Article
Engineering, Multidisciplinary
Zewen Zhou, Bingyan Chen, Baoshan Huang, Weihua Zhang, Fengshou Gu, Andrew D. Ball, Xue Gong
Summary: This paper proposes a novel blind deconvolution method called maximum negative entropy deconvolution (MNED) for detecting repetitive transients caused by bearing faults. MNED optimizes the filter coefficients by maximizing negative entropy instead of kurtosis, and resolves the instability and high computational time issues of existing blind deconvolution methods. Experimental results demonstrate the effectiveness of MNED in enhancing repetitive transients and its advantages in fault detection and computational efficiency compared to existing methods.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Information Systems
Zichang Liu, Siyu Li, Rongcai Wang, Xisheng Jia
Summary: This article proposes a method for extracting faint faults in rolling bearings based on SSA, VMD, and MCKD, with a success rate of up to 100%. By adaptively determining the parameters in VMD and MCKD, it enhances the fault impact components in the signals and effectively extracts the fault characteristic frequencies of rolling bearings.
Article
Engineering, Multidisciplinary
Jialu Tang, Jun Zhou, Xing Wu, Tao Liu, Xiaoqin Liu
Summary: This paper proposes a novel compound fault blind extraction method, named ISCA-IMCKD, to separate and extract fault frequencies from strong background noise in the gathered signals. The method involves transforming the signals into time-frequency area using STFT, optimizing the ISCA method using improved fuzzy C-means clustering, and extracting the estimated source signal using the membership degree of clustering results. The IMCKD is employed to enhance the characteristics of the projected source signals, and the defect frequencies of the composite faults are finally extracted by envelope analysis. The proposed method is validated using simulation experiments and measured data, showing improved efficiency in rolling bearing defect detection while saving time cost.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Mechanical
Yonghao Miao, Chenhui Li, Huifang Shi, Te Han
Summary: This paper proposes a novel deep network-based maximum correlated kurtosis deconvolution (MCKD-DeNet) method to solve the problems of traditional deconvolution methods using neural network and correlated kurtosis. The proposed method initializes the filter using the Hanning window and learns the fault feature with different filters. Correlated kurtosis is used as the cost function to train the neural network, and the input period is estimated by calculating the autocorrelation of the most informative filtered signal. Finally, the component with the most fault information is selected as the output of MCKD-DeNet based on the correlation coefficient.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Limu Qin, Gang Yang, Qi Sun
Summary: This paper presents a new blind deconvolution (BD) method, named maximum correlation Pearson correlation coefficient deconvolution (MCPCCD), which improves the recovery of periodic impulses. The method constructs a new objective function that combines correlation Pearson correlation coefficient (CPC) and signal fidelity term (SFT), and maximizes the function to obtain the optimal FIR filter. A preprocessing step is also introduced to reduce the requirement for periodic prior.
Article
Chemistry, Analytical
Shishuai Wu, Jun Zhou, Tao Liu
Summary: This paper proposes a fault feature extraction approach combining adaptive variational modal decomposition (AVMD) and improved multiverse optimization (IMVO) algorithm parameterized maximum correlated kurtosis deconvolution (MCKD), which can efficiently extract the acoustic signal fault features of compound faults in rolling bearings.
Article
Engineering, Mechanical
Cristian Lopez, Dong Wang, Angel Naranjo, Keegan J. Moore
Summary: This study investigates the connection between minimum entropy deconvolution and maximum kurtosis deconvolution in blind filtering, incorporating a generalized Rayleigh quotient to form a filter that extracts a signal with the sparsest envelope spectrum. The proposed method is validated using both simulated and real experimental data, demonstrating its effectiveness in detecting multiple faults with a single measurement set.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Haiyang Pan, Xuelin Yin, Jian Cheng, Jinde Zheng, Jinyu Tong, Tao Liu
Summary: As an effective method for roller bearing fault diagnosis, the Adaptive Periodic Mode Decomposition (APMD) method has a great ability to extract repeated transients. The Maximum Likelihood Estimation (MLE) method is used in APMD to calculate projection energy, and the main period of the signal is determined by the maximum projection energy. However, when there is a large amount of noise, the accuracy of the period estimation is affected. To address this issue, a Periodic Component Pursuit-based Kurtosis Deconvolution (PCPKD) method is proposed, which uses Maximum Reweighted Kurtosis Deconvolution (MRKD) to denoise the signals and reduce the influence of noise energy on periodic estimation. The experimental results on roller bearing compound fault diagnosis show that PCPKD is effective in enhancing signal period and segmenting multi-frequency components.
MECHANISM AND MACHINE THEORY
(2023)
Article
Physics, Multidisciplinary
Shan Wang, Pingjuan Niu, Zijian Qiao, Yongfeng Guo, Fuzhong Wang, Chenghao Xu, Shuzhen Han, Yan Wang
Summary: This study introduces an adaptive unsaturated stochastic resonance method using maximum cross-correlated kurtosis as a signal detection index to locate fault signals, combining cross correlated coefficient and spectrum kurtosis. The method is validated with actual vibration signals from motor and gear bearings, showing it is more suitable for explaining periodic impulse components compared to other denoising methods.
