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.

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 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.

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.

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.

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 ）.

Location Data Fusion Based on Group Consensus

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.

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

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。

改进自适应加权的海面目标距离测量和跟踪

海上目标距离探测和跟踪是海洋安全和军事应用中的关键任务之一,针对复杂海面环境在海杂波等影响因素下目标距离测量精度低的问题,提出了一种改进的多传感器数据融合算法。该算法利用海面舰船雷达及陆地雷达联合探测结果,先对多个数据源数据进行坐标系转换,利用Robust Z-score方法进行纵向数据预处理剔除异常数据,再通过重新定义置信距离度量,将置信度较高的传感器结果代替被踢除数据后,对结果进行自适应加权融合。同时,为了进一步提高数据精度,引入了一种分段融合机制,将改进的传感器数据融合算法与阶梯式自适应加权融合算法进行级联,通过度量各分段融合结果的相似度,设定一个置信阈值,通过该置信阈值确定最终的融合结果。仿真实验结果证实了算法的有效性和准确性。

基于高斯滤波与均值聚类的异质多源传感器数据加权融合

异质多源传感器之间工作频率存在差异,导致数据之间的一致性较差,加权融合后的观测误差较大,因此提出基于高斯滤波与均值聚类的异质多源传感器数据加权融合方法。采用高斯滤波对异质多源传感器数据空间单元格进行划分,建立基于单元格的最佳连通域,保留传感器内部数据,完成传感器数据的高斯滤波平滑处理。引入均值聚类对异质多源传感器数据进行一致性处理。通过免疫粒子群搜索最优权重和参数,利用最优权重和参数完成异质多源传感器数据加权融合。仿真结果表明,所提方法能够降低融合后传感器数据的观测误差与均方误差,观测误差与均方误差最小值均为0.002。因此,说明所提方法提高了融合后异质多源传感器数据的可利用性。

多传感器融合的火灾监测机器人设计

针对目前火灾报警系统不能应用在城市中非密闭空间的问题,提出一种适用于非密闭空间的多传感器融合的火灾监测机器人。该机器人采用履带式结构适应多地形移动,根据城市内非密闭下空间火势初期主要特征参数确定采集模块的搭建;利用D-S证据理论对多传感器火灾数据进行融合检测,以降低单个传感器的误报率,来提高对非密闭空间火灾事故的精确判定,并对火灾进行现场警报与远程回传。实验表明,与单一传感器判断相比,引入D-S证据理论的火灾监测机器人的火灾检测不确定性下降,检测精度得到了提高。

基于多传感器信息融合的机床测量数据自动补偿系统

机床测量数据自动补偿能够减少加工误差,为提高机床加工精度,设计基于多传感器信息融合的机床测量数据自动补偿系统。首先设计温度传感器、单片机主控与补偿脉冲发生装置,获取机床运动数据,然后采用多传感器融合算法融合采集信息,对信息中误差处理,采用粗糙集理论寻找最佳补偿值,实现机床测量数据自动补偿。实验结果表明,该系统能够有效减少加工误差,可以满足实际应用要求。

基于改进PSO优化的RBF火灾预测系统

针对系统预测火灾状态不准确,导致火情变大造成人民群众生命和财产损失的问题,本文提出了一种基于改进粒子群优化的径向基神经网络多传感器数据融合算法的火灾状态预测系统。以温度、烟雾浓度、一氧化碳浓度为输入,以无火、阴燃火、明火的概率为输出,为了避免输出产生偏差,模糊推理系统对神经网络系统的输出做补偿。由于粒子群算法存在容易陷入局部最优的缺陷,采用一种非线性动态自适应惯性权重的改进粒子群优化算法(IPSO)。仿真实验表明,改进后的系统,以明火为例的平均绝对百分比误差达到0.169、均方根误差达到0.0021、平均绝对误差达到0.031。

