Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

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.

Data Fusion in Distributed Multi-sensor System

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.

STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusion technique is put forward because of the localization of single type sensor recognition method. The measuring theory based on multi-sensor data fusion technique is analyzed, and hereby the test platform of recognition system is manufactured. The advantage of data fusion with the fuzzy neural network (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carried out. The experiments show that in various conditions the method can always acquire a much higher recognition rate than normal ones.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

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.

Data acquisition,analysis and applications of multi-sensor integration

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-borne 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 research.

Application of data fusion on multi-function earth drill

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.

Integrated Navigation Based on Robust Estimation Outputs of Multi-sensor Measurements and Adaptive Weights of Dynamic Model Information

An integrated navigation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in calculation. In order to control the influences of measurements outliers and the kinematic model errors on the integrated navigation results, a robust estimation method and an adaptive data fusion method are applied. An integrated navigation example using simulated data is performed and analyzed.

RESEARCH ON THE ACCURACY OF TRACKING LONG RANGE AIRPLANE BY MULTI-SENSOR

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

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

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 ).

基于Kalman-LSTM模型的悬浮质含沙量测量

针对电容法测量河流含沙量过程中易受环境因素影响而导致测量结果不准确的问题,提出基于卡尔曼滤波和长短期记忆神经网络(Kalman-LSTM)的融合模型。先采用卡尔曼滤波器进行滤波处理,减小传感器测量的随机误差;再通过LSTM神经网络模型对含沙量信息和环境量信息进行多传感器数据融合,减小环境因素对电容法测量含沙量的影响;最后建立了电容法测量含沙量的Kalman-LSTM融合模型。为了验证Kalman-LSTM融合模型的融合效果,与BP模型、RBF模型和LSTM模型对比,比较各模型的均方根误差、最大绝对误差、平均绝对误差和平均相对误差。实验结果表明,Kalman-LSTM融合模型的平均相对误差为2.54%,均方根误差为2.47 kg/m^(3),该融合模型能有效降低环境因素对含沙量测量的影响,提高电容法测量含沙量的准确性。

Location Data Fusion Based on Group Consensus

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Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and sig-nificant topic in image processing.Due to advancements in image processing and the availability of sophisticated software,picture fabrication may hide evidence and hinder the detection of such criminal cases.The practice of modifying origi-nal photographic images to generate a forged image is known as digital image forging.A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery.In order to make the forgeries real and inconspicuous,geometric or post-processing techniques are frequently performed on tampered regions during the tampering process.In Copy-Move forgery detection,the high similarity between the tampered regions and the source regions has become crucial evidence.The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform(DCT)com-ponents as block representations.Due to the high dimensionality of the feature space,Gaussian Radial basis function(RBF)kernel based Principal component analysis(PCA)is used to minimize the dimensionality of the feature vector repre-sentation,which improves feature matching efficiency.In this paper,we propose to use a novel enhanced Scale-invariant feature transform(SIFT)detector method called as RootSIFT,combined with the similarity measures to mark the tampered areas in the image.The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity,detection reliability,and forgery location accuracy,according to the experimental results.The F1 score of the proposed method is 92.3%while the literature methods are around 90%on an average.

基于多传感数据融合的变速运行齿轮异常振动故障诊断

变速运行齿轮异常振动故障诊断性能过差会增加汽车维护成本,缩短齿轮使用寿命。为了及时识别齿轮故障,保证汽车变速器总成具有良好的振动特性,提出基于多传感数据融合的变速运行齿轮异常振动故障诊断方法。通过分析多传感器数据融合技术,掌握变速运行齿轮异常振动故障诊断的理论框架,并以此为基础,参考传感器融合模块、特征级并行多神经网络局部诊断模块和终端分类模块,结合变分模态分解、多通道加权融合和单隐层前馈神经网络训练算法,从信号采集、信号特征提取和信号特征分类3个步骤实现变速运行齿轮异常振动故障诊断。实验结果表明:在齿轮发生轻度磨损时,磨损振动信号的幅值在20~40 mV之间,磨损振动信号的频率在0~4000 Hz区间;中度磨损时,信号的幅值在30~55 mV之间,信号频率在3000~7000 Hz区间;重度磨损时,信号幅值在50~70 mV之间,信号频率在6000~12000 Hz区间,且各阶段诊断结果均与故障程度的实际转折点吻合。由此可知在各样本数量均相同的情况下,提出的故障诊断方法预测值与真实值均相同,故障程度和故障类型的诊断性能均较好。

双足人形机器人定位及运动规划系统的设计与实现

基于ZigBee技术和多传感器数据融合设计并实现了双足人形机器人的室内定位与运动规划控制系统,并分别对机器人运动过程建立空间模型和姿态模型,以获得机器人实时位置及姿态信息,实现自主运动规划。系统将树莓派4B和32路串口总线舵机控制板结合起来作为核心控制平台,更好地实现了对机器人的方向控制、目标导航等功能。

