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

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 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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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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A belief interval euclidean distance entropy of the mass function and its application in multi-sensor data fusion

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APPLIED INTELLIGENCE 2024年第17-18期54卷 7545-7569页

作者： Zhang, Fuxiao Chen, Zichong Cai, Rui Southwest Univ Business Coll Chongqing 402460 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Dempster-Shafer (D-S) evidence theory has extensive applications in the field of data fusion. It uses the mass function to replace the probability distribution in Bayesian Probability theory, which has the advantages of weak constraints and representing uncertainty. However, many existing uncertainty measures can not be well applied to the mass function, leading to many defects in some scenarios. For example, many methods can not fully reflect the relationship between focal elements. Therefore, how to improve the uncertainty measurement, and make full use of the advantages of the mass function, so as to further accurately express the system state, is still a hot topic worth studying. To solve this problem, we propose a Belief Interval Euclidean Distance (BIED) entropy of the mass function. This method combines nonspecificity and discord measurement of the mass function and is suitable for various situations. In this paper, we introduce a large number of numerical examples to demonstrate the practicality of the BIED entropy. Compared with existing advanced methods, the BIED entropy can better identify the quantity relationship and similarity relationship between focal elements, and exhibit a linear trend with the changing cardinality of focal elements. Finally, we implement a BIED entropy-based multi-sensor data fusion on various kinds of datasets. The experimental results indicate that BIED entropy has relatively high robustness, with the target recognition accuracy close to 79.33% and the F1-Score close to 77.29%.

关键词： Uncertainty measure D-S evidence theory Entropy Belief interval multi-sensor data fusion

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Improved Q-Learning-Based Motion Control for Basketball Intelligent Robots Under multi-sensor data fusion

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IEEE ACCESS 2024年 12卷 57059-57070页

作者： Lan, Jianping Dong, Xiujuan Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hubei Univ Automot Technol Inst Automot Engineers Shiyan 442002 Peoples R China

With the continuous development of science and technology, the application of artificial intelligence in robot technology, especially in the progress of sports robot technology, has received great attention. This development provides new opportunities for the development of intelligent basketball robots. However, achieving high accuracy and stability in motion control remains a challenge in the research field. The motion control technology of basketball intelligent robots is crucial. The research proposes a study on enhancing Q-learning-based action control technology for basketball robots with multi-sensor data fusion to address the problem of inaccuracy and instability. The first step involves examining how multi-sensor data fusion technology can offer more accurate and comprehensive environmental information that can aid the robot in making more precise action decisions. The second area of study involves the development of enhanced algorithms for controlling basketball robots through improved Q-learning techniques. The goal is to increase the accuracy and stability of the robots' actions. The final focus is on creating a model for controlling the basketball robots' actions, which is based on the improved Q-learning algorithm. This model effectively increases the accuracy and stability of the robots' actions. The experiment was conducted on a computer equipped with Intel Core i9-11900K processor and NVIDIA GeForce RTX 3080 graphics card. MATLAB R2023a was used for system parameterization and simulation, while Webots R2023a software was used to simulate robot actions. The sensor adopted Hokuyo URG-04LX-UG01 laser rangefinder, and the robot model was KUKA youBot. At the same time, in order to improve the control performance of basketball intelligent robots, comparisons were made with existing methods such as PID control, trajectory planning, traditional control methods, multi-sensor fusion technology, and visual perception basketball shooting correction technology.

关键词： multi-sensor data fusion Q-learning algorithm motion control basketball robot DQN algorithm

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multi-sensor data fusion-based Parallel Manipulator with IoT Monitoring Employing Machine Learning

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SN Computer Science 2023年第2期4卷 165页

作者： Shreyanth, S. Department of Electronics and Communication Engineering Anna University Tamil Nadu Chennai 600025 India Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani 333031 India

A robotic parallel manipulator is implemented by employing embedded systems integrated with a set of sensors. More than one type of sensor are implemented together with the control input data from a human limb. Initially, a data set is collected on the map to certain equivalent actuations at the manipulator, and then using an appropriate machine learning algorithm, the control data value for the continuous position of the actuator is generated. A substantial amount of work is done on mapping the position of the limb to the actuator position by creating a three-dimensional model conventional 3D conversion, which is used on the boundary values of the input and output matched with a certain level of intermediate values, a proper training dataset for a machine learning algorithm can be created. The position of the manipulator is monitored by an IoT system and a set of sensors installed at the end. In addition, applied sciences transmits the possession date of the equator, and this information can be viewed remotely from any device connected to the internet. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Convolution neural networks Image processing Intelligent systems Internet of things Machine learning multi-sensor data fusion Robotics sensor fusion Signal processing

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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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Design of a Novel Predictive Technique to Estimate Liquid Level and Concentration Using multi-sensor data fusion

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IEEE ACCESS 2024年 12卷 98787-98802页

作者： Hanni, Jayalaxmi Rajesh Santhosh, K. V. Srinidhi, N. N. Manipal Acad Higher Educ MAHE Manipal Inst Technol Bengaluru Dept Elect & Elect Engn Manipal 576104 India Manipal Acad Higher Educ MAHE Manipal Inst Technol Ctr Cyber Phys Syst Dept Instrumentat & Control Engn Manipal 576104 India Manipal Acad Higher Educ MAHE Manipal Inst Technol Bengaluru Dept Comp Sci & Engn Manipal 576104 India

Level is one of the important parameters to be measured in many of the industrial applications, in which Capacitance Level sensors (CLS) play an important role in measuring liquid level because of its ruggedness. But CLS is inept at measuring levels irrespective of liquid types hence this work proposes a novel technique to overcome the demerits of conventional measurement of CLS. This paper aims to develop a novel predictive technique to measure the liquid level along with its concentration. The predictive technique comprises a Level Predictive Model (LPM) and a Concentration Predictive Model (CPM). LPM predicts an accurate level for the change in liquid type and CPM predicts the change in sugar concentration of a liquid. An experimental setup was established for the model development. LPM was developed by infusing the data fusion technique from the data obtained by three different sensors CLS, Pressure Level sensor (PLS) and Ultrasonic Level sensor (ULS) for adaptive level measurement. CPM was developed using a Support Vector Machine (SVM) model for predicting the changes in sugar concentration in a liquid. The work was validated by implementing the developed LPM and CPM on a real-time system. System validation results showed that LPM detected the level with an error of -6 to 0.0001 cm, and CPM gave an accurate result for predicting the sugar concentration in liquid with an error between +/-0.8%. The obtained results of models catered to the objectives of the work with enhanced accuracy and avoided the re-calibration of CLS-based Liquid Level Measurement Systems (LLMS).

关键词： Liquids sensors Capacitance Permittivity sensor phenomena and characterization Calibration data analysis multisensor systems Measurement Calibration and fitting methods concentration measurement data analysis liquid level measurement multi-sensor data fusion

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Research on fire detection method of complex space based on multi-sensor data fusion

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第8期35卷 085107-085107页

作者： Su, Qian Hu, Guangzhou Liu, Zhenxing Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Sch Artificial Intelligence Wuhan 430081 Peoples R China

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

关键词： fire detection multi-sensor data fusion adaptive filtering fuzzy logic inference

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