretable sparse kernels, which effectively mines fault features from spectrum signals and enhances the interpretability and reliability of the model. Experimental results show that the proposed MCSN achieves high fault recognition accuracy and outperforms other open-source network models.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Kui Hu, Qingbo He, Changming Cheng, Zhike Peng
Summary: This paper proposes an adaptive incremental diagnosis model (AIDM) with incremental capabilities, which can achieve quick reconstruction and updating by adding new output nodes and adopting knowledge distillation loss. A new dynamic weight correction algorithm is also introduced to realize the stable and reliable incremental training and dynamic updating of IFD models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
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.