基于多传感器数据自动融合的配电房能耗远程监控系统

为了全面、准确地监测配电房能耗,设计了基于多传感器数据自动融合的配电房能耗远程监控系统。硬件上,采用A1101R04C霍尔传感器、DC 0~20 V电压传感器、SINAEX P530功率传感器及DHT11温湿度传感器设计传感器模块,并选择STM32F407ZGT芯片作为微处理器。软件上,采集配电房传感器监测数据,基于多传感器数据自动融合算法进行不同传感器监测数据的融合,获取配电房能耗监控结果。结果表明,配电房能耗远程监控数据的误差在±1%范围内,具有较高的监测精度和稳定性。

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。

基于多源数据融合与卷积长短期记忆神经网络的聚合物挤出过程熔体密度监测方法

聚合物挤出过程中熔体密度是影响产品质量的关键因素。由于挤出加工过程的高温、高压复杂工况,寻求能准确、在线监测聚合物挤出过程中熔体密度的方法是一个具有挑战性的问题。尽管基于机器学习的质量监测方法提供了一种解决方案,但在聚合物挤出加工过程中,由于数据类型、工艺参数、操作环境等多变性因素的影响,传统的机器学习方法可能难以捕捉聚合物加工中不同输入参数和输出质量参数之间的复杂关系,使得监测任务难以获得理想的准确性。本文提出了一种基于多源数据融合与卷积长短期记忆神经网络(CNN–LSTM)的熔体密度监测方法,用于在线监测聚碳酸酯–丙烯腈–丁二烯–苯乙烯共聚物(PC/ABS)共混体系的熔体密度。首先,通过实时采集安装在挤出机模头处的近红外、拉曼及超声3种传感器数据,对3种传感数据进行预处理并融合后作为输入;然后,通过合理设计的网络结构,构建CNN–LSTM监测模型,利用CNN的特征提取能力与LSTM的预测能力,最终实现对聚合物共混过程中的熔体密度的实时监测。基于独立开发的多源传感数据实时采集装置获取的数据,利用所提方法对PC/ABS共混挤出过程的熔体密度进行实时监测,结果表明:本文方法能够准确监测聚合物熔体密度,其在测试集上的均方根误差和决定系数分别为0.975 5、0.006 3 g/cm3,比传统的卷积神经网络方法、长短期记忆网络方法、岭回归方法、偏最小二乘回归方法、多层感知机方法和支持向量机回归方法具有更高的预测精度;本文方法的10次输入平均预测时间为1.523 5 s,能够满足实际生产过程的实时监测。综上所述,所提出的基于多源数据融合与CNN–LSTM的熔体密度监测方法显著提高了聚合物挤出过程中熔体密度的实时监测精度,为挤出过程中聚合物的质量提供了可靠的技术支持。

移动机器人多传感器数据融合方法研究

多传感器数据融合技术能够更好地解决智能设备产生的数据不兼容问题,提高设备运行效率和生产力。现有方法缺乏高性能表现以及未考虑数据隐私保护问题,将联邦学习引入多传感器数据融合中,联邦学习局部模型采用门控循环单元(Gated Recurrent Unit,GRU)算法解决多传感器数据拟合问题,首次设计了一种在时间域和空间域上并行的立体式多传感器数据融合方法,该方法既具备优异的融合性能,又保证了各客户端数据的隐私性。实验结果表明了该方法的正确性与合理性,并在鲁棒性方面呈现出优势。

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。

两级融合的多传感器数据融合算法研究

针对智慧工厂监测环境中多源数据融合精度问题,提出了一种两级融合的多传感器数据融合方法,旨在提高多源数据融合的准确性和可靠性。该方法分为一级数据融合和二级决策融合,首先采用卡尔曼滤波结合自适应加权平均对同类型传感器进行数据降噪融合处理,其次利用人工兔优化算法(ARO)优化ELM神经网络进行决策融合。实验结果表明,基于ARO优化ELM神经网络的多传感器数据融合算法在融合精度方面优于其他先进算法。经验证,所提出的两级融合多传感器数据融合方法具有更好的融合性能,有效提升感知系统的可靠性和鲁棒性,实现更加准确和可靠的监测和预测。