Article
Engineering, Electrical & Electronic
Huimin Zhao, Haodong Liu, Yang Jin, Xiangjun Dang, Wu Deng
Summary: The study introduced a new data-driven feature extraction method to enhance the accuracy of RUL prediction for rolling bearings. By combining different algorithms, a rapid, stable, and adaptable RUL prediction model specifically for rolling bearings was established.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Industrial
Guofa Li, Jingfeng Wei, Jialong He, Haiji Yang, Fanning Meng
Summary: This study focuses on the prediction of Remaining Useful Life (RUL) in rolling bearing prognostics and health management. It proposes an improved Kalman filtering method that uses variational Bayesian technique to adaptively describe noise information and considers linear and nonlinear factors of multi-channel signals to recognize the degradation stage transition point of bearing as Time to Start Prediction (TSP). The proposed method is validated on XJTU-SY and IMS-Rexnord bearing data, showing accurate recognition of TSP and improved long-term prediction accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Jingna Liu, Rujiang Hao, Qiang Liu, Wenwu Guo
Summary: Fault in rolling element bearings is a common issue in mechanical systems, which can result in equipment malfunction, accidents, and losses. Periodical monitoring of bearings is crucial for reducing unplanned maintenance and improving machine reliability. However, existing methods for detecting bearing faults involve subjective factors. To address this, the paper presents a hybrid real-time method that utilizes a dynamic sigma interval and voting mechanism to predict the starting time of a bearing fault. Experimental results show that this approach effectively reduces subjective interference and accurately predicts the remaining useful life of the bearing.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Acoustics
Rui Bai, Yongbo Li, Khandaker Noman, Shun Wang
Summary: The prediction of remaining useful life (RUL) of rolling bearings is crucial for reducing downtime and improving productivity. This study proposes a Bayesian deep learning method based on diversity entropy to quantify uncertainty and improve prediction accuracy. Experimental results demonstrate the superiority of the proposed method for RUL prediction of rolling bearings.
JOURNAL OF VIBRATION AND CONTROL
(2022)
Article
Computer Science, Hardware & Architecture
Jinhua Mi, Lulu Liu, Yonghao Zhuang, Libing Bai, Yan-Feng Li
Summary: In this article, a synthetic feature processing method is proposed for the remaining useful life prediction of rolling bearings. The method addresses the shortcomings of feature selection, feature fusion, and health state segment, and achieves more accurate and robust prediction results.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Electrical & Electronic
Hengyu Liu, Rui Yuan, Yong Lv, Hewenxuan Li, Ersegun Deniz Gedikli, Gangbing Song
Summary: This article focuses on the extraction of real-time damage feature and the prediction of remaining useful life (RUL) in predictive maintenance of rolling bearings. It proposes the algorithm of segmented relative phase space warping (SRPSW) and a strategy combining double exponential model (DEM) and particle filter (PF) to predict the RUL. The proposed approach provides a new avenue for predictive maintenance of bearings.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Yuxiong Li, Xianzhen Huang, Tianhong Gao, Chengying Zhao, Shangjie Li
Summary: In this paper, a novel method for remaining useful life (RUL) prediction considering multiple degradation patterns is developed. The method estimates degradation parameters and predicts RUL through offline and online algorithms. Comparison with other methods shows that the proposed method achieves higher accuracy.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Dong Guo, Zhi Cao, Hongyong Fu, Zhenxiang Li
Summary: The rolling bearing, as a core part of industrial equipment, plays a crucial role in ensuring safety and reliability. This article proposes a Transformer prediction model with a multiscale gated CNN to accurately predict the remaining useful life (RUL) of rolling bearings. Through experiments on real-life datasets, the proposed model demonstrates superior performance in capturing long-term dependency and accurately estimating RUL.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Fei Jiang, Kang Ding, Guolin He, Huibin Lin, Zhuyun Chen, Weihua Li
Summary: This article proposes a dual-attention-based convolutional neural network for predicting the remaining useful life (RUL) of rolling bearings. The method accurately divides the prediction stages and extracts signal features to improve the effectiveness and robustness of RUL prediction.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Zong Meng, Bo Xu, Lixiao Cao, Fengjie Fan, Jimeng Li
Summary: Rolling bearing is widely used in rotating machinery and plays a significant role in judging the running state and predicting the remaining useful life (RUL) of bearings. This article proposes a novel convolution network called TAFCN, which utilizes a temporal attention fusion (TAF) mechanism to describe the relationship between local and global temporal features in bearing vibration signals. The experimental results demonstrate the robustness and accuracy of TAFCN in predicting the RUL of rolling bearings.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Masashi Kitai, Takuji Kobayashi, Hiroki Fujiwara, Ryoji Tani, Masayuki Numao, Ken-Ichi Fukui
Summary: A new RUL prediction framework based on CNN and HBR is proposed to improve the prediction accuracy of rolling bearings under defect progression, which includes features like using an intermediate variable and considering past degradation conditions in CNN. The framework generates a monotonous RUL prediction curve with a probability distribution, enhancing the RUL prediction accuracy under defect progression.
Article
Engineering, Electrical & Electronic
Lingli Cui, Wenjie Li, Xin Wang, Dezun Zhao, Huaqing Wang
Summary: This article proposes a comprehensive RUL prediction model based on time-varying particle filter (TVPF) for rolling element bearings. The TVPF algorithm can select the optimal state model with a sliding window, track the degradation state of bearings with different degradation trends, and the global/local information fusion technique is used to consider overall information and the latest degraded state. The proposed method outperforms other state-of-art methods in terms of accuracy and robustness.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Peihua Xu, Zhaoyu Tu, Menghui Li, Jun Wang, Xian-Bo Wang
Summary: This paper proposes a bearing RUL prediction method combining relevance vector (RV) machine (RVM) and hybrid degradation model to overcome the shortcomings of existing methods such as low accuracy and reliance on expert experience for parameter estimation. The proposed method extracts bearing degradation characteristics from vibration acceleration signals, determines the bearing first predicting time using the time-varying 3σ criterion, regresses the sequence from initial failure time point to inspection time using differential kernel parameter RVM, and selects the best degradation curve based on similarity and extrapolates it to the failure threshold. Experimental results show that the proposed method has better prediction efficiency than conventional exponential models and overcomes the widespread drawbacks of monotonicity and trend bias in model-based methods.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Automation & Control Systems
Muhammad Gibran Alfarizi, Bahareh Tajiani, Jorn Vatn, Shen Yin
Summary: This article proposes a new data-driven prediction framework for bearing remaining useful life (RUL) utilizing an integration of empirical mode decomposition, random forest (RF), and Bayesian optimization. The proposed approach consists of two main phases: feature extraction and RUL prediction. It is validated using datasets obtained from an actual run-to-failure experiment of roller bearings and shows significant improvement compared to standard approaches.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Guang-Jun Jiang, Jin-Sen Yang, Tian-Cai Cheng, Hong-Hua Sun
Summary: This paper introduces a combined model of convolutional neural network (CNN) and long short-term memory network (LSTM) for predicting the remaining useful life (RUL) in rotational equipment, aiming to support decision-making and ensure safety. By utilizing Bayesian short-term and long-term memory neural networks, the proposed method provides a confidence interval for RUL prediction of rolling bearings. Comparative analysis with existing methods validates the superior performance of the proposed method. Overall, this approach contributes significantly to the prediction of life and condition-based maintenance of bearings and complex rotational systems.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
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.