This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a ...This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.展开更多
With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagno...With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.展开更多
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan...Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.展开更多
Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks with...Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.展开更多
Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joint...Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.展开更多
At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-se...At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.展开更多
In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel...In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.展开更多
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnos...Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.展开更多
In this study, the dynamic characteristic of a multi-sensor system made up of such sensors as are sensitive to several parameters is discussed, and the effect of cross-sensitivity on the precision of a measurement sys...In this study, the dynamic characteristic of a multi-sensor system made up of such sensors as are sensitive to several parameters is discussed, and the effect of cross-sensitivity on the precision of a measurement system is also discussed. A multi-sensor system is looked as a serial of a linear filter and a memoryless nonlinear system, i.e. Wiener system, and the subsequent information fusion system is regarded as a Hammerstein system, i.e. a serial of a memoryless nonlinear system and a linear filter. On the basis of static calibration, it is presented to determine the inverse filter in a Hammerstein system using blind deconvolution. In order to control the uncertainty of amplitude of signals recovered by blind deconvolution well, a regulation approach to regulating the inverse linear filter coefficient matrixes is presented according to the identity between inverse filter coefficient matrixes and static calibrating matrix. So the approximate inverse dynamic model of multi-sensor system is obtained, the degree of distortion of dynamic measurement result is reduced, the measurement precision is improved, and the need of practice can be reached. Simulation example and simulation result show that the recovered error of the inputs of sensor system, the frequency of which is 1/10 of sampling frequency, is 1/20 of the measurement results without dynamic compensation, and is one half of the measurement results with sole dynamic compensation, and the rapidity is improved 2 times. The dynamic compensation results of a metal oxide semiconductor methane sensor show that the dynamic measurement error is less than one half of that without dynamic compensation. So this method expands the bandwidth of multi-sensor system.展开更多
As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery...As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.展开更多
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data...The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.展开更多
To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy simila...To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.展开更多
This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorre...This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.展开更多
Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and ot...Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.展开更多
Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities i...Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.展开更多
This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection...This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-bone intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co. , Ltd. and has been successfully put into use. The latter is an ongoing resealch.展开更多
Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.C...Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
文摘This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.
基金supported in part by the National Key R&D Program of China Grant 2022YFB4602200.
文摘With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.
文摘Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving.
基金supported by the National Natural Science Foundation of China(Grant no.52075488)the Natural Science Foundation of Zhejiang Province(LY20E050023).
文摘Quadruped robots with body joints exhibit enhanced mobility,however,in outdoor environments,the energy that the robot can carry is limited,necessitating optimization of energy consumption to accomplish more tasks within these constraints.Inspired by quadruped animals,this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator(CPG)with multi-sensor fusion bio-reflexes.First,an energy consumption model for the robot is established,and energy characteristic tests are conducted under different gait parameters.Based on these energy characteristics,optimal energy-efficient gait parameters are determined for various environmental conditions.Second,biological reflex mechanisms are studied,and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation.By integrating the reflex model and gait parameters,real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains,reducing energy loss caused by terrain disturbances.Finally,a prototype of the body joint quadruped robot is built for experimental verification.Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport(COT)and significantly improves energy efficiency.The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.
基金Project(SICGM2023301) supported by the State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,ChinaProject(SMDPC202202) supported by the Key Laboratory of Mining Disaster Prevention and Control,ChinaProject(U21A2030) supported by the National Natural Science Foundation of China。
文摘Microseismic (MS) source location plays an important role in MS monitoring. This paper proposes a MS source location method based on particle swarm optimization (PSO) and multi-sensor arrays, where a free weight joints the P-wave first arrival data. This method adaptively adjusts the preference for “superior” arrays and leverages “inferior” arrays to escape local optima, thereby improving the location accuracy. The effectiveness and stability of this method were validated through synthetic tests, pencil-lead break (PLB) experiments, and mining engineering applications. Specifically, for synthetic tests with 1 μs Gaussian noise and 100 μs large noise in rock samples, the location error of the multi-sensor arrays jointed location method is only 0.30 cm, which improves location accuracy by 97.51% compared to that using a single sensor array. The average location error of PLB events on three surfaces of a rock sample is reduced by 48.95%, 26.40%, and 55.84%, respectively. For mine blast event tests, the average location error of the dual sensor arrays jointed method is 62.74 m, 54.32% and 14.29% lower than that using only sensor arrays 1 and 2, respectively. In summary, the proposed multi-sensor arrays jointed location method demonstrates good noise resistance, stability, and accuracy, providing a compelling new solution for MS location in relevant mining scenarios.
