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
For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system ...For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.展开更多
taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control depende...taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.展开更多
The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature fie...The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.展开更多
A new method of multi sensor location data fusion is proposed.The method is based on group consensus approach, which constructs group utility function (or its density) based on uncertainty of each sensor, and the loc...A new method of multi sensor location data fusion is proposed.The method is based on group consensus approach, which constructs group utility function (or its density) based on uncertainty of each sensor, and the location estimation is obtained based on the group utility function (or its density). The simulation results show that the method is better than those of mean and median estimation, and outlier and sensor failure can not affect the location estimation.展开更多
This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy o...This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy of long-range airplane can be improved greatly if the extant sensors are rationally placed and multi-sensor data fusion technique is used in the case of展开更多
同时定位与建图(simultaneous localization and mapping,SLAM)是地下空间自主探测、自动巡检和应急救援的关键。然而,地下空间巷道狭长、地形复杂、光照不均等使得激光点云和视觉图像匹配极易发生退化,进而导致多源传感器数据融合SLAM...同时定位与建图(simultaneous localization and mapping,SLAM)是地下空间自主探测、自动巡检和应急救援的关键。然而,地下空间巷道狭长、地形复杂、光照不均等使得激光点云和视觉图像匹配极易发生退化,进而导致多源传感器数据融合SLAM精度不足,甚至失效。为此,本文提出一种增强稳健性的多源传感器数据动态加权融合SLAM方法。首先,在视觉图像预处理阶段,采用了一种基于色调、饱和度、亮度(hue,stauration,value,HSV)空间的图像增强技术,结合单参数同态滤波和对比度受限的自适应直方图均衡化(contrast limited adaptive histogram equalization,CLAHE)算法,有效提升了地下空间图像的亮度和对比度,从而增强了视觉里程计的稳健性。然后,通过马氏距离一致性检验方法对各传感器的数据质量进行评估,分析数据退化情况,并自适应地选择适合当前场景的传感器数据进行融合。最后,在综合考虑各传感器关键参数的基础上,构建了多源传感器因子图模型,并根据数据质量动态调整各传感器数据融合因子的权重,形成多源传感器数据权重动态组合模型。为验证本文方法的有效性,使用自主设计集成的移动机器人在地下走廊、开挖的地铁隧道和煤矿巷道等典型地下空间中分别进行了试验,并与多种主流SLAM方法进行定性、定量对比分析。结果表明:本文方法最大轨迹均方根误差(root mean square error,RMSE)仅为0.19 m,以高精度地面三维激光扫描获取的点云为参考,平均点云直接距离比较(cloud to cloud,C2C)小于0.13 m,所构建的点云地图具有较好的全局一致性和几何结构真实性,验证了本文方法在复杂地下空间具有更高的精度和稳健性。展开更多
面向有人车引导的无人多车编队场景,设计并实现无人车在编队行驶中的车辆识别与轨迹跟踪控制系统,提出了一种多传感器后融合动目标检测算法,使用激光雷达、相机和毫米波雷达3种传感器作为数据源,分别使用欧式聚类、深度学习和运动学推...面向有人车引导的无人多车编队场景,设计并实现无人车在编队行驶中的车辆识别与轨迹跟踪控制系统,提出了一种多传感器后融合动目标检测算法,使用激光雷达、相机和毫米波雷达3种传感器作为数据源,分别使用欧式聚类、深度学习和运动学推理的方法对潜在目标进行检测,进而提出后融合方法将多源检测结果融合以实现对前方车辆的准确检测。基于前车轨迹生成期望路径并设计卡尔曼滤波器对期望路径进行平滑和滤波。构建车辆动力学模型、车辆道路误差模型并设计鲁棒H∞控制器进行车辆轨迹跟踪控制仿真。仿真与实车验证结果表明:在测试路段对前方车辆的平均识别准确率大于95%;实时期望路径相对于真实轨迹的均方差和轨迹平均变化率在滤波前后分别降低17.3%和48.6%;侧向控制位置误差和航向角误差相较于PID(proportional integral derivative)控制分别降低了29%和41%;车辆编队以最高54 km/h的速度实现编队整体的稳定行驶。展开更多
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For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.
文摘taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.
基金supported by Focus of the Funding Item of Metrology of Military Industry in National Defense of China in "Tenth-five-year" Project (Grant No. 60104208)
文摘The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.
文摘A new method of multi sensor location data fusion is proposed.The method is based on group consensus approach, which constructs group utility function (or its density) based on uncertainty of each sensor, and the location estimation is obtained based on the group utility function (or its density). The simulation results show that the method is better than those of mean and median estimation, and outlier and sensor failure can not affect the location estimation.
文摘This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy of long-range airplane can be improved greatly if the extant sensors are rationally placed and multi-sensor data fusion technique is used in the case of
文摘同时定位与建图(simultaneous localization and mapping,SLAM)是地下空间自主探测、自动巡检和应急救援的关键。然而,地下空间巷道狭长、地形复杂、光照不均等使得激光点云和视觉图像匹配极易发生退化,进而导致多源传感器数据融合SLAM精度不足,甚至失效。为此,本文提出一种增强稳健性的多源传感器数据动态加权融合SLAM方法。首先,在视觉图像预处理阶段,采用了一种基于色调、饱和度、亮度(hue,stauration,value,HSV)空间的图像增强技术,结合单参数同态滤波和对比度受限的自适应直方图均衡化(contrast limited adaptive histogram equalization,CLAHE)算法,有效提升了地下空间图像的亮度和对比度,从而增强了视觉里程计的稳健性。然后,通过马氏距离一致性检验方法对各传感器的数据质量进行评估,分析数据退化情况,并自适应地选择适合当前场景的传感器数据进行融合。最后,在综合考虑各传感器关键参数的基础上,构建了多源传感器因子图模型,并根据数据质量动态调整各传感器数据融合因子的权重,形成多源传感器数据权重动态组合模型。为验证本文方法的有效性,使用自主设计集成的移动机器人在地下走廊、开挖的地铁隧道和煤矿巷道等典型地下空间中分别进行了试验,并与多种主流SLAM方法进行定性、定量对比分析。结果表明:本文方法最大轨迹均方根误差(root mean square error,RMSE)仅为0.19 m,以高精度地面三维激光扫描获取的点云为参考,平均点云直接距离比较(cloud to cloud,C2C)小于0.13 m,所构建的点云地图具有较好的全局一致性和几何结构真实性,验证了本文方法在复杂地下空间具有更高的精度和稳健性。
文摘面向有人车引导的无人多车编队场景,设计并实现无人车在编队行驶中的车辆识别与轨迹跟踪控制系统,提出了一种多传感器后融合动目标检测算法,使用激光雷达、相机和毫米波雷达3种传感器作为数据源,分别使用欧式聚类、深度学习和运动学推理的方法对潜在目标进行检测,进而提出后融合方法将多源检测结果融合以实现对前方车辆的准确检测。基于前车轨迹生成期望路径并设计卡尔曼滤波器对期望路径进行平滑和滤波。构建车辆动力学模型、车辆道路误差模型并设计鲁棒H∞控制器进行车辆轨迹跟踪控制仿真。仿真与实车验证结果表明:在测试路段对前方车辆的平均识别准确率大于95%;实时期望路径相对于真实轨迹的均方差和轨迹平均变化率在滤波前后分别降低17.3%和48.6%;侧向控制位置误差和航向角误差相较于PID(proportional integral derivative)控制分别降低了29%和41%;车辆编队以最高54 km/h的速度实现编队整体的稳定行驶。
