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Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

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MEASUREMENT 2025年 242卷

作者： Golmohammadi, Ali Hernando, David van den Bergh, Wim Hasheminejad, Navid Univ Antwerp Fac Appl Engn SuPAR Res Grp Antwerp Belgium

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, preprocessing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The pr

关键词： Pavement structural health monitoring multi-sensor data fusion Unsupervised learning Anomaly detection Principle component analysis

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High-Precision machining energy consumption prediction based on multi-sensor data fusion and Ns-Transformer network

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Zhang, Meihang Zhang, Hua Yan, Wei Jiang, Zhigang Tian, Rui Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Res Inst Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Sch Automot & Traff Engn Wuhan 430065 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

Achieving energy efficiency and cost-effectiveness in machining relies on accurate predictions of energy consumption. Despite the advancements in deep learning for predictive applications, precise energy modeling through multi-sensor data integration remains challenging, particularly due to the computational demands of large datasets. To address this, an Ns-Transformer-based strategy leveraging multi-sensor data fusion for high- precision energy consumption prediction is proposed. The methodology begins with data preprocessing, incorporating Lagrange interpolation, Butterworth filtering, principal component analysis, and correlation analysis to identify critical features. Key time-series features are then fused with energy consumption data to create an enriched feature space. The fused features are subjected to feature learning through a dual-layer Ns-Transformer network, followed by the application of linear regression to map the energy consumption state, thereby ensuring prediction accuracy. The framework employs distinct models for training and prediction, sharing parameters to reduce computational overhead. Experimental results demonstrate significant accuracy improvements, with mean squared error reductions exceeding 76% for carbon fiber and surpassing 83.2% for plastics, aluminum, and steel.

关键词： Ns-Transformer Machining energy consumption multi-sensor data fusion Prediction

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Intelligent fault diagnosis based on multi-sensor data fusion and multi-scale dual attention enhanced network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 025110-025110页

作者： Zhang, Weizhong Yan, Xiaoan Ye, Maoyou Hua, Xing Jiang, Dong Nanjing Forestry Univ Sch Mechatron Engn Nanjing 210037 Peoples R China

As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a MSDF strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a MSDAENet is constructed, incorporating both time-frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model's capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.

关键词： multi-sensor data fusion deep learning mechanical fault diagnosis

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multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

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ISA TRANSACTIONS 2021年 112卷 137-149页

作者： Mi, Xiangjun Lv, Tongxuan Tian, Ye Kan, Bingyi Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster-Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster's combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： multi-sensor data fusion Soft likelihood functions Ordered weighted averaging aggregation Dempster-Shafer theory Target recognition system

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multi-sensor data fusion reconstruction method for vibration dynamic responses of aerospace structures

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INSIGHT 2024年第4期66卷 205-214页

作者： Ye, Yumei Chen, Cheng Ma, Jinchao Yu, Zhangyong Wuxi Inst Technol Sch Mech Technol Wuxi 214121 Jiangsu Peoples R China

The dynamic responses of key locations are important inputs for the life and reliability assessment of spacecraft structures. Due to the limited sensing resources, most critical responses are difficult to measure directly. A structural dynamic response reconstruction method is necessary. The responses of target locations can be reconstructed based on the empirical mode decomposition (EMD) of measured signals and the modal superposition. However, the structural modal information contained in the measured signal of a single sensor is limited, affecting the reconstruction accuracy. In this paper, a response reconstruction method based on multi-sensor data fusion is proposed. It is applied to a main load-bearing structure of a spacecraft and its typical components to verify its strain response reconstruction effect under random vibration loads. The experimental results show that multi-sensor data fusion improves the strain reconstruction accuracy. The maximum reduction in reconstruction error is from 8.7% to 1.3%. The reconstruction accuracy is further improved with the increase in the number of sensors. The optimal weighted fusion strategy for this problem is the weights defined by the Euclidean distance (EUC) or the dynamic time warping distance (DTW). The fusion results show a better performance with the increase in the power of the defined distance. The proposed multi-sensor fusion method improves the reconstruction accuracy via supplementing structural information to each other and eliminating the instability of single measured signals. More accurate dynamic responses via reconstruction reduce the large input uncertainty in life prediction and lay the foundation for building structural digital twins and managing structural health more effectively.