文摘At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.
基金supported by the National Natural Science Foundation of China(61473306).
文摘In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.
基金supported by the National Natural Science Foundation of China(Nos.51805376 and U1709208)the Zhejiang Provincial Natural Science Foundation of China(Nos.LY20E050028 and LD21E050001)。
文摘Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.
基金supported by the National Natural Science Foundation of China(Grant No.60276037).
文摘In this study, the dynamic characteristic of a multi-sensor system made up of such sensors as are sensitive to several parameters is discussed, and the effect of cross-sensitivity on the precision of a measurement system is also discussed. A multi-sensor system is looked as a serial of a linear filter and a memoryless nonlinear system, i.e. Wiener system, and the subsequent information fusion system is regarded as a Hammerstein system, i.e. a serial of a memoryless nonlinear system and a linear filter. On the basis of static calibration, it is presented to determine the inverse filter in a Hammerstein system using blind deconvolution. In order to control the uncertainty of amplitude of signals recovered by blind deconvolution well, a regulation approach to regulating the inverse linear filter coefficient matrixes is presented according to the identity between inverse filter coefficient matrixes and static calibrating matrix. So the approximate inverse dynamic model of multi-sensor system is obtained, the degree of distortion of dynamic measurement result is reduced, the measurement precision is improved, and the need of practice can be reached. Simulation example and simulation result show that the recovered error of the inputs of sensor system, the frequency of which is 1/10 of sampling frequency, is 1/20 of the measurement results without dynamic compensation, and is one half of the measurement results with sole dynamic compensation, and the rapidity is improved 2 times. The dynamic compensation results of a metal oxide semiconductor methane sensor show that the dynamic measurement error is less than one half of that without dynamic compensation. So this method expands the bandwidth of multi-sensor system.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z433)Hunan Provincial Natural Science Foundation of China (Grant No. 09JJ8005)Scientific Research Foundation of Graduate School of Beijing University of Chemical and Technology,China (Grant No. 10Me002)
文摘As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.
基金This project is supported by Provincial Youth Science Foundation of Shanxi China (No.20011020)National Natural Science Foundation of China (No.59975064).
文摘The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.
文摘To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.
基金the National Natural Science Foundation of China(No.61374160)the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST201237)
文摘This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.
基金National Key R&D Program of China(No.2021YFB2501102)。
文摘Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.
基金the financial support from Shanghai Science and Technology Committee Innovation Grand(Grant Nos.19ZR1404600,17JC1400601)National Key R&D Program of China(Project Nos.2017YFA0701200,2016YFF0102003)Science Challenging Program of CAEP(Grant No.JCKY2016212 A506-0106).
文摘Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.
基金The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 40721001)The Ph.D. Programs Foundation of Ministry of Education of China (No. 20070486001)+1 种基金The State Key Program of National Natural Science of China (No. 40830530)The National Natural Science Foundation of China (No. 60872132)
文摘This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-bone intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co. , Ltd. and has been successfully put into use. The latter is an ongoing resealch.
基金Supported by the National Natural Science Foundation of China(No.61300214)the National Natural Science Foundation of Henan Province(No.132300410148)+1 种基金the Post-doctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher ofHenan Province Universities(No.2013GGJS-026)
文摘Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