CHINESE JOURNAL OF PHYSICS
(2021)
Article
Chemistry, Multidisciplinary
Weihan Li, Yang Li, Ling Yu, Jian Ma, Lei Zhu, Lingfeng Li, Huayue Chen, Wu Deng
Summary: The KMVMD-PGMCKD method integrates the advantages of KMVMD and PGMCKD to construct a novel weak fault feature extraction model, which effectively extracts the fault features of bearing rolling elements and accurately diagnoses weak faults under variable working conditions.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Dongjie Li, Mingyue Li, Liu Yang, Xueying Wang, Fuyue Zhang, Yu Liang
Summary: This paper proposes a K-value calculation method of variational mode decomposition (VMD) based on singular value kurtosis difference spectrum, combined with the improved maximum correlation kurtosis deconvolution (MCKD) to solve the difficulty in fault feature extraction for rolling bearings under strong noise conditions. The method involves denoising, filtering, and reconstructing the faulty signals, as well as optimizing the filter parameters to enhance signal characteristics. Finally, the method uses the envelope spectrum to extract eigenfrequencies for fault diagnosis and location determination of rolling bearings.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Chuliang Liu, Jianping Tan, Zhonghe Huang
Summary: The distortion of signal caused by transmission path poses difficulties for feature extraction in bearing fault diagnosis. In this paper, a novel method called maximum correntropy criterion-based blind deconvolution (MCCBD) is proposed to eliminate the influence of transmission path and recover impulsive features. The method uses an autocorrentropy-based strategy to automatically evaluate the impulse period, enhancing fault impulses buried by noise.
Article
Engineering, Mechanical
Ben Cui, Panpan Guo, Wenbin Zhang
Summary: A fault diagnosis method for rolling bearings based on the maximum correlation kurtosis deconvolution (MCKD), singular spectral decomposition (SSD), and teager energy operator (TEO) was proposed to overcome the challenges posed by noise contamination and extraction of fault character frequency. The method involved denoising and preprocessing of vibration signals using MCKD, followed by SSD for selecting optimal components. Finally, the energy spectra of the selected components were calculated to extract characteristic frequencies for fault diagnosis. Simulation and experimental analysis validated the feasibility and superiority of the proposed method compared to empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD).
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2023)
Article
Instruments & Instrumentation
Yuanyuan Sheng, Huanyu Liu, Lu Li, Junbao Li
Summary: This work proposes a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. The model effectively reduces noise interference through PSF denoising and enhances cyclic fault signals using FWEO. The presence of faults is identified by observing the squared envelope spectrum. Experimental results show that the proposed model has high anti-noise performance and robustness, and can extract fault frequencies well.
REVIEW OF SCIENTIFIC INSTRUMENTS
(2023)
Article
Engineering, Multidisciplinary
Jun Zhou, Shi-Shuai Wu, Tao Liu, Xing Wu
Summary: This paper proposes a method for extracting compound fault features, which uses the improved particle swarm optimization algorithm, maximum correlation kurtosis deconvolution, improved variational mode decomposition, and cyclic autocorrelation function to accurately extract the features of compound faults in vibration signals. This method not only improves the accuracy of feature extraction, but also solves the problem of parameter selection.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Yuqing Zhou, Bintao Sun, Weifang Sun, Zhi Lei
Summary: This study proposes a new tool condition monitoring method that utilizes a few appropriate feature parameters of acoustic sensor signals and a two-layer angle kernel extreme learning machine, achieving more accurate predictions of tool condition.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Engineering, Multidisciplinary
Jingshan Huang, Binqiang Chen, Yang Li, Weifang Sun
Summary: The paper introduces a theory of centralized multiresolution analysis for vibration measurement in condition monitoring of rotating machinery, revealing implicit fractal geometry properties in the theoretical framework. Within this framework, a concept of nested centralized wavelet packet space is introduced to describe self-similarity phenomenon, and a generalized CMR characterized by tunable and flexible frequency-scale topology configuration is established. This approach combines improved transient signature dictionary with stationary signature dictionary to enhance fault feature extraction in multiple modes coupled vibration measurements.
Article
Engineering, Multidisciplinary
Gaofeng Zhi, Dedao He, Weifang Sun, Yuqing Zhou, Xiaoming Pan, Chen Gao
Summary: This paper introduces a new TCM method based on EGNN for small training datasets, using CNN to extract features and a fully connected graph to predict tool wear condition, demonstrating superior performance in milling TCM experiments.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Green & Sustainable Science & Technology
Zhi Lei, Qinsong Zhu, Yuqing Zhou, Bintao Sun, Weifang Sun, Xiaoming Pan
Summary: This study proposes a tool wear estimation method based on workpiece vibration signals, which achieves efficient tool wear estimation by optimizing the initialized weights and thresholds of the ELM model. Experimental results demonstrate that the performance of the proposed method outperforms other three methods.