关键词： response reconstruction multi-sensor data fusion complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) structural vibration

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multi-sensor data fusion by a hybrid methodology - A comparative study

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COMPUTERS IN INDUSTRY 2016年 75卷 27-34页

作者： Rawat, Sarvesh Rawat, Surabhi VIT Univ Sch Elect & Elect Engn SELECT Vellore 632014 Tamil Nadu India Lovely Fac Technol & Sci Sch Biotechnol & Biosci Phagwara Punjab India

multi-sensor data fusion is considered as an inherent problem in wireless sensor network applications. It is widely assumed as a sturdy non linear system in view of the complexities involved in its operation. An accurate and precise methodical solution is therefore a complicated task to accomplish. It is crucial for the sensory systems that they should not be influenced in terms of accuracy and precision by any means. To address these issues a hybrid model employing rough set (RS) with back-propagation neural network (BPNN) is used to ameliorate the data fusion capability of the system with an illustrative example. Experimental results have demonstrated an escalating improvement in the predictive accuracy of the hybrid model as compared to the traditional BPNN model. (C) 2015 Elsevier B.V. All rights reserved.

关键词： multi-sensor data fusion Hybrid architecture Neural network Diagnostic tool Rough-sets

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multi-sensor data fusion methods for indoor localization under collinear ambiguity

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PERVASIVE AND MOBILE COMPUTING 2016年 30卷 18-31页

作者： Kumar, Sudhir Hegde, Rajesh M. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer-Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Node localization multi-sensor data fusion Least square Singular value decomposition Collinearity

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multi-sensor data fusion framework for CNC machining monitoring

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2016年 66-67卷 505-520页

作者： Duro, Joao A. Padget, Julian A. Bowen, Chris R. Kim, H. Alicia Nassehi, Aydin Univ Bath Dept Comp Sci Claverton Down BA2 7AY Avon England Univ Bath Dept Mech Engn Claverton Down BA2 7AY Avon England

Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： CNC machining Acoustic emission Process monitoring multi-sensor data fusion

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multi-sensor data fusion and comparison of total column ozone

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2008年第15期29卷 4553-4573页

作者： Nirala, Mohan NASA Goddard Space Flight Ctr Goddard Earth Sci Data & Informat Serv Ctr RSIS Greenbelt MD 20771 USA

With many remote- sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution- as well as format, structure, and temporal and spatial coverage- make the data merging, or fusion, difficult. NASA Goddard Earth Sciences data and Information Services Center ( GES- DISC) has archived several data products for various sensors in different formats, structures, and multi- temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/ fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/ fusing several NASA Earth Observing Systems ( EOS) remote- sensing datasets available at GES- DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES- DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer ( MODIS) Terra and Aqua Level3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation ( OI), also was conducted and found to produce better results. Ozone Monitoring Instrument ( OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder ( AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high ( 0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/ A

关键词： atmosphere ozone data Merging sensor data fusion multi-sensor data fusion Information Services data products Earth sciences dataset Moderate Resolution Imaging Spectroradiometer

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multi-sensor data fusion for autonomous flight of unmanned aerial vehicles in complex flight environments

DRONE SYSTEMS AND APPLICATIONS

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DRONE SYSTEMS AND APPLICATIONS 2024年 12卷 1-12页

作者： Yue, Kun Tianjin Modern Vocat Technol Coll Sch Intelligent Engn Tianjin 300350 Peoples R China

The flight environment of unmanned aerial vehicles faces various challenges. To effectively navigate and perform tasks, they need to effectively integrate multiple sensors. This study applies the adaptive weighted average method, combined with data from global positioning system, inertial measurement unit, three-dimensional optical detection and ranging, and uses linear Kalman filtering to smooth the merged velocity data. High-order B-spline curves for route planning and applying flight constraint formulas to better adapt are used to the dynamics of unmanned aerial vehicles. The research results indicated that the improved adaptive weighting algorithm had high comprehensive performance for multi-sensor data fusion, with the highest accuracy, robustness, real-time performance, and consistency of 94.2%, 93.7%, 100%, and 95.6%, respectively. The flight path lengths planned by the A & lowast;algorithm and higher-order B-spline curve were 15.7 and 16.3 m, respectively, and the flight time was 8.2 and 7.1 s, respectively. The flight path planned by higher-order B-spline curve was further away from obstacles. The use of adaptive weighted fusion and linear Kalman filtering facilitates the fusion of multi-sensor data, and autonomous flight routes planned using high-order B-spline curves can also meet the needs of unmanned aerial vehicle flight in complex flight environments.

关键词： complex flight environment drones autonomous flight multi-sensor data fusion adaptive weighting algorithm

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