D-S证据理论中冲突证据的合成方法

针对高度冲突证据的合成问题,分析了D-S合成方法及其改进方法的不足,提出了一种新的改进方法。新方法仍利用D-S合成公式,但针对该公式要求参与合成的各证据需具有相同重要程度的要求,考虑到融合系统中各证据的重要程度可能不同,直接利用公式合成可能得到与实际相悖的结论,因而引入证据权的概念,给出了证据权的具体算法,通过证据权对已有的基本可信度分配函数进行修正得到了重要性相同的新的证据的基本可信度分配模型。最后通过算例比较表明新方法的合成结果更为理想。

基于Hall和GMR的多传感器融合方法及实现

目前霍尔传感器(Hall)和巨磁阻(GMR)传感器均广泛地应用于电力系统电流测量。为同时发挥二者的优势、降低各自的局限性,在分析Hall和GMR的温度特性、噪声特性和被测电流范围的基础上,提出了一种基于Hall和GMR的多传感器融合方案。在定义GMR和Hall的灵敏度差值ΔS基础上,将被测电流i和灵敏度差值ΔS构成的融合域划分为四个域,在域Ⅱ采用多传感加权观测融合Kalman滤波算法,将Hall和GMR的观测量和观测噪声融合后与状态方程联立进行Kalman滤波;在域Ⅰ采用数据加权融合最优权值分配的方法,给Hall的测量数据赋予较大权值,GMR的测量数据赋予较小的权值;在域Ⅲ,权值分配情况相反,各域之间可实现数据融合的平滑过渡。基于多传感器融合方法,设计了一种组合式闭环电流传感器,包括磁芯、电路部分设计及仿真。仿真和样机实验结果表明,在域Ⅱ时多传感器融合值与真实值的均方根误差低至0.004;在域Ⅰ、Ⅲ时电流测量的相对误差E_(i)均在0.255%以下。与单一传感器相比,多传感器融合的方法使组合式传感器测量电流范围增大,适用于温度变化范围较大的场景,电流测量精度及可信度更高。

TM-SAR数据融合在黄河口沙咀动态监测中的应用研究

黄河口沙咀位于 1976年之前黄河入海故道与现黄河入海口之间 ,是目前世界上陆地面积生长最快的地区。该地区于 1992年经国务院批准为国家级自然保护区 ,是以保护新生湿地生态系统、珍稀频危鸟类为主体的国家级自然资源保护区。由于受河流泥沙淤积作用、海水侵蚀作用、石油开发等自然及人为因素综合作用的影响 ,该区海岸线蚀进及地表覆盖状况变化较快。利用获取的 1995 ,1997,2 0 0 0年陆地卫星TM数据 ,对地观测卫星ERS - 1和RADARSAT数据 ,通过数据融合处理及FL神经网络分类方法 ,可以清楚的对 1995～ 2 0 0 0年间黄河口沙咀海岸线蚀进及地表覆盖状况进行动态分析。研究结果表明 ,TM和SAR的融合数据对该地区动态监测有一定的优势。

基于D-S证据理论的数据融合井下监测方法分析

针对矿井单传感器监测系统监测数据单一、不确定性较高的缺陷,利用基于D-S证据理论的多传感器信息融合技术,在对原始数据分析处理的基础上,把多种参数通过数据融合模块实现对井下工作环境安全性的评估、决策。同时,可根据各参数间的相关性获得更多单传感器监测系统所无法获得的井下环境信息,极大地提高了数据采集的可靠性、全面性和矿井监测系统的有效性。

面向畜禽舍温度监测数据融合算法改进研究

在现代化大型智能畜禽养殖中,由于畜禽舍内温度分布不均匀、传感器采集数据效率低下等原因,无法全面、准确及时反映畜禽舍内温度变化情况。为提高畜禽养殖温度监测系统的性能,提出一种分层无线传感器网络(WSN)实时融合策略。该策略设计的无线传感器网络分为两层,首先将底层传感器采集的温度数据利用改进的无迹卡尔曼滤波器(IUKF)进行预处理,然后融合中心利用改进蜣螂算法优化核极限学习机(IDBO-KELM)对预处理后的数据进行实时融合。试验结果表明,在数据预处理方面改进的无迹卡尔曼滤波器能够有效抑制畜禽舍内噪声干扰,克服采集数据出现异常和发散现象;在多传感器数据融合方面本文建立的IDBO-KELM算法其训练集与测试集准确率分别是99.15%和98.12%,相较于原始算法准确率提升6.98%,数据融合用时3.36 s,保证禽畜舍内温度监测的效率和准确性,同时减少运算时间。