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY
(2021)
Article
Automation & Control Systems
Weifang Sun, Jiyan Yi, Guang Ma, Fengping Li, Xiaogang Li, Guang Feng, Chengji Lu
Summary: This article introduces a vision-based method for in situ measurement of film cooling hole dimensions in aero-engines during the laser beam drilling process. The method utilizes complex wavelet transform and Gini index-based extraction for edge information. Experimental results show small measurement errors, providing a new approach for in situ measurement in this field.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Linzhi Xia, Yizhu Shi, Hongjie Lin, Houyuan Zheng, Xincheng Cao, Binqiang Chen, Yuqing Zhou, Weifang Sun
Summary: This paper proposes an improved vision-based method for segmentation and quantitative evaluation of tool wear monitoring. Experimental results show its effectiveness.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Yizhu Shi, Yuqing Zhou, Yan Ren, Weifang Sun, Jiawei Xiang
Summary: This research proposes a hybrid method for identifying the spring energy storage state in circuit breaker operating mechanisms. The method utilizes the Gramian angular field (GAF) to represent the dynamic characteristics evolution process and combines it with a convolutional block attention module (CBAM) and residual network (ResNet). Experimental results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Multidisciplinary
Yuqing Zhou, Wei Sun, Canyang Ye, Bihui Peng, Xu Fang, Canyu Lin, Gonghai Wang, Anil Kumar, Weifang Sun
Summary: Accurate tool condition monitoring is crucial for the development of the manufacturing industry, and machine-learning models have shown promising results in this field. However, the limited availability of training samples due to high experimental costs poses a challenge for the performance of these models. To address this, a time-series dimension expansion and transfer learning method is proposed to improve tool condition monitoring for small samples.
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
(2023)
Article
Engineering, Electrical & Electronic
Shiliang Feng, Zewei Wang, Wenxi Jiang, Binqiang Chen, Zuowen Yuan, Weifang Sun
Summary: The authors propose a new method for evaluating the performance of mechanical systems based on time-domain signal analysis. By constructing a projection matrix and calculating the difference between reference and testing signals, abnormal states can be identified and the health of the mechanical system can be quantitatively evaluated. The proposed method is validated in experiments on bearing performance degradation, bearing fault diagnosis, and milling tool wear, showing positive results.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Tailong Wu, Yuan Yao, Zhihao Li, Binqiang Chen, Yue Wu, Weifang Sun
Summary: A novel wavelet-enhanced dual-tree residual network is proposed in this paper for accurate prediction of the remaining useful life of circuit breaker operating mechanisms, which is validated through experiments. The method has potential applications in smart grid and green energy construction, as well as in the field of circuit breaker prognostics.
JOURNAL OF POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Yuqing Zhou, Hongche Wang, Gonghai Wang, Anil Kumar, Weifang Sun, Jiawei Xiang
Summary: In recent years, deep learning-based methods have made remarkable achievements in the intelligent fault diagnosis of rotating machinery. However, the lack of labeled and large unlabeled samples in actual industrial scenes affects the performance of supervised learning methods. This paper proposes a novel semi-supervised fault diagnosis method based on multiscale permutation entropy (MPE) enhanced contrastive learning (CL). Experimental results in gearbox and milling tool fault diagnosis experiments show that the proposed MPE-CL method outperforms other benchmark methods with classification accuracy above 95.4% and 96.0% when the labeled training dataset size is 50/class, respectively.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Jinchuan Shi, Yan Ren, Jiyan Yi, Weifang Sun, Hesheng Tang, Jiawei Xiang
Summary: The multisensor fusion technique faces challenges in representing information and may lead to a decline in ability. A new technique utilizing processed images is proposed for fault diagnosis, achieving high accuracy in diagnosing faults in axial piston pumps and hydraulic reversing valves.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Weifang Sun, Yuqing Zhou, Jiawei Xiang, Binqiang Chen, Wei Feng
Summary: This article introduces an enhanced framework using acquired time-series signals for fault identification, with an improved Hankel matrix-based method showing promising prospects in engineering fault detection applications.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Qinsong Zhu, Bintao Sun, Yuqing Zhou, Weifang Sun, Jiawei Xiang
Summary: This article discusses a method of augmenting the training dataset for AI classifiers by combining numerical simulation and generative adversarial networks, addressing the challenge of obtaining large training samples in TCM applications. Through this approach, experiments show that the classification accuracies of several AI classifiers trained on augmented datasets are close to or equal to 100%.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Multidisciplinary
Weifang Sun, Xincheng Cao, Binqiang Chen, Yuqing Zhou, Zhihuang Shen, Jiawei Xiang
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2020)
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.
